LLMs advance cancer survival prediction with new comparative learning framework

Researchers have developed CACSurv, a novel framework that leverages large language models (LLMs) for cancer survival prediction using patient reports. This method addresses two key challenges: a formulation mismatch where survival prediction requires ranking patients rather than independent time regression, and a supervision mismatch where censored patient data implies orderings but not exact survival times. CACSurv reformulates the problem as comparative reasoning, predicting relative prognostic orderings and using concordance-aligned rewards for supervision. The framework achieved a C-index of 0.722 on the TCGA-SurvReport benchmark, outperforming existing models. AI

IMPACT Enhances LLM capabilities in specialized medical domains, potentially improving diagnostic and treatment planning tools.

RANK_REASON Academic paper detailing a new methodology for cancer survival prediction using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• CACSurv
• cancer survival prediction
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• large-language models
• ScienceCast
• TCGA-SurvReport
• The Cancer Genome Atlas

• paper
• model release

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