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Predictive Immune Modeling of Solid Tumors

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Published on: February 25, 2020

Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma.

Jingwei Huang¹, Ayesha Batool^2,3, Zifan Gu¹

¹O'Donnell School of Public Health, UT Southwestern Medical Center Dallas.

Biorxiv : the Preprint Server for Biology

|February 27, 2026

Summary

This summary is machine-generated.

Large Language Models (LLMs) accurately extracted prognostic histologic features from Ewing sarcoma pathology reports. Neuron-Specific Enolase (NSE) indicates higher risk, while S100 suggests better survival, especially in localized disease.

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Area of Science:

Oncology
Medical Informatics
Artificial Intelligence in Medicine

Background:

Current Ewing sarcoma risk stratification primarily uses clinical factors like metastatic status.
Histologic heterogeneity is a potential prognostic indicator but is difficult to extract from unstructured pathology reports.
Large-scale retrospective analyses are limited by the labor-intensive nature of manual data extraction from historical clinical trial documents.

Purpose of the Study:

To validate the utility of Large Language Models (LLMs) for scalable data abstraction from pathology reports.
To identify prognostic histologic features in a large, multi-institutional cohort of Ewing sarcoma patients.
To assess the impact of AI-derived histologic data on refining risk stratification for Ewing sarcoma.

Main Methods:

Retrospective cohort study of 931 patients from six Children's Oncology Group (COG) clinical trials.
Utilized an LLM-based pipeline to extract immunohistochemical (IHC) markers and CD99 staining patterns from digitized pathology reports.
Validated LLM extraction accuracy against human-annotated ground truth and senior experts; assessed prognostic value using survival analyses.

Main Results:

The LLM achieved 98.1% accuracy in cross-validation, outperforming human annotators.
Neuron-Specific Enolase (NSE) positivity was associated with significantly inferior Overall Survival (OS) (HR 2.15), particularly in non-metastatic disease (HR 5.64).
S100 positivity was associated with improved OS (HR 0.58).

Conclusions:

LLM-assisted extraction of pathology variables is accurate, scalable, and can unlock valuable 'dark data' from historical clinical trials.
NSE and S100 are identified as significant prognostic biomarkers for Ewing sarcoma, particularly in localized disease.
AI-derived histologic data can refine risk stratification and warrants consideration for future prospective clinical trials.