Multitask Deep Learning Model for Predicting Lymph Node Metastasis and Disease-Free Survival From Cytology Images in Bladder Cancer: A Multicenter Study With Pan-Cytology Validation.

Accurate prediction of lymph node metastasis (LNM) and survival outcomes is crucial for personalized treatment in bladder cancer, yet remains clinically challenging. This study aimed to develop a Cytology-based multitask predictive Network (CytoNet) to simultaneously predict LNM and disease-free survival (DFS) in bladder cancer, and to evaluate its generalizability in cervical and thyroid cancers. This study included a multicenter cohort of 5273 patients, with bladder cancer (n = 1134), cervical cancer (n = 598), and thyroid cancer (n = 3541). CytoNet was constructed through a three-stage pipeline comprising self-supervised pretraining, supervised fine-tuning, and multitask prediction. In bladder cancer, CytoNet achieved an area under the curve (AUC) of 0.831-0.881 for LNM prediction and a C index of 0.680-0.710 for DFS prediction across all cohorts, outperforming single-task models and standard radiological assessments. In cervical cancer, CytoNet demonstrated robust performance for both LNM prediction (AUC 0.847-0.862) and DFS prediction (C index 0.690-0.709), while in thyroid cancer, it achieved an AUC of 0.819-0.855 for LNM prediction. In conclusion, CytoNet represents a reliable tool for predicting LNM and prognosis from cytopathology images across multiple cancer types, showing potential as a valuable adjunct to current clinical decision-making. Further prospective validation is warranted to assess its clinical utility.