Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion(PNI) in colorectal cancer(CRC).Methods: After computed tomography(CT) radiomics features extraction,...Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion(PNI) in colorectal cancer(CRC).Methods: After computed tomography(CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort(346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen(CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation(separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results: The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index(c-index): 0.817; 95% confidence interval(95% CI): 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination(c-index: 0.803; 95% CI: 0.794–0.812).Conclusions: Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.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
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
Objective: To predict preoperative staging using a radiomics approach based on computed tomography(CT)images of patients with esophageal squamous cell carcinoma(ESCC).Methods: This retrospective study included 154 pat...Objective: To predict preoperative staging using a radiomics approach based on computed tomography(CT)images of patients with esophageal squamous cell carcinoma(ESCC).Methods: This retrospective study included 154 patients(primary cohort: n=114; validation cohort: n=40) with pathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. High throughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomics signature was constructed using the least absolute shrinkage and selection operator(Lasso). Associations between radiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomics approach and tumor volume for discriminating between stages Ⅰ-Ⅱ and Ⅲ-Ⅳ was evaluated and compared using the receiver operating characteristics(ROC) curves and net reclassification improvement(NRI).Results: A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomics signature after feature dimension reduction. The radiomics signature was significantly associated with ESCC staging(P<0.001), and yielded a better performance for discrimination of early and advanced stage ESCC compared to tumor volume in both the primary [area under the receiver operating characteristic curve(AUC): 0.795 vs. 0.694,P=0.003; NRI=0.424)] and validation cohorts(AUC: 0.762 vs. 0.624, P=0.035; NRI=0.834).Conclusions: The quantitative approach has the potential to identify stage Ⅰ-Ⅱ and Ⅲ-Ⅳ ESCC before treatment.展开更多
In the last decade,the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering"sub-visual"...In the last decade,the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering"sub-visual"prognostic image cues from the histopathological image.While we are getting more knowledge and experience in digital pathology,the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic assay.In this paper,we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer prognosis.It includes:correlation of pathomics and genomics;fusion of pathomics and genomics;fusion of pathomics and radiomics.We also present challenges,potential opportunities,and avenues for future work.展开更多
Objective:To evaluate the human epidermal growth factor receptor 2(HER2)status in patients with breast cancer using multidetector computed tomography(MDCT)-based handcrafted and deep radiomics features.Methods:This re...Objective:To evaluate the human epidermal growth factor receptor 2(HER2)status in patients with breast cancer using multidetector computed tomography(MDCT)-based handcrafted and deep radiomics features.Methods:This retrospective study enrolled 339 female patients(primary cohort,n=177;validation cohort,n=162)with pathologically confirmed invasive breast cancer.Handcrafted and deep radiomics features were extracted from the MDCT images during the arterial phase.After the feature selection procedures,handcrafted and deep radiomics signatures and the combined model were built using multivariate logistic regression analysis.Performance was assessed by measures of discrimination,calibration,and clinical usefulness in the primary cohort and validated in the validation cohort.Results:The handcrafted radiomics signature had a discriminative ability with a C-index of 0.739[95%confidence interval(95%CI):0.661-0.818]in the primary cohort and 0.695(95%CI:0.609-0.781)in the validation cohort.The deep radiomics signature also had a discriminative ability with a C-index of 0.760(95%CI:0.690-0.831)in the primary cohort and 0.777(95%CI:0.696-0.857)in the validation cohort.The combined model,which incorporated both the handcrafted and deep radiomics signatures,showed good discriminative ability with a C-index of 0.829(95%CI:0.767-0.890)in the primary cohort and 0.809(95%CI:0.740-0.879)in the validation cohort.Conclusions:Handcrafted and deep radiomics features from MDCT images were associated with HER2 status in patients with breast cancer.Thus,these features could provide complementary aid for the radiological evaluation of HER2 status in breast cancer.展开更多
Objectives:To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma(GA).Methods:This retrospective study enrolled 592 patients with clinicopathologic...Objectives:To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma(GA).Methods:This retrospective study enrolled 592 patients with clinicopathologically confirmed GA(low-grade:n=154;high-grade:n=438)from January 2008 to March 2018 who were divided into training(n=450)and validation(n=142)sets according to the time of computed tomography(CT)examination.Radiomic features were extracted from the portal venous phase CT images.The Mann-Whitney U test and the least absolute shrinkage and selection operator(LASSO)regression model were used for feature selection,data dimension reduction and radiomics signature construction.Multivariable logistic regression analysis was applied to develop the prediction model.The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram.The performance of the nomogram was assessed with respect to its calibration and discrimination.Results:A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA(P<0.001 for both training and validation sets).A nomogram including the radiomics signature and tumor location as predictors was developed.The model showed both good calibration and good discrimination,in which C-index in the training set,0.752[95%confidence interval(95%CI):0.701-0.803];C-index in the validation set,0.793(95%CI:0.711-0.874).Conclusions:This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures,which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.展开更多
Objective:To develop and validate a radiomics prognostic scoring system(RPSS)for prediction of progressionfree survival(PFS)in patients with stageⅣnon-small cell lung cancer(NSCLC)treated with platinum-based chemothe...Objective:To develop and validate a radiomics prognostic scoring system(RPSS)for prediction of progressionfree survival(PFS)in patients with stageⅣnon-small cell lung cancer(NSCLC)treated with platinum-based chemotherapy.Methods:In this retrospective study,four independent cohorts of stageⅣNSCLC patients treated with platinum-based chemotherapy were included for model construction and validation(Discovery:n=159;Internal validation:n=156;External validation:n=81,Mutation validation:n=64).First,a total of 1,182 three-dimensional radiomics features were extracted from pre-treatment computed tomography(CT)images of each patient.Then,a radiomics signature was constructed using the least absolute shrinkage and selection operator method(LASSO)penalized Cox regression analysis.Finally,an individualized prognostic scoring system incorporating radiomics signature and clinicopathologic risk factors was proposed for PFS prediction.Results:The established radiomics signature consisting of 16 features showed good discrimination for classifying patients with high-risk and low-risk progression to chemotherapy in all cohorts(All P<0.05).On the multivariable analysis,independent factors for PFS were radiomics signature,performance status(PS),and N stage,which were all selected into construction of RPSS.The RPSS showed significant prognostic performance for predicting PFS in discovery[C-index:0.772,95%confidence interval(95%CI):0.765-0.779],internal validation(C-index:0.738,95%CI:0.730-0.746),external validation(C-index:0.750,95%CI:0.734-0.765),and mutation validation(Cindex:0.739,95%CI:0.720-0.758).Decision curve analysis revealed that RPSS significantly outperformed the clinicopathologic-based model in terms of clinical usefulness(All P<0.05).Conclusions:This study established a radiomics prognostic scoring system as RPSS that can be conveniently used to achieve individualized prediction of PFS probability for stageⅣNSCLC patients treated with platinumbased chemotherapy,which holds promise for guiding personalized pre-therapy of stageⅣNSCLC.展开更多
Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ...Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.Methods: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical usefulness.Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage Ⅰ-Ⅲ CRC patients.展开更多
Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer.However, there is still room for improvement in this scoring method to achieve further advances in its clini...Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer.However, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore(IS-mod) system for predicting overall survival(OS) in patients with stage Ⅰ-Ⅲ colon cancer.Methods: The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort(N=212) and validation cohort(N=103)from two centers were used to evaluate the prognostic value of the IS-mod.Results: In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS[adjusted hazard ratio(HR)=0.36, 95% confidence interval(95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like(IS-like) system(C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25%(C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation cohort.Conclusions: Our method simplifies the annotation and accelerates the calculation of Immunoscore method,thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set(N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.展开更多
