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Related Experiment Video

Updated: Jun 13, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

SCRIPT: Stratified clinical risk prediction from pathology reports using large language models.

Chiara M L Loeffler1,2,3, Nic G Reitsam1,4,5, Fabian Wolf1

  • 1Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, 01307 Dresden, Germany.

Journal of Pathology Informatics
|June 12, 2026
PubMed
Summary

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Nature communications·2026

Large language models (LLMs) can extract prognostic information from free-text pathology reports, creating a novel survival biomarker. This approach enhances cancer risk stratification without increasing pathologist workload.

Area of Science:

  • Oncology
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Accurate cancer risk stratification is crucial for treatment decisions.
  • Current methods often overlook valuable prognostic information within narrative pathology reports.
  • Free-text pathology reports contain nuanced descriptions and expert judgment that are underutilized.

Purpose of the Study:

  • To investigate the potential of large language models (LLMs) to extract prognostic information from free-text pathology reports.
  • To develop a method for converting narrative pathology report data into a binary survival biomarker.
  • To assess the utility of LLM-generated risk scores in predicting patient survival outcomes.

Main Methods:

  • Utilized the LLaMA 3.3 70B large language model to process complete free-text pathology reports.
Keywords:
Deep learningGastrointestinal cancerLLMPathology reportsPrognostic biomarker

Related Experiment Videos

Last Updated: Jun 13, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

  • Prompted the LLM to synthesize narrative reports into a binary prognostic risk score.
  • Evaluated the association between LLM-generated risk scores and survival outcomes (overall survival, progression-free survival, disease-specific survival) in gastrointestinal cancers.

    • Main Results:

    • LLM-generated risk scores showed significant prognostic value for overall survival, progression-free survival, and disease-specific survival in colorectal cancer.
    • Hazard ratios indicated strong associations between LLM scores and adverse survival outcomes (e.g., HR=2.77 for OS, HR=2.93 for PFS, HR=5.85 for DSS).
    • Multivariate analysis confirmed the LLM-generated risk score as an independent prognostic factor for progression-free survival.

    Conclusions:

    • Large language models can effectively transform narrative pathology reports into a singular, independent survival biomarker.
    • This method leverages existing free-text data, requiring no additional tissue analysis or pathologist effort.
    • The LLM-based approach offers a deployable solution to enhance cancer risk stratification and inform treatment decisions.