BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging(MRI)for different tasks related to the management of patients with hepatocellular carcinoma(HCC).However,its implement...BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging(MRI)for different tasks related to the management of patients with hepatocellular carcinoma(HCC).However,its implementation in clinical practice is still far,with many issues related to the methodological quality of radiomic studies.AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score(RQS).METHODS A systematic literature search of PubMed,Google Scholar,and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023.The methodological quality of radiomic studies was assessed using the RQS tool.Spearman’s correlation(ρ)analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies.The level of statistical significance was set at P<0.05.RESULTS One hundred and twenty-seven articles were included,of which 43 focused on HCC prognosis,39 on prediction of pathological findings,16 on prediction of the expression of molecular markers outcomes,18 had a diagnostic purpose,and 11 had multiple purposes.The mean RQS was 8±6.22,and the corresponding percentage was 24.15%±15.25%(ranging from 0.0% to 58.33%).RQS was positively correlated with journal impact factor(IF;ρ=0.36,P=2.98×10^(-5)),5-years IF(ρ=0.33,P=1.56×10^(-4)),number of patients included in the study(ρ=0.51,P<9.37×10^(-10))and number of radiomics features extracted in the study(ρ=0.59,P<4.59×10^(-13)),and time of publication(ρ=-0.23,P<0.0072).CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients,our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.展开更多
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of...Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.展开更多
BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma(PDAC)is that most patients are usually diagnosed at late stages.There is an urgent unmet clinical need to identif...BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma(PDAC)is that most patients are usually diagnosed at late stages.There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages.METHODS A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography(CT)within 30 d prior to surgery were included in the study.Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system.Radiomics features were extracted from the region of interest(ROI)for each patient using Analysis Kit software.The most important and predictive radiomics features were selected using Mann-Whitney U test,univar-iate logistic regression analysis,and minimum redundancy maximum relevance(MRMR)method.Random forest(RF)method was used to construct the radiomics model,and 10-times leave group out cross-validation(LGOCV)method was used to validate the robustness and reproducibility of the model.RESULTS A total of 792 radiomics features(396 from late arterial phase and 396 from portal venous phase)were extracted from the ROI for each patient using Analysis Kit software.Nine most important and predictive features were selected using Mann-Whitney U test,univariate logistic regression analysis,and MRMR method.RF method was used to construct the radiomics model with the nine most predictive radiomics features,which showed a high discriminative ability with 97.7%accuracy,97.6%sensitivity,97.8%specificity,98.4%positive predictive value,and 96.8%negative predictive value.The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models.CONCLUSION The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.展开更多
BACKGROUND Although accurately evaluating the overall survival(OS)of gastric cancer patients remains difficult,radiomics is considered an important option for studying pro-gnosis.AIM To develop a robust and unbiased b...BACKGROUND Although accurately evaluating the overall survival(OS)of gastric cancer patients remains difficult,radiomics is considered an important option for studying pro-gnosis.AIM To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography(CT)image radiomics.METHODS This study included 181 stage II/III gastric cancer patients,141 from Lichuan People's Hospital,and 40 from the Cancer Imaging Archive(TCIA).Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest(ROI),and approximately 1700 radiomics features were extracted from each ROI.The skeletal muscle index(SMI)and skeletal muscle density(SMD)were measured using CT images from the lower margin of the third lumbar vertebra.Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation,36 radiomics features were identified as important predictors,and the OS-associated CT image radiomics score(OACRS)was cal-culated for each patient using these important predictors.RESULTS Patients with a high OACRS had a poorer prognosis than those with a low OACRS score(P<0.05)and those in the TCIA cohort.Univariate and multivariate analyses revealed that OACRS was a risk factor[RR=3.023(1.896-4.365),P<0.001]independent of SMI,SMD,and pathological features.Moreover,OACRS outperformed SMI and SMD and could improve OS prediction(P<0.05).CONCLUSION A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.展开更多
Background:Traditional Chinese medicine(TCM)is commonly used for the diagnosis and treatment of insomnia,with tongue diagnosis being particularly important.The aim of our study was to develop and validate a novel tong...Background:Traditional Chinese medicine(TCM)is commonly used for the diagnosis and treatment of insomnia,with tongue diagnosis being particularly important.The aim of our study was to develop and validate a novel tongue imaging-based radiomics(TIR)method for accurately diagnosing insomnia severity.Methods:This two-center analysis prospectively enrolled 399 patients who underwent tongue imaging between July and October 2021 and divided them into primary and validation cohorts by study center.Here,we referred to the Insomnia Severity Index(ISI)standard and the degree of insomnia was evaluated as absent,subthreshold,moderate,or severe.For developed the TIR diagnostic tool,a U-Net algorithm was used to segment tongue images.Subsequently,seven imaging features were selected from the extracted high-throughout radiomics features using the least absolute shrinkage and selection operator algorithm.Then,the final radiomics model was developed in the primary cohort and tested in the independent validation cohort.Finally,we assessed and compared the diagnostic performance differences between TCM tongue diagnosis and our TIR diagnostic tool with the ISI gold standard.The confusion matrix was calculated to evaluate the diagnostic performance.Results:Seven tongue imaging features were selected to build the TIR tool,with showing good correlations with the insomnia degree.The TIR method had an accuracy of 0.798,a macro-average sensitivity of 0.78,a macro-average specificity of 0.906,a weighted-average sensitivity of 0.798,and a weighted specificity of 0.916,showing a significantly better performance compared to the average performance of three experienced TCM physicians(mean accuracy of 0.458,P<0.01).Conclusions:The preliminary study demonstrates the potential application of TIR in the diagnosis of insomnia degree and measurement of sleep health.The integration of quantitative imaging analysis and machine learning algorithms holds promise for advancing both of TCM and precision sleep medicine.展开更多
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease.However,the evaluation of the overall adjuvant chemotherapy benefit in patients with a high...The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease.However,the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging.Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques,working on numerical information coded within Digital Imaging and Communications in Medicine files:This image numerical analysis has been named“radiomics”.Radiomics allows the extraction of quantitative features from radiological images,mainly invisible to the naked eye,that can be further analyzed by artificial intelligence algorithms.Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis,staging,and prognosis,prediction of treatment response and diseases monitoring and surveillance.Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography(CT)images with different aims:The preoperative prediction of lymph node metastasis,detecting BRAF and RAS gene mutations.Moreover,the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints.Most published studies concerning radiomics and magnetic resonance imaging(MRI)mainly focused on the response of advanced tumors that under-went neoadjuvant therapy.Nodes status is the main determinant of adjuvant chemotherapy.Therefore,several radiomics model based on MRI,especially on T2-weighted images and ADC maps,for the preoperative prediction of nodes metastasis in rectal cancer has been developed.Current studies mostly focused on the applications of radiomics in positron emission tomogra-phy/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy.Since colorectal liver metastases develop in about 25%of patients with colorectal carcinoma,the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions.Radiomics could be an additional tool in clinical setting,especially in identifying patients with high-risk disease.Nevertheless,radiomics has numerous shortcomings that make daily use extremely difficult.Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.展开更多
Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease,the management of hepatocellular carcinoma(HCC)patients requires experienced multidisciplinary team discussion.Moreover,imagi...Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease,the management of hepatocellular carcinoma(HCC)patients requires experienced multidisciplinary team discussion.Moreover,imaging plays a key role in the diagnosis,staging,restaging,and surveillance of HCC.Currently,imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability.Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging.The main potential applications of radiomic models in HCC are to predict histology,response to treatment,genetic signature,recurrence,and survival.Despite the encouraging results to date,there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice.The purpose of this article is to review the main concepts and challenges pertaining to radiomics,and to review recent studies and potential applications of radiomics in HCC.展开更多
BACKGROUND Macrotrabecular-massive hepatocellular carcinoma(MTM-HCC)is closely related to aggressive phenotype,gene mutation,carcinogenic pathway,and immunohistochemical markers and is a strong independent predictor o...BACKGROUND Macrotrabecular-massive hepatocellular carcinoma(MTM-HCC)is closely related to aggressive phenotype,gene mutation,carcinogenic pathway,and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis.With the development of imaging technology,successful applications of contrast-enhanced magnetic resonance imaging(MRI)have been reported in identifying the MTM-HCC subtype.Radiomics,as an objective and beneficial method for tumour evaluation,is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine.AIM To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms.METHODS This retrospective study enrolled 232(training set,162;test set,70)hepatocellular carcinoma patients from April 2018 to September 2021.A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI,followed by dimension reduction of these features.Logistic regression(LR),K-nearest neighbour(KNN),Bayes,Tree,and support vector machine(SVM)algorithms were used to select the best radiomics signature.We used the relative standard deviation(RSD)and bootstrap methods to quantify the stability of these five algorithms.The algorithm with the lowest RSD represented the best stability,and it was used to construct the best radiomics model.Multivariable logistic analysis was used to select the useful clinical and radiological features,and different predictive models were established.Finally,the predictive performances of the different models were assessed by evaluating the area under the curve(AUC).RESULTS The RSD values based on LR,KNN,Bayes,Tree,and SVM were 3.8%,8.6%,4.3%,17.7%,and 17.4%,respectively.Therefore,the LR machine learning algorithm was selected to construct the best radiomics signature,which performed well with AUCs of 0.766 and 0.739 in the training and test sets,respectively.In the multivariable analysis,age[odds ratio(OR)=0.956,P=0.034],alphafetoprotein(OR=10.066,P<0.001),tumour size(OR=3.316,P=0.002),tumour-to-liver apparent diffusion coefficient(ADC)ratio(OR=0.156,P=0.037),and radiomics score(OR=2.923,P<0.001)were independent predictors of MTM-HCC.Among the different models,the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model(AUCs:0.888 vs 0.836,P=0.046)and radiological model(AUCs:0.796 vs 0.688,P=0.012),respectively,in the training set,highlighting the improved predictive performance of radiomics.The nomogram performed best,with AUCs of 0.896 and 0.805 in the training and test sets,respectively.CONCLUSION The nomogram containing radiomics,age,alpha-fetoprotein,tumour size,and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype.展开更多
Background: Although transarterial chemoembolization(TACE) is the first-line therapy for intermediatestage hepatocellular carcinoma(HCC), it is not suitable for all patients. This study aimed to determine how to selec...Background: Although transarterial chemoembolization(TACE) is the first-line therapy for intermediatestage hepatocellular carcinoma(HCC), it is not suitable for all patients. This study aimed to determine how to select patients who are not suitable for TACE as the first treatment choice. Methods: A total of 243 intermediate-stage HCC patients treated with TACE at three centers were retrospectively enrolled, of which 171 were used for model training and 72 for testing. Radiomics features were screened using the Spearman correlation analysis and the least absolute shrinkage and selection operator(LASSO) algorithm. Subsequently, a radiomics model was established using extreme gradient boosting(XGBoost) with 5-fold cross-validation. The Shapley additive explanations(SHAP) method was used to visualize the radiomics model. A clinical model was constructed using univariate and multivariate logistic regression. The combined model comprising the radiomics signature and clinical factors was then established. This model’s performance was evaluated by discrimination, calibration, and clinical application. Generalization ability was evaluated by the testing cohort. Finally, the model was used to analyze overall and progression-free survival of different groups. Results: A third of the patients(81/243) were unsuitable for TACE treatment. The combined model had a high degree of accuracy as it identified TACE-unsuitable cases, at a sensitivity, specificity, and area under the receiver operating characteristic curve(AUC) of 0.759, 0.885, 0.906 [95% confidence interval(CI): 0.859-0.953] in the training cohort and 0.826, 0.776, and 0.894(95% CI: 0.815-0.972) in the testing cohort, respectively. Conclusions: The high degree of accuracy of our clinical-radiomics model makes it clinically useful in identifying intermediate-stage HCC patients who are unsuitable for TACE treatment.展开更多
BACKGROUND Esophagogastric variceal bleeding(EGVB)is a serious complication of patients with decompensated cirrhosis and is associated with high mortality and morbidity.Early diagnosis and screening of cirrhotic patie...BACKGROUND Esophagogastric variceal bleeding(EGVB)is a serious complication of patients with decompensated cirrhosis and is associated with high mortality and morbidity.Early diagnosis and screening of cirrhotic patients at risk for EGVB is crucial.Currently,there is a lack of noninvasive predictive models widely available in clinical practice.AIM To develop a nomogram based on clinical variables and radiomics to facilitate the noninvasive prediction of EGVB in cirrhotic patients.METHODS A total of 211 cirrhotic patients hospitalized between September 2017 and December 2021 were included in this retrospective study.Patients were divided into training(n=149)and validation(n=62)groups at a 7:3 ratio.Participants underwent three-phase computed tomography(CT)scans before endoscopy,and radiomic features were extracted from portal venous phase CT images.The independent sample t-test and least absolute shrinkage and selection operator logistic regression were used to screen out the best features and establish a radiomics signature(RadScore).Univariate and multivariate analyses were performed to determine the independent predictors of EGVB in clinical settings.A noninvasive predictive nomogram for the risk of EGVB was built using independent clinical predictors and RadScore.Receiver operating characteristic,calibration,clinical decision,and clinical impact curves were applied to evaluate the model’s performance.RESULTS Albumin(P=0.001),fibrinogen(P=0.001),portal vein thrombosis(P=0.002),aspartate aminotransferase(P=0.001),and spleen thickness(P=0.025)were selected as independent clinical predictors of EGVB.RadScore,constructed with five CT features of the liver region and three of the spleen regions,performed well in training(area under the receiver operating characteristic curve(AUC)=0.817)as well as in validation(AUC=0.741)cohorts.There was excellent predictive performance in both the training and validation cohorts for the clinical-radiomics model(AUC=0.925 and 0.912,respectively).Compared with the existing noninvasive models such as ratio of aspartate aminotransferase to platelets and Fibrosis-4 scores,our combined model had better predictive accuracy with the Delong's test less than 0.05.The Nomogram had a good fit in the calibration curve(P>0.05),and the clinical decision curve further supported its clinical utility.CONCLUSION We designed and validated a clinical-radiomics nomogram able to noninvasively predict whether cirrhotic patients will develop EGVB,thus facilitating early diagnosis and treatment.展开更多
Hepatocellular carcinoma(HCC)is a common malignant tumor.At present,early diagnosis of HCC is dif-ficult and therapeutic methods are limited.Radiomics can achieve accurate quantitative evaluation of the lesions withou...Hepatocellular carcinoma(HCC)is a common malignant tumor.At present,early diagnosis of HCC is dif-ficult and therapeutic methods are limited.Radiomics can achieve accurate quantitative evaluation of the lesions without invasion,and has important value in the diagnosis and treatment of HCC.Radiomics fea-tures can predict the development of cancer in patients,serve as the basis for risk stratification of HCC patients,and help clinicians distinguish similar diseases,thus improving the diagnostic accuracy.Further-more,the prediction of the treatment outcomes helps determine the treatment plan.Radiomics is also helpful in predicting the HCC recurrence,disease-free survival and overall survival.This review summa-rized the role of radiomics in the diagnosis,treatment and prognosis of HCC.展开更多
BACKGROUND Radical resection remains an effective strategy for patients with hepatocellular carcinoma(HCC).Unfortunately,the postoperative early recurrence(recurrence within 2 years)rate is still high.AIM To develop a...BACKGROUND Radical resection remains an effective strategy for patients with hepatocellular carcinoma(HCC).Unfortunately,the postoperative early recurrence(recurrence within 2 years)rate is still high.AIM To develop a radiomics model based on preoperative contrast-enhanced computed tomography(CECT)to evaluate early recurrence in HCC patients with a single tumour.METHODS We enrolled a total of 402 HCC patients from two centres who were diagnosed with a single tumour and underwent radical resection.First,the features from the portal venous and arterial phases of CECT were extracted based on the region of interest,and the early recurrence-related radiomics features were selected via the least absolute shrinkage and selection operator proportional hazards model(LASSO Cox)to determine radiomics scores for each patient.Then,the clinicopathologic data were combined to develop a model to predict early recurrence by Cox regression.Finally,we evaluated the prediction performance of this model by multiple methods.RESULTS A total of 1915 radiomics features were extracted from CECT images,and 31 of them were used to determine the radiomics scores,which showed a significant difference between the early recurrence and nonearly recurrence groups.Univariate and multivariate Cox regression analyses showed that radiomics scores and serum alphafetoprotein were independent indicators,and they were used to develop a combined model to predict early recurrence.The area under the receiver operating characteristic curve values for the training and validation cohorts were 0.77 and 0.74,respectively,while the C-indices were 0.712 and 0.674,respectively.The calibration curves and decision curve analysis showed satisfactory accuracy and clinical utilities.Kaplan-Meier curves based on recurrence-free survival and overall survival showed significant differences.CONCLUSION The preoperative radiomics model was shown to be effective for predicting early recurrence among HCC patients with a single tumour.展开更多
Objective:To investigate the prognostic value of radiomics features based on ^(18)F-FDG PET/CT imaging for advanced non-small cell lung cancer(NSCLC)treated with chemotherapy.Methods:A sample of 146 NSCLC patients sta...Objective:To investigate the prognostic value of radiomics features based on ^(18)F-FDG PET/CT imaging for advanced non-small cell lung cancer(NSCLC)treated with chemotherapy.Methods:A sample of 146 NSCLC patients stagedⅢor stageⅣwere included in this retrospective study who received ^(18)F-FDG PET/CT before treatment.All patients were treated with standardized chemotherapy after PET/CT examination and were divided into training group and validation group in an 8:2 ratio randomly.Radiomics features were extracted.In the training group,the minimum absolute contraction and selection operator(LASSO)algorithm and Cox risk proportional regression model were used to screen radiomics and clinical prognostic factors of progression-free survival(PFS).The radiomic model,clinical model and complex model were established respectively.The corresponding scores were calculated,then verified in the validation group.Results:The LASSO algorithm finally screened four radiomics features.ROC results showed that in the training group,the AUC of PFS predicted by the radiomics model was 0.746,and that in the verification group was 0.622.COX multivariate analysis finally included three clinical features related to PFS in NSCLC patients,namely pathological type,clinical stage and MTV30.The AUC for predicting PFS by clinical model,radiomics model and composite model were 0.746,0.753 and 0.716,respectively.The radiomics model had the highest diagnostic efficacy,and its sensitivity and specificity were 0.663 and 0.833,respectively.Delong test verified that there was no statistical difference in the predictive efficacy between the radiomics model and the composite model(Z=1.777,P=0.076)and the clinical imaging model(Z=0.323,P=0.747).Conclusion:The radiomics model based on PET/CT has a good predictive value for the prognosis of advanced NSCLC treated with chemotherapy,but it needs further validation before it can be widely used in clinical practice.展开更多
Esophageal cancer,also referred to as esophagus cancer,is a prevalent disease in the cardiothoracic field and is a leading cause of cancer-related mortality in China.Accurately determining the status of lymph nodes is...Esophageal cancer,also referred to as esophagus cancer,is a prevalent disease in the cardiothoracic field and is a leading cause of cancer-related mortality in China.Accurately determining the status of lymph nodes is crucial for developing treatment plans,defining the scope of intraoperative lymph node dissection,and ascertaining the prognosis of patients with esophageal cancer.Recent advances in diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging(MRI)have improved the effectiveness of MRI for assessing lymph node involvement,making it a beneficial tool for guiding personalized treatment plans for patients with esophageal cancer in a clinical setting.Radiomics is a recently developed imaging technique that transforms radiological image data from regions of interest into high-dimensional feature data that can be analyzed.The features,such as shape,texture,and waveform,are associated with the cancer phenotype and tumor microenvironment.When these features correlate with the clinical disease outcomes,they form the basis for specific and reliable clinical evidence.This study aimed to review the potential clinical applications of MRIbased radiomics in studying the lymph nodes affected by esophageal cancer.The combination of MRI and radiomics is a powerful tool for diagnosing and treating esophageal cancer,enabling a more personalized and effectual approach.展开更多
BACKGROUND Radiomics can assess prognostic factors in several types of tumors,but considering its prognostic ability in pancreatic cancer has been lacking.AIM To evaluate the performance of two different radiomics sof...BACKGROUND Radiomics can assess prognostic factors in several types of tumors,but considering its prognostic ability in pancreatic cancer has been lacking.AIM To evaluate the performance of two different radiomics software in assessing survival outcomes in pancreatic cancer patients.METHODS We retrospectively reviewed pretreatment contrast-enhanced dual-energy computed tomography images from 48 patients with biopsy-confirmed pancreatic ductal adenocarcinoma who later underwent neoadjuvant chemoradiation and surgery.Tumors were segmented using TexRad software for 2-dimensional(2D)analysis and MIM software for 3D analysis,followed by radiomic feature extraction.Cox proportional hazard modeling correlated texture features with overall survival(OS)and progression-free survival(PFS).Cox regression was used to detect differences in OS related to pretreatment tumor size and residual tumor following treatment.The Wilcoxon test was used to show the relationship between tumor volume and the percent of residual tumor.Kaplan-Meier analysis was used to compare survival in patients with different tumor densities in Hounsfield units for both 2D and 3D analysis.RESULTS 3D analysis showed that higher mean tumor density[hazard ratio(HR)=0.971,P=0.041)]and higher median tumor density(HR=0.970,P=0.037)correlated with better OS.2D analysis showed that higher mean tumor density(HR=0.963,P=0.014)and higher mean positive pixels(HR=0.962,P=0.014)correlated with better OS;higher skewness(HR=3.067,P=0.008)and higher kurtosis(HR=1.176,P=0.029)correlated with worse OS.Higher entropy correlated with better PFS(HR=0.056,P=0.036).Models determined that patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumors of over 10%.CONCLUSION Several radiomics features can be used as prognostic tools for pancreatic cancer.However,results vary between 2D and 3D analyses.Mean tumor density was the only variable that could reliably predict OS,irrespective of the analysis used.展开更多