Background:In colorectal cancer(CRC),mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype,morphology,and prognosis.However,mucinous components are present in a large number of adenocarcinomas,a...Background:In colorectal cancer(CRC),mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype,morphology,and prognosis.However,mucinous components are present in a large number of adenocarcinomas,and the prognostic value of mucus proportion has not been investigated.Artificial intelligence provides a way to quantify mucus proportion on whole-slide images(WSIs)accurately.We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts.Methods:Deep learning was used to segment WSIs stained with hematoxylin and eosin.Mucus-tumor ratio(MTR)was defined as the proportion of mucinous component in the tumor area.A training cohort(N=419)and a validation cohort(N=315)were used to evaluate the prognostic value of MTR.Survival analysis was performed using the Cox proportional hazard model.Result:Patients were stratified tomucus-low andmucus-high groups,with 24.1%as the threshold.In the training cohort,patients with mucus-high had unfavorable outcomes(hazard ratio for high vs.low 1.88,95%confidence interval 1.18–2.99,P=0.008),with 5-year overall survival rates of 54.8%and 73.7%in mucus-high and mucus-lowgroups,respectively.The resultswere confirmed in the validation cohort(2.09,1.21–3.60,0.008;62.8%vs.79.8%).The prognostic value of MTR was maintained in multivariate analysis for both cohorts.Conclusion:The deep learning quantified MTR was an independent prognostic factor in CRC.With the advantages of advanced efficiency and high consistency,our method is suitable for clinical application and promotes precision medicine development.展开更多
Background:Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings.We aim to investigate whether combining quantitative a...Background:Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings.We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging(MRI)features could differentiate anorectal malignant melanoma from low rectal cancer.Methods:Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent preoperative rectal MRI from three hospitals were retrospectively enrolled.All patients were divided into the primary cohort(N=84)and validation cohort(N=51).Quantitative image analysiswas performed on T1-weighted(T1WI),T2-weighted(T2WI),and contrast-enhanced T1-weighted imaging(CE-T1WI).The subjective qualitative MRI findings were evaluated by two radiologists in consensus.Multivariable analysis was performed using stepwise logistic regression.The discrimination performance was assessed by the area under the receiver operating characteristic curve(AUC)with a 95%confidence interval(CI).Results:The skewness derived from T2WI(T2WI-skewness)showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer(primary cohort:AUC=0.852,95%CI 0.788–0.916;validation cohort:0.730,0.645–0.815).Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors,and incorporating both factors achieved good discrimination performance in two cohorts(primary cohort:AUC=0.913,95%CI 0.868–0.958;validation cohort:0.902,0.844–0.960).Conclusions:Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer.The quantitative image analysis helps improve diagnostic accuracy.展开更多
Background:Artificial intelligence(AI)technology represented by deep learning has made remarkable achievements in digital pathology,enhancing the accuracy and reliability of diagnosis and prognosis evaluation.The spat...Background:Artificial intelligence(AI)technology represented by deep learning has made remarkable achievements in digital pathology,enhancing the accuracy and reliability of diagnosis and prognosis evaluation.The spatial distribution of CD3^(+)and CD8^(+)T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer(CRC).This study aimed to investigate CD3_(CT)(CD3^(+)T cells density in the core of the tumor[CT])prognostic ability in patients with CRC by using AI technology.Methods:The study involved the enrollment of 492 patients from two distinct medical centers,with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort.To facilitate tissue segmentation and T-cells quantification in whole-slide images(WSIs),a fully automated workflow based on deep learning was devised.Upon the completion of tissue segmentation and subsequent cell segmentation,a comprehensive analysis was conducted.Results:The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3_(CT) and CD3-CD8(the combination of CD3^(+)and CD8^(+)T cells density within the CT and invasive margin)in predicting mortality(C-index in training cohort:0.65 vs.0.64;validation cohort:0.69 vs.0.69).The CD3_(CT) was confirmed as an independent prognostic factor,with high CD3_(CT) density associated with increased overall survival(OS)in the training cohort(hazard ratio[HR]=0.22,95%confidence interval[CI]:0.12–0.38,P<0.001)and validation cohort(HR=0.21,95%CI:0.05–0.92,P=0.037).Conclusions:We quantify the spatial distribution of CD3^(+)and CD8^(+)T cells within tissue regions in WSIs using AI technology.The CD3_(CT) confirmed as a stage-independent predictor for OS in CRC patients.Moreover,CD3_(CT) shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.展开更多
基金supported by the National Key Research and Development Program of China (No. 2017YFC1309100)the National Natural Scientific Foundation of China (No. 81771912, 81701782 and 81601469)
文摘Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion(PNI) in colorectal cancer(CRC).Methods: After computed tomography(CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort(346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen(CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation(separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results: The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index(c-index): 0.817; 95% confidence interval(95% CI): 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination(c-index: 0.803; 95% CI: 0.794–0.812).Conclusions: Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.
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.
