# Molecular-driven Foundation Model for Oncologic Pathology

arXiv: https://arxiv.org/abs/2501.16652 · HTML: https://mlsanity.com/paper/2501.16652/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2025.

## Built on by (most cited first, 30 of 55)

- [SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence](https://mlsanity.com/paper/2506.02555/) (2025)
- [Do Multiple Instance Learning Models Transfer?](https://mlsanity.com/paper/2506.09022/) (2025)
- [Evidence-based diagnostic reasoning with multi-agent copilot for human pathology](https://mlsanity.com/paper/2506.20964/) (2025)
- [PathAgent: Toward Interpretable Analysis of Whole-slide Pathology Images via Large Language Model-based Agentic Reasoning](https://mlsanity.com/paper/2511.17052/) (2025)
- [A Survey of Pathology Foundation Model: Progress and Future Directions](https://mlsanity.com/paper/2504.04045/) (2025)
- [Multi-Modal Foundation Models for Computational Pathology: A Survey](https://mlsanity.com/paper/2503.09091/) (2025)
- [SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers](https://mlsanity.com/paper/2502.04946/) (2025)
- [GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology](https://mlsanity.com/paper/2504.01009/) (2025)
- [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025)
- [PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue](https://mlsanity.com/paper/2506.13063/) (2025)
- [A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics](https://mlsanity.com/paper/2508.01490/) (2025)
- [MTBBench: A Multimodal Sequential Clinical Decision-Making Benchmark in Oncology](https://mlsanity.com/paper/2511.20490/) (2025)
- [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025)
- [Fusion of Multi-scale Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis](https://mlsanity.com/paper/2510.27237/) (2025)
- [Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging](https://mlsanity.com/paper/2503.02824/) (2025)
- [Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment](https://mlsanity.com/paper/2508.01602/) (2025)
- [Ensemble learning of pathology foundation models for precision oncology](https://mlsanity.com/paper/2508.16085/) (2025)
- [Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?](https://mlsanity.com/paper/2502.20823/) (2025)
- [MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention](https://mlsanity.com/paper/2503.00374/) (2025)
- [MOOZY: A Patient-First Foundation Model for Computational Pathology](https://mlsanity.com/paper/2603.27048/) (2026)
- [ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology](https://mlsanity.com/paper/2503.17564/) (2025)
- [SGPMIL: Sparse Gaussian Process Multiple Instance Learning](https://mlsanity.com/paper/2507.08711/) (2025)
- [Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients](https://mlsanity.com/paper/2508.16742/) (2025)
- [EXAONE Path 2.5: Pathology Foundation Model with Multi-Omics Alignment](https://mlsanity.com/paper/2512.14019/) (2025)
- [Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness](https://mlsanity.com/paper/2603.05884/) (2026)
- [A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images](https://mlsanity.com/paper/2502.19217/) (2025)
- [Morphology-Aware Prognostic model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole Slide Images](https://mlsanity.com/paper/2510.14800/) (2025)
- [nnMIL: A generalizable multiple instance learning framework for computational pathology](https://mlsanity.com/paper/2511.14907/) (2025)
- [PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classification](https://mlsanity.com/paper/2603.01547/) (2026)
- [Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images](https://mlsanity.com/paper/2604.21060/) (2026)

## Abstract (arXiv)

Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. Here, we introduce Threads, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. Threads was pre-trained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles - the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables Threads to capture the tissue's underlying molecular composition, yielding powerful representations applicable to a wide array of downstream tasks. In extensive benchmarking across 54 oncology tasks, including clinical subtyping, grading, mutation prediction, immunohistochemistry status determination, treatment response prediction, and survival prediction, Threads outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well suited for predicting rare events, further emphasizing its clinical utility. We intend to make the model publicly available for the broader community.