Objective: To develop and validate a computed tomography(CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2(HER2) status in patients with gastric cancer.Methods: This retrospective st...Objective: To develop and validate a computed tomography(CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2(HER2) status in patients with gastric cancer.Methods: This retrospective study included 134 patients with gastric cancer(HER2-negative: n=87;HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training(n=94) and validation(n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator(LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts.Results: The radiomics signature was significantly associated with HER2 status in both training(P<0.001) and validation(P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen(CEA) level demonstrated good discriminative performance for HER2 status prediction,with an area under the curve(AUC) of 0.799 [95% confidence interval(95% CI): 0.704-0.894] in the training cohort and 0.771(95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful.Conclusions: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.展开更多
Objective: The standard treatment for patients with locally advanced gastric cancer has relied on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of ra...Objective: The standard treatment for patients with locally advanced gastric cancer has relied on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomics for pre-treatment computed tomography(CT) in the prediction of the pathological response of locally advanced gastric cancer with preoperative chemotherapy.Methods: Thirty consecutive patients with CT-staged II/III gastric cancer receiving neoadjuvant chemotherapy were enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CT during the unenhanced, late arterial phase(AP) and portal venous phase(PP) before the administration of neoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each patient.Four methods were adopted during feature selection and eight methods were used in the process of building the classifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiver operating characteristic(ROC) curves were used to evaluate the capability of each combination of feature selection and classification method to predict a non-good response(non-GR) based on tumor regression grade(TRG).Results: The mean area under the curve(AUC) ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722 in the PP, according to different combinations of feature selection and the classification methods. There was only one cross-combination machine-learning method indicating a relatively higher AUC(>0.600) in the AP, while 12 cross-combination machine-learning methods presented relatively higher AUCs(all >0.600) in the PP. The feature selection method adopted by a filter based on linear discriminant analysis + classifier of random forest achieved a significantly prognostic performance in the PP(AUC, 0.722±0.108; accuracy, 0.793; sensitivity, 0.636; specificity,0.889; Z=2.039; P=0.041).Conclusions: It is possible to predict non-GR after neoadjuvant chemotherapy in locally advanced gastric cancers based on the radiomics of CT.展开更多
BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative resi...BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.AIM To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.METHODS For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.RESULTS Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure(area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure(area under the curve: 0.894;95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model(integrated discrimination improvement = 0.117, P =0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic(P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.CONCLUSION A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy.展开更多
The high incidence of rectal cancer in both sexes makes it one of the most common tumors,with significant morbidity and mortality rates.To define the best treatment option and optimize patient outcome,several rectal c...The high incidence of rectal cancer in both sexes makes it one of the most common tumors,with significant morbidity and mortality rates.To define the best treatment option and optimize patient outcome,several rectal cancer biological variables must be evaluated.Currently,medical imaging plays a crucial role in the characterization of this disease,and it often requires a multimodal approach.Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors.Computed tomography is widely adopted for the detection of distant metastases.However,conventional imaging has recognized limitations,and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation.There is a growing interest in artificial intelligence applications in medicine,and imaging is by no means an exception.The introduction of radiomics,which allows the extraction of quantitative features that reflect tumor heterogeneity,allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers.To manage such a huge amount of data,the use of machine learning algorithms has been proposed.Indeed,without prior explicit programming,they can be employed to build prediction models to support clinical decision making.In this review,current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented,with an imaging modality-based approach and a keen eye on unsolved issues.The results are promising,but the road ahead for translation in clinical practice is rather long.展开更多
Objective: To develop and validate a radiomics-based predictive risk score(RPRS) for preoperative prediction of lymph node(LN) metastasis in patients with resectable non-small cell lung cancer(NSCLC).Methods: We retro...Objective: To develop and validate a radiomics-based predictive risk score(RPRS) for preoperative prediction of lymph node(LN) metastasis in patients with resectable non-small cell lung cancer(NSCLC).Methods: We retrospectively analyzed 717 who underwent surgical resection for primary NSCLC with systematic mediastinal lymphadenectomy from October 2007 to July 2016. By using the method of radiomics analysis, 591 computed tomography(CT)-based radiomics features were extracted, and the radiomics-based classifier was constructed. Then, using multivariable logistic regression analysis, a weighted score RPRS was derived to identify LN metastasis. Apparent prediction performance of RPRS was assessed with its calibration,discrimination, and clinical usefulness.Results: The radiomics-based classifier was constructed, which consisted of 13 selected radiomics features.Multivariate models demonstrated that radiomics-based classifier, age group, tumor diameter, tumor location, and CT-based LN status were independent predictors. When we assigned the corresponding score to each variable,patients with RPRSs of 0-3, 4-5, 6, 7-8, and 9 had distinctly very low(0%-20%), low(21%-40%), intermediate(41%-60%), high(61%-80%), and very high(81%-100%) risks of LN involvement, respectively. The developed RPRS showed good discrimination and satisfactory calibration (C-index: 0.785, 95% confidence interval(95% CI):0.780-0.790)Additionally, RPRS outperformed the clinicopathologic-based characteristics model with net reclassification index(NRI) of 0.711(95% CI: 0.555-0.867).Conclusions: The novel clinical scoring system developed as RPRS can serve as an easy-to-use tool to facilitate the preoperatively individualized prediction of LN metastasis in patients with resectable NSCLC. This stratification of patients according to their LN status may provide a basis for individualized treatment.展开更多
基金Supported by the“Ricerca Corrente”Grant from Italian Ministry of Health,No.IRCCS SYNLAB SDN.
