蚂蚁阿福与河北肿瘤医院在癌症领域取得研究突破：用AI提前预测胃癌术后风险

TL;DR - Ant Group’s AQ healthcare AI team and Hebei Cancer Hospital developed three multimodal models for predicting complications, early recurrence, and liver metastasis across the gastric-cancer treatment journey. The results suggest AI can improve risk stratification beyond conventional TNM staging, though randomized trials are still needed before clinical adoption.

• DeepComp combines contrast-enhanced CT, body-composition measurements, and clinical data; across 5,237 patients from 11 centers, its AUC was 0.888 internally and 0.824–0.869 in nine external cohorts.
• With DeepComp assistance, 10 surgeons’ average sensitivity for identifying patients at risk of moderate-to-severe postoperative complications rose from 47.1% to 87.9%.
• The pathology-based RSA model predicted early recurrence with AUCs of 0.843–0.887, while multimodal RCSA predicted liver metastasis with AUCs of 0.862–0.909.
• The evidence is primarily retrospective; a randomized controlled trial of DeepComp is being launched to determine whether model-guided care improves patient outcomes.