Narrative-based explainable AI for precision oncology survival prediction

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ID: 338240
2026
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Abstract
MOTIVATION: Explainable and accurate Survival Prediction (SP) remain an open challenge in Precision Oncology. Early SP methods achieved interpretability through simple models, often at the expenses of prediction performance. More recent Machine Learning (ML) methods can learn complex patterns, but they often resemble inscrutable black boxes. Here we propose SurvXAI, a new framework that combines reliable SP with the latest eXplainable AI (XAI) approach. RESULTS: We first train a Random Survival Forest model to predict the survival function of each patient. We then interpret these prediction by extracting patient-specific feature contributions with SurvSHAP, and we feed them into locally deployed Large Language Models (LLMs), to translate the numeric individual-level interpretation of the SP into natural language narrative explanations. Since conventional numerical breakdowns of the features contributions to the final predictions are often perceived as un-intuitive to non-ML experts, SurvXAI overcomes this limitation by automatically translating them into narrative explanations that can be readily used by clinicians, to facilitate the interpretation of model predictions.We validated our SP pipeline on six oncological datasets, including an in-house Multiple Myeloma cohort, comparing it with nine state of the art SP predictors. We then quantified the quality and usefulness of SurvXAI narrative explanations with a blind evaluation carried by seven human experts. This evaluation showed that larger LLMs produced more coherent explanations with accurate feature ranking and clinically plausible reasoning. SurvXAI demonstrates the potential of integrating explainable SP with LLMs to enable trustworthy, personalized interpretation in oncology and other clinical fields. AVAILABILITY AND IMPLEMENTATION: SurvXAI is available at https://github.com/AI-for-Genome-Inter pretation-lab/SurvXAI_pipeline. The module for narrative generation is available at https://github.com/AI-for-Genome-Interpretation-lab/SurvX AI_stories.
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openalex_W7225096082 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ziyun Pan, Francesco Codicè, Caroline Bret, Elvira Garcia De Paco, Morgane Thomas, André Mas, Jérôme Moreaux, Daniele Raimondi
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag806
URL
https://doi.org/10.1093/bioinformatics/btag806
Keywords Keywords not found

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