基金supported by the National Key R&D Program of China (No. 2017YFC1309100)National Natural Scientific Foundation of China (No. 81771912)Science and Technology Planning Project of Guangdong Province (No. 2017B020227012)
文摘Objective: To predict preoperative staging using a radiomics approach based on computed tomography(CT)images of patients with esophageal squamous cell carcinoma(ESCC).Methods: This retrospective study included 154 patients(primary cohort: n=114; validation cohort: n=40) with pathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. High throughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomics signature was constructed using the least absolute shrinkage and selection operator(Lasso). Associations between radiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomics approach and tumor volume for discriminating between stages Ⅰ-Ⅱ and Ⅲ-Ⅳ was evaluated and compared using the receiver operating characteristics(ROC) curves and net reclassification improvement(NRI).Results: A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomics signature after feature dimension reduction. The radiomics signature was significantly associated with ESCC staging(P<0.001), and yielded a better performance for discrimination of early and advanced stage ESCC compared to tumor volume in both the primary [area under the receiver operating characteristic curve(AUC): 0.795 vs. 0.694,P=0.003; NRI=0.424)] and validation cohorts(AUC: 0.762 vs. 0.624, P=0.035; NRI=0.834).Conclusions: The quantitative approach has the potential to identify stage Ⅰ-Ⅱ and Ⅲ-Ⅳ ESCC before treatment.
基金supported by the DoD Breast Cancer Research Program Breakthrough Level 1 Award W81XWH-19-1-0668,NIH-NCI R21 CA253108-01DoD Prostate Cancer Research Program Idea Development Award W81XWH-18-1-0524+2 种基金Key R&D Program of Guangdong Province,China(No.2021B0101420006)National Science Fund for Distinguished Young Scholars,China(No.81925023)National Natural Science Foundation of China(No.62002082,62102103,61906050,81771912)。
文摘In the last decade,the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering"sub-visual"prognostic image cues from the histopathological image.While we are getting more knowledge and experience in digital pathology,the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic assay.In this paper,we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer prognosis.It includes:correlation of pathomics and genomics;fusion of pathomics and genomics;fusion of pathomics and radiomics.We also present challenges,potential opportunities,and avenues for future work.
基金supported by the National Key R&D Program of China(No.2017YFC1309100)the National Science Fund for Distinguished Young Scholars(No.81925023)+1 种基金the National Natural Science Foundation of China(No.81771912,81701662,81701782,81601469,and 81702322)Science and Technology Planning Project of Guangdong Province(No.2017B020227012)。
文摘Objective:To evaluate the human epidermal growth factor receptor 2(HER2)status in patients with breast cancer using multidetector computed tomography(MDCT)-based handcrafted and deep radiomics features.Methods:This retrospective study enrolled 339 female patients(primary cohort,n=177;validation cohort,n=162)with pathologically confirmed invasive breast cancer.Handcrafted and deep radiomics features were extracted from the MDCT images during the arterial phase.After the feature selection procedures,handcrafted and deep radiomics signatures and the combined model were built using multivariate logistic regression analysis.Performance was assessed by measures of discrimination,calibration,and clinical usefulness in the primary cohort and validated in the validation cohort.Results:The handcrafted radiomics signature had a discriminative ability with a C-index of 0.739[95%confidence interval(95%CI):0.661-0.818]in the primary cohort and 0.695(95%CI:0.609-0.781)in the validation cohort.The deep radiomics signature also had a discriminative ability with a C-index of 0.760(95%CI:0.690-0.831)in the primary cohort and 0.777(95%CI:0.696-0.857)in the validation cohort.The combined model,which incorporated both the handcrafted and deep radiomics signatures,showed good discriminative ability with a C-index of 0.829(95%CI:0.767-0.890)in the primary cohort and 0.809(95%CI:0.740-0.879)in the validation cohort.Conclusions:Handcrafted and deep radiomics features from MDCT images were associated with HER2 status in patients with breast cancer.Thus,these features could provide complementary aid for the radiological evaluation of HER2 status in breast cancer.
基金supported by the National Key Research and Development Program of China(No.2017YFC 1309100)the National Science Fund for Distinguished Young Scholars(No.81925023)the National Natural Science Foundation of China(No.82071892,81771912,81901910)。
文摘Objectives:To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma(GA).Methods:This retrospective study enrolled 592 patients with clinicopathologically confirmed GA(low-grade:n=154;high-grade:n=438)from January 2008 to March 2018 who were divided into training(n=450)and validation(n=142)sets according to the time of computed tomography(CT)examination.Radiomic features were extracted from the portal venous phase CT images.The Mann-Whitney U test and the least absolute shrinkage and selection operator(LASSO)regression model were used for feature selection,data dimension reduction and radiomics signature construction.Multivariable logistic regression analysis was applied to develop the prediction model.The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram.The performance of the nomogram was assessed with respect to its calibration and discrimination.Results:A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA(P<0.001 for both training and validation sets).A nomogram including the radiomics signature and tumor location as predictors was developed.The model showed both good calibration and good discrimination,in which C-index in the training set,0.752[95%confidence interval(95%CI):0.701-0.803];C-index in the validation set,0.793(95%CI:0.711-0.874).Conclusions:This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures,which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.
