A Multi-Agent Open-Source LLM for Structured Cancer Registry Information Extraction from Pathology and Medical Reports

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Health & Wellbeing — Clinical Care & Medical Practice, Health & Medical Research · Depth: Advanced, medium

Summary

A modular multi-agent framework has been proposed for structured cancer registry information extraction from pathology and medical reports. This framework decomposes the abstraction process into semantic chunking, retrieval, field-specific extraction, validation, evaluation, and aggregation stages. Evaluated on 818 annotated cancer cases from Sultan Qaboos University Hospital, specifically 454 breast and 174 colorectal reports, it was compared against prompt-based LLaMA 3.3 baselines. The framework improved weighted F1-scores for context-dependent tasks like grade extraction, increasing from 0.71 to 0.78 for breast cancer and 0.56 to 0.67 for colorectal cancer, and also for colorectal laterality. For highly structured tasks such as TNM staging and morphology, performance was comparable to direct prompting. The framework offers enhanced modularity, traceability, and pipeline-level interpretability.

Key takeaway

For NLP Engineers developing solutions for medical information extraction, this multi-agent framework offers a robust approach to improve accuracy and interpretability. You should consider adopting a modular, multi-agent design, especially for context-dependent fields like cancer grade extraction, to enhance performance and facilitate error analysis. This method provides superior traceability compared to direct prompting, enabling more reliable clinician-guided refinement.

Key insights

A modular multi-agent LLM framework significantly improves context-dependent cancer registry data extraction from medical reports.

Principles

• Modular decomposition enhances complex information extraction.
• Explicit intermediate reasoning stages improve interpretability.

Method

The framework involves semantic chunking, retrieval, field-specific extraction, validation, evaluation, and aggregation stages.

In practice

• Apply multi-agent LLMs for nuanced medical text extraction.
• Implement explicit reasoning stages to refine extraction pipelines.

Topics

• Multi-Agent LLMs
• Cancer Registry
• Information Extraction
• Pathology Reports
• Medical Reports
• LLaMA 3.3

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.