A Novel Nomogram Combined With Radiomics Features, Age and Albuminuria to Predict the Pathological Grade of Bladder Cancer Running Title: Application Study on the Prediction Model of Bladder Cancer Pathological Grade Based on Parameters of Thin-layer Enhanced CT. Author Information

Abstract Background: Based on multi-parameter thin-slice enhanced CT texture features and related clinical indicators, a preoperative pathological grade prediction model of bladder urothelial carcinoma was established.Methods: The CT images and clinical data of 372 patients with urothelial carcinoma in our hospital from January 2015 to October 2020 were collected. 372 patients were divided into two groups: HGUC(n=190) and LGUC(n=182). All patients were divided into 10 groups on average, of which 7 were used as training group (n=259) and the remaining 3 as verification group (n=113). Then, by using 3D-Slicer software from all enhanced in patients with preoperative CT images (Arterial and Venous phase calibration chart) split out the region of interest (ROI), respectively from the tumor image data extraction based on First-order and Second-order, High-order and filtering characteristics of 1223 texture features, and use the inter/intra-class correlation coefficient(ICC > 0.75) between classes and least absolute shrinkage selection operator (LASSO) regression feature selection; Secondly, the clinical effective factors were obtained by logistic regression analysis, and the clinical predictive model was constructed. Finally, the selected clinical key indicators and radiomics features were mapped. In order to verify the predictive ability of the nomogram, conformance index (C-index), calibration curve, Receiver operator characteristic (ROC) curve and clinical decision curve analysis (DCA) were used to test the nomogram.Results: Lasso regression analysis showed that 11 radiomics features were significantly correlated with the pathological grade of bladder cancer. After comparing the four models, it is found that Logistic regression model has the best prediction ability (AUC=0.858). The results of multivariate analysis showed that age and albuminuria were independent influencing factors. A comprehensive model for predicting the pathological grade of bladder cancer (radiomics + clinical) was constructed by combining clinical independent risk factors with 11 radiomics features. Compared with clinical feature model and radiomics model, it was found that the predictive performance of imaging comprehensive model combined with clinical factors was the best (AUC=0.864).Conclusions: The radiomics model based on multi-parameter thin-layer enhanced CT, combined with clinical factors, can effectively predict high-and low-grade urothelial carcinoma.