Applied Research On Predicting Pathological Grading And Postoperative Recurrence Risk Of Bladder Cancer Based On Radiomics Features Of Multi-Phase CT Images

Part1:Radiomics prediction of the pathological grade of bladder cancer based on multi-phase CT imagesObjective:To explore the value of the radiomics based on multi-phase CT images combined with clinical risk factors in predicting of the pathological grade of bladder cancer(BCa).Methods:Patients with BCa who underwent CT scan and surgical treatment at the First Affiliated Hospital of Soochow University from January 1,2019 to December 31,2021 were analyzed retrospectively,with 104 cases of high-grade BCa and 100 cases of low-grade BCa included.Radiomics features were extracted from tumor volume in the images of the plain scan,arterial phase,and venous phase,respectively.Logistic Regression model,SVM model,and Random Forest model were established,and the model with higher diagnostic efficiency was chosen.Additionally,a radiomics-clinical model was conducted by selected independent predictors according to logistic regression analysis.Then the performance of the model was assessed.Results:Among the 204 patients enrolled,the training cohort was consisted of 142 patients and the validation cohort was made up of 62 patients.The Logistic Regression model was proved to be the most effective one among the three models.The radiomics-clinical model consisted of 2 independent predictors,patient age and Rad-Score,with an AUC of 0.904(95%CI 0.857-0.951)and 0.906(95%CI 0.837-0.975)in the training and validation cohorts,respectively.The diagnostic accuracy,sensitivity,and specificity of the validation cohort were 0.790,0.813,and 0.767 respectively.Conclusion:Combining with radiomics based on multi-phase CT images and clinical risk factors,the radiomics-clinical model possessed great potential in predicting the pathological grade of BCa.Part2:Feasibility study on predicting recurrence risk of bladder cancer based on radiomics features of multi-phase CT imagesObjective:To explore the radiomics based on multi-phase CT images combined with clinical risk factors,and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer(BCa)within 2 years after surgery.Methods:Patients with BCa who underwent surgical treatment at the the First Affiliated Hospital of Soochow University from January 1,2016 to December 31,2019 were retrospectively included and followed up to record the disease recurrence.A total of 183 patients were included in the study,and they were randomly divided into training cohort and validation cohort in a ratio of 7:3.The three basic models which were plain scan,arterial phase and venous phase as well as two combination models,namely,arterial phase+venous phase and plain scan+arterial phase+venous phase,were built with the Logistic Regression model,and we selected the model with higher performance and calculated the Rad-score(radiomics score)of each patient.The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors,then the radiomics-clinical model was constructed,and their performance was evaluated.Results:Of the 183 patients included,128 patients constituted the training cohort and 55 patients constituted the validation cohort.In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors,tumor grade,and Rad-score—the AUCs of the training cohort and validation cohort were 0.813(95%CI 0.740-0.886)and 0.838(95%CI 0.733-0.943),respectively.In the validation cohort,the diagnostic accuracy,sensitivity,and specificity were 0.727,0.739,and 0.719,respectively.Conclusion:Combining with radiomics based on multi-phase CT images and clinical risk factors,the radiomics-clinical model constructed to predict the recurrence risk of BCa within 2 years after surgery had a good performance.