文摘BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging(MRI)for different tasks related to the management of patients with hepatocellular carcinoma(HCC).However,its implementation in clinical practice is still far,with many issues related to the methodological quality of radiomic studies.AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score(RQS).METHODS A systematic literature search of PubMed,Google Scholar,and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023.The methodological quality of radiomic studies was assessed using the RQS tool.Spearman’s correlation(ρ)analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies.The level of statistical significance was set at P<0.05.RESULTS One hundred and twenty-seven articles were included,of which 43 focused on HCC prognosis,39 on prediction of pathological findings,16 on prediction of the expression of molecular markers outcomes,18 had a diagnostic purpose,and 11 had multiple purposes.The mean RQS was 8±6.22,and the corresponding percentage was 24.15%±15.25%(ranging from 0.0% to 58.33%).RQS was positively correlated with journal impact factor(IF;ρ=0.36,P=2.98×10^(-5)),5-years IF(ρ=0.33,P=1.56×10^(-4)),number of patients included in the study(ρ=0.51,P<9.37×10^(-10))and number of radiomics features extracted in the study(ρ=0.59,P<4.59×10^(-13)),and time of publication(ρ=-0.23,P<0.0072).CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients,our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
基金supported in part by the National Natural Science Foundation of China(82072019)the Shenzhen Basic Research Program(JCYJ20210324130209023)+5 种基金the Shenzhen-Hong Kong-Macao S&T Program(Category C)(SGDX20201103095002019)the Mainland-Hong Kong Joint Funding Scheme(MHKJFS)(MHP/005/20),the Project of Strategic Importance Fund(P0035421)the Projects of RISA(P0043001)from the Hong Kong Polytechnic University,the Natural Science Foundation of Jiangsu Province(BK20201441)the Provincial and Ministry Co-constructed Project of Henan Province Medical Science and Technology Research(SBGJ202103038,SBGJ202102056)the Henan Province Key R&D and Promotion Project(Science and Technology Research)(222102310015)the Natural Science Foundation of Henan Province(222300420575),and the Henan Province Science and Technology Research(222102310322).
文摘Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.
基金Supported by the National Natural Science foundation of China,No.82202135,82371919,82372017,and 82171925China Postdoctoral Science Foundation,No.2023M741808+3 种基金Young Elite Scientists Sponsorship Program by Jiangsu Association for Science and Technology,No.JSTJ-2023-WJ027Foundation of Excellent Young Doctor of Jiangsu Province Hospital of Chinese Medicine,No.2023QB0112Nanjing Postdoctoral Science Foundation,Natural Science Foundation of Nanjing University of Chinese Medicine,No.XZR2023036 and XZR2021050Medical Imaging Artificial Intelligence Special Research Fund Project,Nanjing Medical Association Radiology Branch,Project of National Clinical Research Base of Traditional Chinese Medicine in Jiangsu Province,China,No.JD2023SZ16.
文摘BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma(PDAC)is that most patients are usually diagnosed at late stages.There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages.METHODS A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography(CT)within 30 d prior to surgery were included in the study.Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system.Radiomics features were extracted from the region of interest(ROI)for each patient using Analysis Kit software.The most important and predictive radiomics features were selected using Mann-Whitney U test,univar-iate logistic regression analysis,and minimum redundancy maximum relevance(MRMR)method.Random forest(RF)method was used to construct the radiomics model,and 10-times leave group out cross-validation(LGOCV)method was used to validate the robustness and reproducibility of the model.RESULTS A total of 792 radiomics features(396 from late arterial phase and 396 from portal venous phase)were extracted from the ROI for each patient using Analysis Kit software.Nine most important and predictive features were selected using Mann-Whitney U test,univariate logistic regression analysis,and MRMR method.RF method was used to construct the radiomics model with the nine most predictive radiomics features,which showed a high discriminative ability with 97.7%accuracy,97.6%sensitivity,97.8%specificity,98.4%positive predictive value,and 96.8%negative predictive value.The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models.CONCLUSION The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.
文摘BACKGROUND Although accurately evaluating the overall survival(OS)of gastric cancer patients remains difficult,radiomics is considered an important option for studying pro-gnosis.AIM To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography(CT)image radiomics.METHODS This study included 181 stage II/III gastric cancer patients,141 from Lichuan People's Hospital,and 40 from the Cancer Imaging Archive(TCIA).Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest(ROI),and approximately 1700 radiomics features were extracted from each ROI.The skeletal muscle index(SMI)and skeletal muscle density(SMD)were measured using CT images from the lower margin of the third lumbar vertebra.Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation,36 radiomics features were identified as important predictors,and the OS-associated CT image radiomics score(OACRS)was cal-culated for each patient using these important predictors.RESULTS Patients with a high OACRS had a poorer prognosis than those with a low OACRS score(P<0.05)and those in the TCIA cohort.Univariate and multivariate analyses revealed that OACRS was a risk factor[RR=3.023(1.896-4.365),P<0.001]independent of SMI,SMD,and pathological features.Moreover,OACRS outperformed SMI and SMD and could improve OS prediction(P<0.05).CONCLUSION A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.
基金supported by the Shandong Province Traditional Chinese Medicine Science and Technology Project (M-2022012)。
文摘Background:Traditional Chinese medicine(TCM)is commonly used for the diagnosis and treatment of insomnia,with tongue diagnosis being particularly important.The aim of our study was to develop and validate a novel tongue imaging-based radiomics(TIR)method for accurately diagnosing insomnia severity.Methods:This two-center analysis prospectively enrolled 399 patients who underwent tongue imaging between July and October 2021 and divided them into primary and validation cohorts by study center.Here,we referred to the Insomnia Severity Index(ISI)standard and the degree of insomnia was evaluated as absent,subthreshold,moderate,or severe.For developed the TIR diagnostic tool,a U-Net algorithm was used to segment tongue images.Subsequently,seven imaging features were selected from the extracted high-throughout radiomics features using the least absolute shrinkage and selection operator algorithm.Then,the final radiomics model was developed in the primary cohort and tested in the independent validation cohort.Finally,we assessed and compared the diagnostic performance differences between TCM tongue diagnosis and our TIR diagnostic tool with the ISI gold standard.The confusion matrix was calculated to evaluate the diagnostic performance.Results:Seven tongue imaging features were selected to build the TIR tool,with showing good correlations with the insomnia degree.The TIR method had an accuracy of 0.798,a macro-average sensitivity of 0.78,a macro-average specificity of 0.906,a weighted-average sensitivity of 0.798,and a weighted specificity of 0.916,showing a significantly better performance compared to the average performance of three experienced TCM physicians(mean accuracy of 0.458,P<0.01).Conclusions:The preliminary study demonstrates the potential application of TIR in the diagnosis of insomnia degree and measurement of sleep health.The integration of quantitative imaging analysis and machine learning algorithms holds promise for advancing both of TCM and precision sleep medicine.
