Mp36-04 Deep Learning-Based Multi-Model Prediction for Disease-Free Survival Status of Patients With Clear Cell Renal Cell Carcinoma: A Multicenter Study

You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance I (MP36)1 May 2024MP36-04 DEEP LEARNING-BASED MULTI-MODEL PREDICTION FOR DISEASE-FREE SURVIVAL STATUS OF PATIENTS WITH CLEAR CELL RENAL CELL CARCINOMA: A MULTICENTER STUDY Siteng Chen and Junhua Zheng Siteng ChenSiteng Chen and Junhua ZhengJunhua Zheng View All Author Informationhttps://doi.org/10.1097/01.JU.0001008612.93052.9d.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature are needed to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery. METHODS: A total of 414 patients with whole slide images, CT images, and clinical data from three medical centers were retrospectively analyzed. We performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and radiomics on CT images, respectively. A multi-model prediction signature for disease-free survival were further developed by combining three single-modality prediction models and tumor stage/grade system. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. RESULTS: Single-modality prediction models performed well in predicting the disease-free survival status of ccRCC. The multi-model prediction signature (MMPS) achieved higher area under the curve value of 0.742, 0.917, and 0.900 in three independent patient cohorts, respectively. MMPS could distinguish patients with worse disease-free survival, with HR of 12.90 (95% CI: 2.443-68.120, p<0.0001), 11.10 (95% CI: 5.467-22.520, p<0.0001), and 8.27 (95% CI: 1.482-46.130, p<0.0001) in three different patient cohorts. In addition, MMPS outperformed single-modality prediction models and current clinical prognostic factors, which could also provide complements to current risk stratification for adjuvant therapy of ccRCC. CONCLUSIONS: Our novel multi-model prediction analysis for disease-free survival exhibited significant improvements in prognostic prediction for patients with ccRCC. After further validation in multiple centers and regions, the multimodal system could be a potential practical tool for clinicians in the treatment for ccRCC patients. Download PPTDownload PPT Source of Funding: This study was supported by the National Natural Science Foundation of China (81972393) © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e592 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Siteng Chen More articles by this author Junhua Zheng More articles by this author Expand All Advertisement PDF downloadLoading ...