AI-assisted imaging made substantial advances in tumor diagnosis and management. However, a major barrier to developing robust oncology foundation models is the scarcity of large-scale, high-quality annotated datasets, which are limited by privacy restrictions and the high cost of manual labeling. To address this gap, we present PASTA, a pan-tumor radiology foundation model built on PASTA-Gen, a synthetic data framework that generated 30,000 3D CT scans with pixel-level lesion masks and structured reports of tumors across ten organ systems. Leveraging this resource, PASTA achieves state-of-the-art performance on 45 of 46 oncology tasks, including non-contrast CT tumor screening, lesion segmentation, structured reporting, tumor staging, survival prediction, and MRI-modality transfer. To assess clinical applicability, we developed PASTA-AID, a clinical decision support system, and ran a retrospective simulated clinical trial across two scenarios. For pan-tumor screening on plain CT with fixed reading time, PASTA-AID increased radiologists' throughput by 11.1-25.1% and improved sensitivity by 17.0-31.4% and precision by 10.5-24.9%; additionally, in a diagnosis-aid workflow, it reduced segmentation time by up to 78.2% and reporting time by up to 36.5%. Beyond gains in accuracy and efficiency, PASTA-AID narrowed the expertise gap, enabling less-experienced radiologists to approach expert-level performance. Together, this work establishes an end-to-end, synthetic data-driven pipeline spanning data generation, model development, and clinical validation, thereby demonstrating substantial potential for pan-tumor research and clinical translation.

翻译：人工智能辅助成像在肿瘤诊断与管理方面取得了显著进展。然而，开发稳健的肿瘤学基础模型面临的主要障碍是大规模、高质量标注数据集的稀缺，这受限于隐私限制和人工标注的高昂成本。为弥补这一缺口，我们提出了PASTA，一种基于PASTA-Gen的泛肿瘤放射学基础模型。PASTA-Gen是一个合成数据框架，生成了30,000个三维CT扫描，包含像素级病灶掩码以及涵盖十个器官系统的肿瘤结构化报告。利用这一资源，PASTA在46项肿瘤学任务中的45项上实现了最先进的性能，包括非增强CT肿瘤筛查、病灶分割、结构化报告、肿瘤分期、生存预测和MRI模态迁移。为评估临床适用性，我们开发了临床决策支持系统PASTA-AID，并在两种场景下进行了回顾性模拟临床试验。对于固定阅片时间下的平扫CT泛肿瘤筛查，PASTA-AID将放射科医生的阅片通量提高了11.1-25.1%，并将灵敏度提升了17.0-31.4%，精确度提升了10.5-24.9%；此外，在诊断辅助工作流中，它将分割时间减少了高达78.2%，报告时间减少了高达36.5%。除了准确性和效率的提升，PASTA-AID还缩小了专业水平差距，使经验较少的放射科医生能够接近专家级水平。总之，这项工作建立了一个端到端的、基于合成数据的流程，涵盖数据生成、模型开发和临床验证，从而展示了其在泛肿瘤研究和临床转化方面的巨大潜力。