145-TORCH: AI Prediction of Cancer Origin from Cytology

This article details the development and validation of TORCH, a deep-learning model designed to predict the tumor origin in cancers of unknown primary (CUP) using cytological images from serous effusions (hydrothorax and ascites). The study leveraged a massive dataset of over 57,000 cases to train this artificial intelligence tool, which performed robustly across multiple internal and external testing sets, achieving high Area Under the Receiver Operating Curve (AUROC) values for both cancer diagnosis and origin localization. Notably, TORCH significantly outperformed human pathologists in prediction accuracy and dramatically improved junior pathologists' diagnostic scores when used as an ancillary tool. Furthermore, the research established a link between treatment concordance based on TORCH's predictions and improved overall patient survival, underscoring the model’s potential value in clinical practice for personalized CUP management.

References:

• Tian F, Liu D, Wei N, et al. Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning[J]. Nature Medicine, 2024, 30(5): 1309-1319.