BACKGROUND The colon cancer prognosis is influenced by multiple factors,including clinical,pathological,and non-biological factors.However,only a few studies have focused on computed tomography(CT)imaging features.The...BACKGROUND The colon cancer prognosis is influenced by multiple factors,including clinical,pathological,and non-biological factors.However,only a few studies have focused on computed tomography(CT)imaging features.Therefore,this study aims to predict the prognosis of patients with colon cancer by combining CT imaging features with clinical and pathological characteristics,and establishes a nomogram to provide critical guidance for the individualized treatment.AIM To establish and validate a nomogram to predict the overall survival(OS)of patients with colon cancer.METHODS A retrospective analysis was conducted on the survival data of 249 patients with colon cancer confirmed by surgical pathology between January 2017 and December 2021.The patients were randomly divided into training and testing groups at a 1:1 ratio.Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors associated with OS,and a nomogram model was constructed for the training group.Survival curves were calculated using the Kaplan–Meier method.The concordance index(C-index)and calibration curve were used to evaluate the nomogram model in the training and testing groups.RESULTS Multivariate logistic regression analysis revealed that lymph node metastasis on CT,perineural invasion,and tumor classification were independent prognostic factors.A nomogram incorporating these variables was constructed,and the C-index of the training and testing groups was 0.804 and 0.692,respectively.The calibration curves demonstrated good consistency between the actual values and predicted probabilities of OS.CONCLUSION A nomogram combining CT imaging characteristics and clinicopathological factors exhibited good discrimination and reliability.It can aid clinicians in risk stratification and postoperative monitoring and provide important guidance for the individualized treatment of patients with colon cancer.展开更多
The mutation status of KRAS is a significant biomarker in the prognosis of rectal cancer.This study investigated the feasibility of MRI-based radiomics in predicting the mutation status of KRAS with a composite index ...The mutation status of KRAS is a significant biomarker in the prognosis of rectal cancer.This study investigated the feasibility of MRI-based radiomics in predicting the mutation status of KRAS with a composite index which could be an important criterion for KRAS mutation in clinical practice.In this retrospective study,a total of 127 patients with rectal cancer were enrolled.The 3D Slicer was used to extract the radiomics features from the MRI images,and sparse support vector machine(SVM)with linear kernel was applied for feature reduction.The radiomics classifier for predicting the KRAS status was then constructed by Linear Discriminant Analysis(LDA)and its performance was evaluated.The composite index was determined with LDA model.Out of 127 rectal cancer subjects,there were 44 KRAS mutation cases and 83 wild cases.A total of 104 radiomics features were extracted,54 features were filtered by linear SVM with L1-norm regularization and 6 features that had no significant correlations within them were finally selected.The radiomics classifier constructed using the 6 features featured an AUC value of 0.669(specificity:0.506;sensitivity:0.773)with LDA.Furthermore,the composite index(Radscore)had statistically significant difference between the KRAS mutation and wild groups.It is suggested that the MRI-based radiomics has the potential in predicting the KRAS status in patients with rectal cancer,which may enhance the diagnostic value of MRI in rectal cancer.展开更多
Through improving the redundant data filtering of unreliable data filter for radio frequency identification（RFID） with sliding-window,a data filter which integrates self-adaptive sliding-window and Euclidean distanc...Through improving the redundant data filtering of unreliable data filter for radio frequency identification（RFID） with sliding-window,a data filter which integrates self-adaptive sliding-window and Euclidean distance is proposed.The input data required being filtered have been shunt by considering a large number of redundant data existing in the unreliable data for RFID and the redundant data in RFID are the main filtering object with utilizing the filter based on Euclidean distance.The comparison between the results from the method proposed in this paper and previous research shows that it can improve the accuracy of the RFID for unreliable data filtering and largely reduce the redundant reading rate.展开更多
基金Supported by Cancer Research Program of National Cancer Center,No.NCC201917B05Special Research Fund Project of Biomedical Center of Hubei Cancer Hospital,No.2022SWZX06.
文摘BACKGROUND The colon cancer prognosis is influenced by multiple factors,including clinical,pathological,and non-biological factors.However,only a few studies have focused on computed tomography(CT)imaging features.Therefore,this study aims to predict the prognosis of patients with colon cancer by combining CT imaging features with clinical and pathological characteristics,and establishes a nomogram to provide critical guidance for the individualized treatment.AIM To establish and validate a nomogram to predict the overall survival(OS)of patients with colon cancer.METHODS A retrospective analysis was conducted on the survival data of 249 patients with colon cancer confirmed by surgical pathology between January 2017 and December 2021.The patients were randomly divided into training and testing groups at a 1:1 ratio.Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors associated with OS,and a nomogram model was constructed for the training group.Survival curves were calculated using the Kaplan–Meier method.The concordance index(C-index)and calibration curve were used to evaluate the nomogram model in the training and testing groups.RESULTS Multivariate logistic regression analysis revealed that lymph node metastasis on CT,perineural invasion,and tumor classification were independent prognostic factors.A nomogram incorporating these variables was constructed,and the C-index of the training and testing groups was 0.804 and 0.692,respectively.The calibration curves demonstrated good consistency between the actual values and predicted probabilities of OS.CONCLUSION A nomogram combining CT imaging characteristics and clinicopathological factors exhibited good discrimination and reliability.It can aid clinicians in risk stratification and postoperative monitoring and provide important guidance for the individualized treatment of patients with colon cancer.
文摘The mutation status of KRAS is a significant biomarker in the prognosis of rectal cancer.This study investigated the feasibility of MRI-based radiomics in predicting the mutation status of KRAS with a composite index which could be an important criterion for KRAS mutation in clinical practice.In this retrospective study,a total of 127 patients with rectal cancer were enrolled.The 3D Slicer was used to extract the radiomics features from the MRI images,and sparse support vector machine(SVM)with linear kernel was applied for feature reduction.The radiomics classifier for predicting the KRAS status was then constructed by Linear Discriminant Analysis(LDA)and its performance was evaluated.The composite index was determined with LDA model.Out of 127 rectal cancer subjects,there were 44 KRAS mutation cases and 83 wild cases.A total of 104 radiomics features were extracted,54 features were filtered by linear SVM with L1-norm regularization and 6 features that had no significant correlations within them were finally selected.The radiomics classifier constructed using the 6 features featured an AUC value of 0.669(specificity:0.506;sensitivity:0.773)with LDA.Furthermore,the composite index(Radscore)had statistically significant difference between the KRAS mutation and wild groups.It is suggested that the MRI-based radiomics has the potential in predicting the KRAS status in patients with rectal cancer,which may enhance the diagnostic value of MRI in rectal cancer.
基金supported by the foundation of Science and Technology Commission of Shanghai Municipality (Grant No.13521103902)
文摘Through improving the redundant data filtering of unreliable data filter for radio frequency identification（RFID） with sliding-window,a data filter which integrates self-adaptive sliding-window and Euclidean distance is proposed.The input data required being filtered have been shunt by considering a large number of redundant data existing in the unreliable data for RFID and the redundant data in RFID are the main filtering object with utilizing the filter based on Euclidean distance.The comparison between the results from the method proposed in this paper and previous research shows that it can improve the accuracy of the RFID for unreliable data filtering and largely reduce the redundant reading rate.
