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Woznicki, Piotr; Laqua, Fabian Christopher; Messmer, Katharina; Kunz, Wolfgang Gerhard; Stief, Christian; Nörenberg, Dominik; Schreier, Andrea; Wojcik, Jan; Rübenthaler, Johannes; Ingrisch, Michael; Ricke, Jens; Buchner, Alexander; Schulz, Gerald Bastian and Gresser, Eva (2022): Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy. In: Cancers, Vol. 14, No. 18 [PDF, 1MB]


Simple Summary Accurate prognostic assessment of bladder cancer patients is essential for risk stratification, individualized therapeutic decision-making and follow-up management. In this study, the potential of quantitative features from preoperative CT images (radiomics features) to predict overall survival in patients treated with radical cystectomy was investigated. Both bladder tumors and pelvic lymph nodes as well as their immediate surroundings were segmented and analyzed. Regression models based on radiomics and clinical parameters were developed and compared. The combination of radiomics features from all regions with clinical parameters achieved the best results with a mean area under the ROC curve of 0.785 integrated over 1 to 7 years after radical cystectomy. Furthermore, the combined model stratified patients into high- and low-risk groups with significantly different outcomes. Therefore, the prognostic information from preoperative CT images could aid in the early stratification of patients with bladder cancer even before RC is conducted and could complement the well-established clinical factors. (1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48-74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657-0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617-0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.

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