{"slug": "foundation-model-3x-better-at-predicting-cancer-treatment", "title": "Foundation Model 3x better at predicting cancer treatment", "summary": "A new multimodal foundation model called oFM, developed on a real-world oncology cohort of 1.67 million cancer patients, achieved a three-fold higher treatment-benefit AUTOC than baseline features across 11 comparative-treatment cohorts, with improved benefit ranking in 9 of 11 cohorts. The model, which integrates clinical trajectories with DNA, RNA, and H&E pathology, also improved AUC for overall survival to 0.774 versus 0.563 for baseline features, according to a paper submitted to arXiv on 25 Aug 2026.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 25 Aug 2026]\n\n# Title:A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology\n\n[View PDF](/pdf/2608.24688)\n\n[HTML (experimental)](https://arxiv.org/html/2608.24688v1)\n\nAbstract:Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal observations. We introduce the oFM, a foundation model developed on a real-world oncology cohort of 1.67 million cancer patients that integrates clinical trajectories with DNA, RNA, and H&E pathology. Patient-level partitions were reserved for training, validation, and testing, with over one million patients used for training. The oFM encodes daily clinical and molecular episodes and, along with pathology images, integrates them over time to produce a patient state embedding. We evaluate frozen oFM embeddings against expert-curated clinical and molecular baseline features. In prognostic benchmarks, the oFM improved AUC for treatment response, progression-free survival, and overall survival (0.774 vs. 0.563 for overall survival). Across 11 comparative-treatment cohorts, the oFM embeddings achieved a three-fold higher pooled and scale-normalized treatment-benefit AUTOC than baseline features with improved benefit ranking in 9 of 11 cohorts, and provided stronger prognostic discrimination within both treatment arms. We also evaluated a mechanism discovery framework that interprets downstream models built on oFM embeddings by linking their predicted outcomes to clinically and biologically grounded mechanisms through an evidence-grounded temporal graph, enabling evaluation in clinical and drug-development applications.\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/foundation-model-3x-better-at-predicting-cancer-treatment", "canonical_source": "https://arxiv.org/abs/2608.24688", "published_at": "2026-08-27 16:42:40+00:00", "updated_at": "2026-08-27 16:49:30.151208+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-research"], "entities": ["oFM", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/foundation-model-3x-better-at-predicting-cancer-treatment", "markdown": "https://wpnews.pro/news/foundation-model-3x-better-at-predicting-cancer-treatment.md", "text": "https://wpnews.pro/news/foundation-model-3x-better-at-predicting-cancer-treatment.txt", "jsonld": "https://wpnews.pro/news/foundation-model-3x-better-at-predicting-cancer-treatment.jsonld"}}