文摘The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease.However,the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging.Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques,working on numerical information coded within Digital Imaging and Communications in Medicine files:This image numerical analysis has been named“radiomics”.Radiomics allows the extraction of quantitative features from radiological images,mainly invisible to the naked eye,that can be further analyzed by artificial intelligence algorithms.Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis,staging,and prognosis,prediction of treatment response and diseases monitoring and surveillance.Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography(CT)images with different aims:The preoperative prediction of lymph node metastasis,detecting BRAF and RAS gene mutations.Moreover,the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints.Most published studies concerning radiomics and magnetic resonance imaging(MRI)mainly focused on the response of advanced tumors that under-went neoadjuvant therapy.Nodes status is the main determinant of adjuvant chemotherapy.Therefore,several radiomics model based on MRI,especially on T2-weighted images and ADC maps,for the preoperative prediction of nodes metastasis in rectal cancer has been developed.Current studies mostly focused on the applications of radiomics in positron emission tomogra-phy/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy.Since colorectal liver metastases develop in about 25%of patients with colorectal carcinoma,the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions.Radiomics could be an additional tool in clinical setting,especially in identifying patients with high-risk disease.Nevertheless,radiomics has numerous shortcomings that make daily use extremely difficult.Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
基金the NIH/NCI Cancer Center Support Grant,P30 CA008748。
文摘Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease,the management of hepatocellular carcinoma(HCC)patients requires experienced multidisciplinary team discussion.Moreover,imaging plays a key role in the diagnosis,staging,restaging,and surveillance of HCC.Currently,imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability.Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging.The main potential applications of radiomic models in HCC are to predict histology,response to treatment,genetic signature,recurrence,and survival.Despite the encouraging results to date,there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice.The purpose of this article is to review the main concepts and challenges pertaining to radiomics,and to review recent studies and potential applications of radiomics in HCC.
基金Supported by Zhejiang Provincial Natural Science Foundation of China,No.LTGY23H180017Medical Science and Technology Project of Zhejiang Province,No.2023KY503.
文摘BACKGROUND Macrotrabecular-massive hepatocellular carcinoma(MTM-HCC)is closely related to aggressive phenotype,gene mutation,carcinogenic pathway,and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis.With the development of imaging technology,successful applications of contrast-enhanced magnetic resonance imaging(MRI)have been reported in identifying the MTM-HCC subtype.Radiomics,as an objective and beneficial method for tumour evaluation,is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine.AIM To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms.METHODS This retrospective study enrolled 232(training set,162;test set,70)hepatocellular carcinoma patients from April 2018 to September 2021.A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI,followed by dimension reduction of these features.Logistic regression(LR),K-nearest neighbour(KNN),Bayes,Tree,and support vector machine(SVM)algorithms were used to select the best radiomics signature.We used the relative standard deviation(RSD)and bootstrap methods to quantify the stability of these five algorithms.The algorithm with the lowest RSD represented the best stability,and it was used to construct the best radiomics model.Multivariable logistic analysis was used to select the useful clinical and radiological features,and different predictive models were established.Finally,the predictive performances of the different models were assessed by evaluating the area under the curve(AUC).RESULTS The RSD values based on LR,KNN,Bayes,Tree,and SVM were 3.8%,8.6%,4.3%,17.7%,and 17.4%,respectively.Therefore,the LR machine learning algorithm was selected to construct the best radiomics signature,which performed well with AUCs of 0.766 and 0.739 in the training and test sets,respectively.In the multivariable analysis,age[odds ratio(OR)=0.956,P=0.034],alphafetoprotein(OR=10.066,P<0.001),tumour size(OR=3.316,P=0.002),tumour-to-liver apparent diffusion coefficient(ADC)ratio(OR=0.156,P=0.037),and radiomics score(OR=2.923,P<0.001)were independent predictors of MTM-HCC.Among the different models,the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model(AUCs:0.888 vs 0.836,P=0.046)and radiological model(AUCs:0.796 vs 0.688,P=0.012),respectively,in the training set,highlighting the improved predictive performance of radiomics.The nomogram performed best,with AUCs of 0.896 and 0.805 in the training and test sets,respectively.CONCLUSION The nomogram containing radiomics,age,alpha-fetoprotein,tumour size,and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype.
基金supported in part by grants from the National Key Research and Development Program (2019YFC0118100 and 2017YFC0110903)the National Natural Science Foundation of China (12026602 and 81802649)+2 种基金the Guangdong Key Area Re-search and Development Program (2020B010165004)the Shen-zhen Key Basic Science Program (JCYJ20180507182437217)the Shenzhen Key Laboratory Program (ZDSYS201707271637577)
文摘Background: Although transarterial chemoembolization(TACE) is the first-line therapy for intermediatestage hepatocellular carcinoma(HCC), it is not suitable for all patients. This study aimed to determine how to select patients who are not suitable for TACE as the first treatment choice. Methods: A total of 243 intermediate-stage HCC patients treated with TACE at three centers were retrospectively enrolled, of which 171 were used for model training and 72 for testing. Radiomics features were screened using the Spearman correlation analysis and the least absolute shrinkage and selection operator(LASSO) algorithm. Subsequently, a radiomics model was established using extreme gradient boosting(XGBoost) with 5-fold cross-validation. The Shapley additive explanations(SHAP) method was used to visualize the radiomics model. A clinical model was constructed using univariate and multivariate logistic regression. The combined model comprising the radiomics signature and clinical factors was then established. This model’s performance was evaluated by discrimination, calibration, and clinical application. Generalization ability was evaluated by the testing cohort. Finally, the model was used to analyze overall and progression-free survival of different groups. Results: A third of the patients(81/243) were unsuitable for TACE treatment. The combined model had a high degree of accuracy as it identified TACE-unsuitable cases, at a sensitivity, specificity, and area under the receiver operating characteristic curve(AUC) of 0.759, 0.885, 0.906 [95% confidence interval(CI): 0.859-0.953] in the training cohort and 0.826, 0.776, and 0.894(95% CI: 0.815-0.972) in the testing cohort, respectively. Conclusions: The high degree of accuracy of our clinical-radiomics model makes it clinically useful in identifying intermediate-stage HCC patients who are unsuitable for TACE treatment.
基金The study was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Chongqing Medical University(No.2022-149).
文摘BACKGROUND Esophagogastric variceal bleeding(EGVB)is a serious complication of patients with decompensated cirrhosis and is associated with high mortality and morbidity.Early diagnosis and screening of cirrhotic patients at risk for EGVB is crucial.Currently,there is a lack of noninvasive predictive models widely available in clinical practice.AIM To develop a nomogram based on clinical variables and radiomics to facilitate the noninvasive prediction of EGVB in cirrhotic patients.METHODS A total of 211 cirrhotic patients hospitalized between September 2017 and December 2021 were included in this retrospective study.Patients were divided into training(n=149)and validation(n=62)groups at a 7:3 ratio.Participants underwent three-phase computed tomography(CT)scans before endoscopy,and radiomic features were extracted from portal venous phase CT images.The independent sample t-test and least absolute shrinkage and selection operator logistic regression were used to screen out the best features and establish a radiomics signature(RadScore).Univariate and multivariate analyses were performed to determine the independent predictors of EGVB in clinical settings.A noninvasive predictive nomogram for the risk of EGVB was built using independent clinical predictors and RadScore.Receiver operating characteristic,calibration,clinical decision,and clinical impact curves were applied to evaluate the model’s performance.RESULTS Albumin(P=0.001),fibrinogen(P=0.001),portal vein thrombosis(P=0.002),aspartate aminotransferase(P=0.001),and spleen thickness(P=0.025)were selected as independent clinical predictors of EGVB.RadScore,constructed with five CT features of the liver region and three of the spleen regions,performed well in training(area under the receiver operating characteristic curve(AUC)=0.817)as well as in validation(AUC=0.741)cohorts.There was excellent predictive performance in both the training and validation cohorts for the clinical-radiomics model(AUC=0.925 and 0.912,respectively).Compared with the existing noninvasive models such as ratio of aspartate aminotransferase to platelets and Fibrosis-4 scores,our combined model had better predictive accuracy with the Delong's test less than 0.05.The Nomogram had a good fit in the calibration curve(P>0.05),and the clinical decision curve further supported its clinical utility.CONCLUSION We designed and validated a clinical-radiomics nomogram able to noninvasively predict whether cirrhotic patients will develop EGVB,thus facilitating early diagnosis and treatment.
