Causal multi-modal AI for personalized chemosensitivity prediction

2026-09-11

AI Summary

本文通过开发一个基于常规病理和临床信息的因果多模态AI模型，解决了乳腺癌化疗敏感性个性化预测的问题，以减少过度化疗。

Abstract

Chemotherapy improves survival for some patients with breast cancer, but doctors cannot reliably predict who. Current guidelines rely on recurrence scores as a proxy for treatment benefit, which may contribute to the overprescription of chemotherapy. Here we present a causal multi-modal AI model that predicts personalized chemosensitivity using routinely collected pathology and clinical information. We developed our model on a multi-national dataset of 9,141 patients (twelve cohorts, nine countries) and evaluated it on another 1,994 patients (five cohorts, three countries). The model generated treatment-specific recurrence probabilities for each patient, with near-perfect calibration and strong prognostic discrimination across both 5- and 10-year follow-up horizons. Moreover, its chemotherapy benefit predictions demonstrated robust predictive performance, and out-performed existing recurrence-score-based tests. Compared to the standard of care, using the model to support personally tailored therapeutic decisions could reduce the number of patients receiving chemotherapy by 30% while achieving the same recurrence-free rate. Tumors predicted to be highly chemosensitive displayed concordant molecular and morphological programs of proliferation, cell cycle progression, and replication stress. The model's predictive capabilities transferred zero-shot to non-breast cancers, indicating our causal multi-modal AI approach may provide a universal strategy to predict treatment outcomes across cancer types.

Problem

• chemotherapy
• breast cancer
• personalized chemosensitivity
• recurrence scores
• overprescription

Innovation

• causal multi-modal AI
• personalized chemosensitivity prediction
• zero-shot transfer
• treatment-specific recurrence probabilities
• robust predictive performance