支柱-0：放射学基础模型的新前沿

摘要

尽管放射学在现代医学中扮演着关键角色，但影像检查量的增长速度已远超放射科医师队伍的增长。基础模型为辅助完成各类放射学任务提供了可行路径，但现有医学模型仍存在局限：将三维CT和MRI视为低分辨率二维切片处理、丢弃关键灰度对比信息，且缺乏反映真实临床场景的评估框架。我们推出Pillar-0放射学基础模型——基于某大型学术中心的42,990例盆腹部CT、86,411例胸部CT、14,348例头部CT及11,543例乳腺MRI预训练而成，同时提出RATE框架，能利用大语言模型以近100%准确率提取366种放射学征象的结构化标签。在包含14,230例盆腹部CT、10,646例胸部CT、4,906例头部CT及1,585例乳腺MRI的内部测试集上，Pillar-0创下性能新纪录，平均AUROC分别达86.4、88.0、90.1和82.9，以7.8-15.8个AUROC分值优势超越MedGemma（谷歌）、MedImageInsight（微软）、灵枢（阿里巴巴）及Merlin（斯坦福），并在87.2%（319/366）的任务中位列第一。在斯坦福腹部CT数据集的外部验证中，Pillar-0同样以82.2对80.6的AUROC值超越所有基线模型。该模型还能胜任预训练范围之外的任务，例如在肺癌长期风险预测中，其于NLST数据集上较当前最优模型Sybil提升3.0个C指数，在MGH和CGMH数据集分别实现5.9和1.9的泛化增益；在脑出血检测任务中，仅需次优基线模型1/20的数据量即可获得超过95的AUROC。Pillar-0与RATE共同构建了开放、临床严谨的高性能放射学系统基础，突破了以往因算力、数据及评估限制难以实现的应用场景。

English

Radiology plays an integral role in modern medicine, yet rising imaging volumes have far outpaced workforce growth. Foundation models offer a path toward assisting with the full spectrum of radiology tasks, but existing medical models remain limited: they process volumetric CT and MRI as low-fidelity 2D slices, discard critical grayscale contrast information, and lack evaluation frameworks that reflect real clinical practice. We introduce Pillar-0, a radiology foundation model pretrained on 42,990 abdomen-pelvis CTs, 86,411 chest CTs, 14,348 head CTs, and 11,543 breast MRIs from a large academic center, together with RATE, a scalable framework that extracts structured labels for 366 radiologic findings with near-perfect accuracy using LLMs. Across internal test sets of 14,230 abdomen-pelvis CTs, 10,646 chest CTs, 4,906 head CTs, and 1,585 breast MRIs, Pillar-0 establishes a new performance frontier, achieving mean AUROCs of 86.4, 88.0, 90.1, and 82.9, outperforming MedGemma (Google), MedImageInsight (Microsoft), Lingshu (Alibaba), and Merlin (Stanford) by 7.8-15.8 AUROC points and ranking best in 87.2\% (319/366) tasks. Pillar-0 similarly outperforms all baselines in an external validation on the Stanford Abdominal CT dataset, including Merlin (82.2 vs 80.6 AUROC). Pillar-0 extends to tasks beyond its pretraining, such as long-horizon lung cancer risk prediction, where it improves upon the state-of-the-art Sybil by 3.0 C-index points on NLST, and generalizes with gains of 5.9 (MGH) and 1.9 (CGMH). In brain hemorrhage detection, Pillar-0 obtained a >95 AUROC when using only 1/20th of the data of the next most sample efficient baseline. Pillar-0 and RATE together provide an open, clinically rigorous foundation for building high-performance radiology systems, enabling applications that were previously infeasible due to computational, data, and evaluation constraints.

支柱-0：放射学基础模型的新前沿

Pillar-0: A New Frontier for Radiology Foundation Models

摘要

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