基金supported by the National Key Research and Development Plan of China(No.2017YFC1309100)the National Science Fund for Distinguished Young Scholars(No.81925023)the National Natural Scientific Foundation of China(No.81771912,81901910,82072090,and 82001986)。
文摘Objective:To develop and validate a radiomics prognostic scoring system(RPSS)for prediction of progressionfree survival(PFS)in patients with stageⅣnon-small cell lung cancer(NSCLC)treated with platinum-based chemotherapy.Methods:In this retrospective study,four independent cohorts of stageⅣNSCLC patients treated with platinum-based chemotherapy were included for model construction and validation(Discovery:n=159;Internal validation:n=156;External validation:n=81,Mutation validation:n=64).First,a total of 1,182 three-dimensional radiomics features were extracted from pre-treatment computed tomography(CT)images of each patient.Then,a radiomics signature was constructed using the least absolute shrinkage and selection operator method(LASSO)penalized Cox regression analysis.Finally,an individualized prognostic scoring system incorporating radiomics signature and clinicopathologic risk factors was proposed for PFS prediction.Results:The established radiomics signature consisting of 16 features showed good discrimination for classifying patients with high-risk and low-risk progression to chemotherapy in all cohorts(All P<0.05).On the multivariable analysis,independent factors for PFS were radiomics signature,performance status(PS),and N stage,which were all selected into construction of RPSS.The RPSS showed significant prognostic performance for predicting PFS in discovery[C-index:0.772,95%confidence interval(95%CI):0.765-0.779],internal validation(C-index:0.738,95%CI:0.730-0.746),external validation(C-index:0.750,95%CI:0.734-0.765),and mutation validation(Cindex:0.739,95%CI:0.720-0.758).Decision curve analysis revealed that RPSS significantly outperformed the clinicopathologic-based model in terms of clinical usefulness(All P<0.05).Conclusions:This study established a radiomics prognostic scoring system as RPSS that can be conveniently used to achieve individualized prediction of PFS probability for stageⅣNSCLC patients treated with platinumbased chemotherapy,which holds promise for guiding personalized pre-therapy of stageⅣNSCLC.
基金supported by the National Key R&D Program of China (No. 2021YFF1201003)the Key R&D Program of Guangdong Province, China (No. 2021B0101420006)+2 种基金the National Science Fund for Distinguished Young Scholars (No. 81925023 and 82071892)the National Natural Science Foundation of China (No. 81771912 and 82071892)the National Natural Science Foundation for Young Scientists of China (No. 81701782 and 81901910).
文摘Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.Methods: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical usefulness.Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage Ⅰ-Ⅲ CRC patients.
基金supported by the National Key Research and Development Program of China(No.2017YFC1309102)National Natural Science Foundation of China(No.81771912,No.82001986,No.82071892)+1 种基金National Science Fund for Distinguished Young Scholars(No.81925023)High-level Hospital Construction Project(No.DFJH201805 and No.DFJH201914)。
文摘Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer.However, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore(IS-mod) system for predicting overall survival(OS) in patients with stage Ⅰ-Ⅲ colon cancer.Methods: The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort(N=212) and validation cohort(N=103)from two centers were used to evaluate the prognostic value of the IS-mod.Results: In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS[adjusted hazard ratio(HR)=0.36, 95% confidence interval(95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like(IS-like) system(C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25%(C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation cohort.Conclusions: Our method simplifies the annotation and accelerates the calculation of Immunoscore method,thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set(N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.
基金This work was supported by the National Key Research and Development Program of China(grant No.2017YFC1309102)the National Science Fund for Distinguished Young Scholars(grant No.81925023)+1 种基金the National Natural Science Foundation of China(grant Nos.81771912,81701782,81702322,82001986,and 82071892)the High-level Hospital Construction Project(grant Nos.DFJH201805 and DFJH201914).
