Deep Learning-Based Multi-Cancer Analysis for Predicting Disease-Free Survival Across Multiple Cancer Types.

Background: Artificial intelligence-derived parameters hold substantial promise as indicators for tumor prognosis prediction and treatment guidance. However, existing studies have not sufficiently addressed the application of these parameters across different cancer types. Methods: We employed a deep learning algorithm to conduct a multi-cancer analysis for disease-free survival (MC-DFS) prediction using 8856 cases with associated whole-slide images and clinical data. The training cohort consisted of 7392 cases from the TCGA set (24 cancer types), and the independent external validation cohort comprised 1464 cases from the CPTAC and General Hospital sets (9 cancer types). A nomogram prediction signature for disease-free survival (NOMO) was developed by integrating the MC-DFS, tumor stage, and patient age. The prognostic model's performance was validated in an independent cohort. Results: In the training and validation cohorts, the MC-DFS model achieved area under the curve (AUC) values of 0.750 and 0.682, respectively. It effectively differentiated patients with poorer disease-free survival, with hazard ratios of 4.823 (95% CI: 4.343-5.356, p < 0.0001) in the training cohort and 2.092 (95% CI: 1.472-2.971, p < 0.0001) in the validation cohort. Each cancer subtype's analysis confirmed the model's robust performance. Additionally, using nomogram analysis, we developed a multi-model prediction signature for disease-free survival across multiple cancer types based on MC-DFS and the clinicopathologic features in the training cohort. This enhanced model offers more precise risk stratification for stage I malignancies and complements the existing tumor staging systems by identifying high-risk patients. Conclusions: The newly developed MC-DFS shows marked improvements in prognostic predictions across multiple cancer types. With further validations across multiple centers, this nomogram prediction system could become a valuable practical tool for managing various cancers.