Multitask Deep Learning Model for Predicting Lymph Node Metastasis and Disease-Free Survival From Cytology Images in...

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.