文摘Background:In colorectal cancer(CRC),mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype,morphology,and prognosis.However,mucinous components are present in a large number of adenocarcinomas,and the prognostic value of mucus proportion has not been investigated.Artificial intelligence provides a way to quantify mucus proportion on whole-slide images(WSIs)accurately.We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts.Methods:Deep learning was used to segment WSIs stained with hematoxylin and eosin.Mucus-tumor ratio(MTR)was defined as the proportion of mucinous component in the tumor area.A training cohort(N=419)and a validation cohort(N=315)were used to evaluate the prognostic value of MTR.Survival analysis was performed using the Cox proportional hazard model.Result:Patients were stratified tomucus-low andmucus-high groups,with 24.1%as the threshold.In the training cohort,patients with mucus-high had unfavorable outcomes(hazard ratio for high vs.low 1.88,95%confidence interval 1.18–2.99,P=0.008),with 5-year overall survival rates of 54.8%and 73.7%in mucus-high and mucus-lowgroups,respectively.The resultswere confirmed in the validation cohort(2.09,1.21–3.60,0.008;62.8%vs.79.8%).The prognostic value of MTR was maintained in multivariate analysis for both cohorts.Conclusion:The deep learning quantified MTR was an independent prognostic factor in CRC.With the advantages of advanced efficiency and high consistency,our method is suitable for clinical application and promotes precision medicine development.
基金This work was supported by the National Key Research and Development Program of China(Grant No.2017YFC1309100)the National Science Fund for Distinguished Young Scholars(Grant No.81925023)+1 种基金the National Natural Science Foundation of China(Grants No.81771912,82071892,and 82072090)the High-level Hospital Construction Project(Grant No.DFJH201805).
文摘Background:Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings.We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging(MRI)features could differentiate anorectal malignant melanoma from low rectal cancer.Methods:Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent preoperative rectal MRI from three hospitals were retrospectively enrolled.All patients were divided into the primary cohort(N=84)and validation cohort(N=51).Quantitative image analysiswas performed on T1-weighted(T1WI),T2-weighted(T2WI),and contrast-enhanced T1-weighted imaging(CE-T1WI).The subjective qualitative MRI findings were evaluated by two radiologists in consensus.Multivariable analysis was performed using stepwise logistic regression.The discrimination performance was assessed by the area under the receiver operating characteristic curve(AUC)with a 95%confidence interval(CI).Results:The skewness derived from T2WI(T2WI-skewness)showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer(primary cohort:AUC=0.852,95%CI 0.788–0.916;validation cohort:0.730,0.645–0.815).Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors,and incorporating both factors achieved good discrimination performance in two cohorts(primary cohort:AUC=0.913,95%CI 0.868–0.958;validation cohort:0.902,0.844–0.960).Conclusions:Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer.The quantitative image analysis helps improve diagnostic accuracy.
基金supported by grants from the National Key R&D Program of China(No.2021YFF1201003)the National Science Fund for Distinguished Young Scholars(No.81925023)+3 种基金the Key-Area Research and Development Program of Guangdong Province(No.2021B0101420006)the Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application(No.2022B1212010011)the High-level Hospital Construction Project(No.DFJHBF202105)the National Science Foundation for Young Scientists of China(No.82001986)
文摘Background:Artificial intelligence(AI)technology represented by deep learning has made remarkable achievements in digital pathology,enhancing the accuracy and reliability of diagnosis and prognosis evaluation.The spatial distribution of CD3^(+)and CD8^(+)T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer(CRC).This study aimed to investigate CD3_(CT)(CD3^(+)T cells density in the core of the tumor[CT])prognostic ability in patients with CRC by using AI technology.Methods:The study involved the enrollment of 492 patients from two distinct medical centers,with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort.To facilitate tissue segmentation and T-cells quantification in whole-slide images(WSIs),a fully automated workflow based on deep learning was devised.Upon the completion of tissue segmentation and subsequent cell segmentation,a comprehensive analysis was conducted.Results:The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3_(CT) and CD3-CD8(the combination of CD3^(+)and CD8^(+)T cells density within the CT and invasive margin)in predicting mortality(C-index in training cohort:0.65 vs.0.64;validation cohort:0.69 vs.0.69).The CD3_(CT) was confirmed as an independent prognostic factor,with high CD3_(CT) density associated with increased overall survival(OS)in the training cohort(hazard ratio[HR]=0.22,95%confidence interval[CI]:0.12–0.38,P<0.001)and validation cohort(HR=0.21,95%CI:0.05–0.92,P=0.037).Conclusions:We quantify the spatial distribution of CD3^(+)and CD8^(+)T cells within tissue regions in WSIs using AI technology.The CD3_(CT) confirmed as a stage-independent predictor for OS in CRC patients.Moreover,CD3_(CT) shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.