基金supported by grants from the National Natural Science Foundation of China(82272396)Suzhou Medical and Health Science and Technology Innovation Project(SKY2022057).
文摘Hepatocellular carcinoma(HCC)is a common malignant tumor.At present,early diagnosis of HCC is dif-ficult and therapeutic methods are limited.Radiomics can achieve accurate quantitative evaluation of the lesions without invasion,and has important value in the diagnosis and treatment of HCC.Radiomics fea-tures can predict the development of cancer in patients,serve as the basis for risk stratification of HCC patients,and help clinicians distinguish similar diseases,thus improving the diagnostic accuracy.Further-more,the prediction of the treatment outcomes helps determine the treatment plan.Radiomics is also helpful in predicting the HCC recurrence,disease-free survival and overall survival.This review summa-rized the role of radiomics in the diagnosis,treatment and prognosis of HCC.
基金National Natural Science Foundation of China,No.81773148Natural Science Foundation of Guangxi,No.2018GXNSFDA138001+3 种基金Program of Guangxi Zhuang Autonomous Region Health and Family Planning Commission,No.Z20210706Guangxi Medical and Healthcare Appropriate Technology Development and Promotion and Application Projects,No.S2022132Guangxi Natural Science Foundation,No.2022JJA140009Guangxi Zhuang Autonomous Region Health and Family Planning Commission Self-funded of Scientific Research Project,No.Z20170812.
文摘BACKGROUND Radical resection remains an effective strategy for patients with hepatocellular carcinoma(HCC).Unfortunately,the postoperative early recurrence(recurrence within 2 years)rate is still high.AIM To develop a radiomics model based on preoperative contrast-enhanced computed tomography(CECT)to evaluate early recurrence in HCC patients with a single tumour.METHODS We enrolled a total of 402 HCC patients from two centres who were diagnosed with a single tumour and underwent radical resection.First,the features from the portal venous and arterial phases of CECT were extracted based on the region of interest,and the early recurrence-related radiomics features were selected via the least absolute shrinkage and selection operator proportional hazards model(LASSO Cox)to determine radiomics scores for each patient.Then,the clinicopathologic data were combined to develop a model to predict early recurrence by Cox regression.Finally,we evaluated the prediction performance of this model by multiple methods.RESULTS A total of 1915 radiomics features were extracted from CECT images,and 31 of them were used to determine the radiomics scores,which showed a significant difference between the early recurrence and nonearly recurrence groups.Univariate and multivariate Cox regression analyses showed that radiomics scores and serum alphafetoprotein were independent indicators,and they were used to develop a combined model to predict early recurrence.The area under the receiver operating characteristic curve values for the training and validation cohorts were 0.77 and 0.74,respectively,while the C-indices were 0.712 and 0.674,respectively.The calibration curves and decision curve analysis showed satisfactory accuracy and clinical utilities.Kaplan-Meier curves based on recurrence-free survival and overall survival showed significant differences.CONCLUSION The preoperative radiomics model was shown to be effective for predicting early recurrence among HCC patients with a single tumour.
基金Research and Cultivation Foundation of Hainan Medical College(HYPY2020022)Hainan Natural Science Foundation Youth fund(822QN482)+1 种基金Doctoral Research Fund project of Hainan Cancer Hospital(2022BS04)Key R&D projects in Hainan Province(ZDYF2021SHFZ244)。
文摘Objective:To investigate the prognostic value of radiomics features based on ^(18)F-FDG PET/CT imaging for advanced non-small cell lung cancer(NSCLC)treated with chemotherapy.Methods:A sample of 146 NSCLC patients stagedⅢor stageⅣwere included in this retrospective study who received ^(18)F-FDG PET/CT before treatment.All patients were treated with standardized chemotherapy after PET/CT examination and were divided into training group and validation group in an 8:2 ratio randomly.Radiomics features were extracted.In the training group,the minimum absolute contraction and selection operator(LASSO)algorithm and Cox risk proportional regression model were used to screen radiomics and clinical prognostic factors of progression-free survival(PFS).The radiomic model,clinical model and complex model were established respectively.The corresponding scores were calculated,then verified in the validation group.Results:The LASSO algorithm finally screened four radiomics features.ROC results showed that in the training group,the AUC of PFS predicted by the radiomics model was 0.746,and that in the verification group was 0.622.COX multivariate analysis finally included three clinical features related to PFS in NSCLC patients,namely pathological type,clinical stage and MTV30.The AUC for predicting PFS by clinical model,radiomics model and composite model were 0.746,0.753 and 0.716,respectively.The radiomics model had the highest diagnostic efficacy,and its sensitivity and specificity were 0.663 and 0.833,respectively.Delong test verified that there was no statistical difference in the predictive efficacy between the radiomics model and the composite model(Z=1.777,P=0.076)and the clinical imaging model(Z=0.323,P=0.747).Conclusion:The radiomics model based on PET/CT has a good predictive value for the prognosis of advanced NSCLC treated with chemotherapy,but it needs further validation before it can be widely used in clinical practice.
文摘Esophageal cancer,also referred to as esophagus cancer,is a prevalent disease in the cardiothoracic field and is a leading cause of cancer-related mortality in China.Accurately determining the status of lymph nodes is crucial for developing treatment plans,defining the scope of intraoperative lymph node dissection,and ascertaining the prognosis of patients with esophageal cancer.Recent advances in diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging(MRI)have improved the effectiveness of MRI for assessing lymph node involvement,making it a beneficial tool for guiding personalized treatment plans for patients with esophageal cancer in a clinical setting.Radiomics is a recently developed imaging technique that transforms radiological image data from regions of interest into high-dimensional feature data that can be analyzed.The features,such as shape,texture,and waveform,are associated with the cancer phenotype and tumor microenvironment.When these features correlate with the clinical disease outcomes,they form the basis for specific and reliable clinical evidence.This study aimed to review the potential clinical applications of MRIbased radiomics in studying the lymph nodes affected by esophageal cancer.The combination of MRI and radiomics is a powerful tool for diagnosing and treating esophageal cancer,enabling a more personalized and effectual approach.
基金The study was reviewed and approved by the University of Texas MD Anderson Cancer Center Institutional Review Board(approval No.4 IRB00005015).
