AUTHOR=Qian Jing , Yang Ling , Hu Su , Gu Siqian , Ye Juan , Li Zhenkai , Du Hongdi , Shen Hailin 

TITLE=Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.899897

DOI=10.3389/fonc.2022.899897

ISSN=2234-943X

ABSTRACT=Background: Predicting recurrence risk of bladder cancer is crucial for individualized clinical treatment of patients with bladder cancer. 
Objective: To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery. 
Methods: Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 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 group and validation group in a ratio of 7: 3. The three basic models of plain scan, corticomedullary phase and parenchymal phase, as well as two combination models including corticomedullary phase + parenchymal phase and plain scan + corticomedullary stage + parenchyma stage were built with the Logistic Regression algorithm, 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 model 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 group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by 3 independent risk factors of number of tumors, tumor grade and Rad-score, the AUC of the training group and validation group were 0.813 (95%CI 0.740-0.886) and 0.838 (95%CI 0.733-0.943) respectively. In the validation group, the diagnostic accuracy, sensitivity and specificity are 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 bladder cancer within 2 years after surgery have a good performance.