# A Synthetic Data-Driven Radiology Foundation Model for Pan-tumor Clinical Diagnosis

arXiv: https://arxiv.org/abs/2502.06171 · HTML: https://mlsanity.com/paper/2502.06171/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2025.

## Built on by (most cited first, 7 of 7)

- [Scaling Self-Supervised and Cross-Modal Pretraining for Volumetric CT Transformers](https://mlsanity.com/paper/2511.17209/) (2025)
- [LesionDiffusion: Towards Text-controlled General Lesion Synthesis](https://mlsanity.com/paper/2503.00741/) (2025)
- [MFM-DA: Instance-Aware Adaptor and Hierarchical Alignment for Efficient Domain Adaptation in Medical Foundation Models](https://mlsanity.com/paper/2503.00802/) (2025)
- [Interactive Segmentation and Report Generation for CT Images](https://mlsanity.com/paper/2503.03294/) (2025)
- [TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT](https://mlsanity.com/paper/2605.16572/) (2026)
- [MieDB-100k: A Comprehensive Dataset for Medical Image Editing](https://mlsanity.com/paper/2602.09587/) (2026)
- [GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for 18F-FDG-PET/CT](https://mlsanity.com/paper/2607.03931/) (2026)

## Abstract (arXiv)

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.