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Towards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance

arXiv 2607.08602 ↗First posted 9 Jul 2026cs.AIcited by 0

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Abstract (arXiv)

Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality. Current guidelines and staging systems provide coarse categories, but often miss within-stage heterogeneity and the clinical context in electronic medical records (EMRs). We present HCC-STAR (Hepatocellular Carcinoma Staging, Treatment And pRognosis), a clinically aligned large language model that reads routine EMR narratives and jointly outputs risk score-based staging, ranked guideline-consistent treatments with evidence-based rationales, and individualized survival estimates. We curated about 30,000 HCC cases from SEER and expanded them into EMR-style narrative training data using a clinician-validated, prompt-based augmentation workflow. On this corpus, we developed a knowledge-aligned reasoning framework optimized with a step-verifiable composite reward, moving beyond text-level memorization of clinical guidelines. In a multi-center cohort of 6,668 patients from 12 hospitals in China, HCC-STAR achieved state-of-the-art performance in treatment recommendation and risk stratification compared with clinical guidelines and competitive models, including GPT-5 and Gemini-2.5 Pro. Hypothetical overall-survival analysis showed a median survival of 51 months under adherence to HCC-STAR recommendations, compared with 29 and 32 months under BCLC and CNLC. In clinician-centric evaluations, blinded hepatobiliary specialists rated HCC-STAR's reasoning and evidence-based justifications as trustworthy. The model surpassed resident and attending physicians in treatment accuracy and helped physicians make more accurate decisions faster when used as an assistant. These findings support HCC-STAR as a reliable and verifiable decision-support system for risk stratification and precision therapy in HCC.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 65 references that are in the map. First posted on arXiv 9 Jul 2026.

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References · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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10 more
  1. 2025SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-trainingcited by 672
  2. 2023WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instructcited by 625
  3. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
  4. 2025Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learningcited by 262
  5. 2025STAIR: Improving Safety Alignment with Introspective Reasoningcited by 77
  6. 2025NFT: Bridging Supervised Learning and Reinforcement Learning in Math Reasoningcited by 38
  7. 2024Towards Efficient Exact Optimization of Language Model Alignmentcited by 37
  8. 2024What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?cited by 23
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  10. 2024HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuningcited by 20

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  2. 2023CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference
  3. 2025CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records
  4. 2026BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma
  5. 2026MedCoRAG: Interpretable Hepatology Diagnosis via Hybrid Evidence Retrieval and Multispecialty Consensus
  6. 2025CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation
  7. 2026CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction
  8. 2024Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging