发表机构
Ataraxis AI; University of Aberdeen; H. Lee Moffitt Cancer Center and Research Institute; University of Pittsburgh; Magee-Womens Hospital of UPMC; Roswell Park Comprehensive Cancer Center; Providence Genomics; Providence Cancer Institute; Centre Léon Bérard; Unicancer（Ataraxis AI; 阿伯丁大学; H. Lee Moffitt癌症中心与研究所; 匹兹堡大学; UPMC马吉妇女医院; 罗斯威尔公园综合癌症中心; 普罗维登斯基因组学; 普罗维登斯癌症研究所; 莱昂·贝拉尔中心; 法国癌症联盟）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出因果多模态AI模型，利用病理和临床信息预测乳腺癌患者个性化化疗敏感性，优于现有复发评分方法，可减少30%化疗人数且保持相同疗效，并零样本泛化至其他癌症。
AI 中文摘要
化疗可提高部分乳腺癌患者的生存率，但医生无法可靠地预测哪些患者会受益。目前的指南依赖于复发评分作为治疗获益的替代指标，这可能导致化疗的过度处方。在此，我们提出一种因果多模态人工智能模型，利用常规收集的病理和临床信息预测个性化化疗敏感性。我们在一个包含9,141名患者（十二个队列，九个国家）的多国数据集上开发了该模型，并在另外1,994名患者（五个队列，三个国家）上进行了评估。该模型为每位患者生成治疗特异性复发概率，在5年和10年随访期内均具有近乎完美的校准和强大的预后区分能力。此外，其化疗获益预测表现出稳健的预测性能，并优于现有的基于复发评分的检测方法。与标准治疗相比，使用该模型支持个性化治疗决策可将接受化疗的患者数量减少30%，同时达到相同的无复发率。被预测为高度化疗敏感的肿瘤显示出增殖、细胞周期进程和复制应激的协调一致的分子和形态学程序。该模型的预测能力可零样本迁移至非乳腺癌，表明我们的因果多模态人工智能方法可能提供一种跨癌症类型预测治疗结果的通用策略。
英文摘要
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.