文摘BACKGROUND Radiomics can assess prognostic factors in several types of tumors,but considering its prognostic ability in pancreatic cancer has been lacking.AIM To evaluate the performance of two different radiomics software in assessing survival outcomes in pancreatic cancer patients.METHODS We retrospectively reviewed pretreatment contrast-enhanced dual-energy computed tomography images from 48 patients with biopsy-confirmed pancreatic ductal adenocarcinoma who later underwent neoadjuvant chemoradiation and surgery.Tumors were segmented using TexRad software for 2-dimensional(2D)analysis and MIM software for 3D analysis,followed by radiomic feature extraction.Cox proportional hazard modeling correlated texture features with overall survival(OS)and progression-free survival(PFS).Cox regression was used to detect differences in OS related to pretreatment tumor size and residual tumor following treatment.The Wilcoxon test was used to show the relationship between tumor volume and the percent of residual tumor.Kaplan-Meier analysis was used to compare survival in patients with different tumor densities in Hounsfield units for both 2D and 3D analysis.RESULTS 3D analysis showed that higher mean tumor density[hazard ratio(HR)=0.971,P=0.041)]and higher median tumor density(HR=0.970,P=0.037)correlated with better OS.2D analysis showed that higher mean tumor density(HR=0.963,P=0.014)and higher mean positive pixels(HR=0.962,P=0.014)correlated with better OS;higher skewness(HR=3.067,P=0.008)and higher kurtosis(HR=1.176,P=0.029)correlated with worse OS.Higher entropy correlated with better PFS(HR=0.056,P=0.036).Models determined that patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumors of over 10%.CONCLUSION Several radiomics features can be used as prognostic tools for pancreatic cancer.However,results vary between 2D and 3D analyses.Mean tumor density was the only variable that could reliably predict OS,irrespective of the analysis used.
基金supported by the National Key Research and Development Program of China (No. 2017YFC1309100)National Natural Scientific Foundation of China (No. 81771912, 81601469, and 81701782)+1 种基金the Science and Technology Planning Project of Guangdong Province (No. 2017B020227012)the Science and Technology Planning Project of Guangzhou (No. 20191A011002).
文摘Objective: To develop and validate a computed tomography(CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2(HER2) status in patients with gastric cancer.Methods: This retrospective study included 134 patients with gastric cancer(HER2-negative: n=87;HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training(n=94) and validation(n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator(LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts.Results: The radiomics signature was significantly associated with HER2 status in both training(P<0.001) and validation(P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen(CEA) level demonstrated good discriminative performance for HER2 status prediction,with an area under the curve(AUC) of 0.799 [95% confidence interval(95% CI): 0.704-0.894] in the training cohort and 0.771(95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful.Conclusions: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.
基金supported by the National Key Research and Development Program of China (No.2017YFC1309100)the National Natural Scientific Foundation of China (No. 81771912)the Applied Basic Research Projects of Yunnan Province, China [No. 2015FB071 and No. 2017FE467-084]
文摘Objective: The standard treatment for patients with locally advanced gastric cancer has relied on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomics for pre-treatment computed tomography(CT) in the prediction of the pathological response of locally advanced gastric cancer with preoperative chemotherapy.Methods: Thirty consecutive patients with CT-staged II/III gastric cancer receiving neoadjuvant chemotherapy were enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CT during the unenhanced, late arterial phase(AP) and portal venous phase(PP) before the administration of neoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each patient.Four methods were adopted during feature selection and eight methods were used in the process of building the classifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiver operating characteristic(ROC) curves were used to evaluate the capability of each combination of feature selection and classification method to predict a non-good response(non-GR) based on tumor regression grade(TRG).Results: The mean area under the curve(AUC) ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722 in the PP, according to different combinations of feature selection and the classification methods. There was only one cross-combination machine-learning method indicating a relatively higher AUC(>0.600) in the AP, while 12 cross-combination machine-learning methods presented relatively higher AUCs(all >0.600) in the PP. The feature selection method adopted by a filter based on linear discriminant analysis + classifier of random forest achieved a significantly prognostic performance in the PP(AUC, 0.722±0.108; accuracy, 0.793; sensitivity, 0.636; specificity,0.889; Z=2.039; P=0.041).Conclusions: It is possible to predict non-GR after neoadjuvant chemotherapy in locally advanced gastric cancers based on the radiomics of CT.
基金the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2017)the Guangdong Natural Science Foundation,No.2017A030313777。
文摘BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.AIM To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.METHODS For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.RESULTS Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure(area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure(area under the curve: 0.894;95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model(integrated discrimination improvement = 0.117, P =0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic(P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.CONCLUSION A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy.
文摘The high incidence of rectal cancer in both sexes makes it one of the most common tumors,with significant morbidity and mortality rates.To define the best treatment option and optimize patient outcome,several rectal cancer biological variables must be evaluated.Currently,medical imaging plays a crucial role in the characterization of this disease,and it often requires a multimodal approach.Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors.Computed tomography is widely adopted for the detection of distant metastases.However,conventional imaging has recognized limitations,and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation.There is a growing interest in artificial intelligence applications in medicine,and imaging is by no means an exception.The introduction of radiomics,which allows the extraction of quantitative features that reflect tumor heterogeneity,allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers.To manage such a huge amount of data,the use of machine learning algorithms has been proposed.Indeed,without prior explicit programming,they can be employed to build prediction models to support clinical decision making.In this review,current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented,with an imaging modality-based approach and a keen eye on unsolved issues.The results are promising,but the road ahead for translation in clinical practice is rather long.
基金supported by the National Key Research and Development Plan of China (No. 2017YFC1309100)the National Natural Scientific Foundation of China (No. 81771912, 81901910, and 81701782)the Provincial Science and Technology Plan Project of Guangdong Province (No. 2017B020227012)
文摘Objective: To develop and validate a radiomics-based predictive risk score(RPRS) for preoperative prediction of lymph node(LN) metastasis in patients with resectable non-small cell lung cancer(NSCLC).Methods: We retrospectively analyzed 717 who underwent surgical resection for primary NSCLC with systematic mediastinal lymphadenectomy from October 2007 to July 2016. By using the method of radiomics analysis, 591 computed tomography(CT)-based radiomics features were extracted, and the radiomics-based classifier was constructed. Then, using multivariable logistic regression analysis, a weighted score RPRS was derived to identify LN metastasis. Apparent prediction performance of RPRS was assessed with its calibration,discrimination, and clinical usefulness.Results: The radiomics-based classifier was constructed, which consisted of 13 selected radiomics features.Multivariate models demonstrated that radiomics-based classifier, age group, tumor diameter, tumor location, and CT-based LN status were independent predictors. When we assigned the corresponding score to each variable,patients with RPRSs of 0-3, 4-5, 6, 7-8, and 9 had distinctly very low(0%-20%), low(21%-40%), intermediate(41%-60%), high(61%-80%), and very high(81%-100%) risks of LN involvement, respectively. The developed RPRS showed good discrimination and satisfactory calibration (C-index: 0.785, 95% confidence interval(95% CI):0.780-0.790)Additionally, RPRS outperformed the clinicopathologic-based characteristics model with net reclassification index(NRI) of 0.711(95% CI: 0.555-0.867).Conclusions: The novel clinical scoring system developed as RPRS can serve as an easy-to-use tool to facilitate the preoperatively individualized prediction of LN metastasis in patients with resectable NSCLC. This stratification of patients according to their LN status may provide a basis for individualized treatment.
