Agent-Based Large Language Model System for Extracting Structured Data from Breast Cancer Synoptic Reports: A Dual-Validation Study
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Objective
To develop and validate an agent-based Large Language Model (LLM) system for extracting structured data from breast cancer synoptic pathology reports and assess the performance gap between synthetic and real-world validation.
Materials and Methods
We developed a modular AI agent-based framework employing sequential specialized LLMs for parsing pathology reports and extracting structured data. We normalized College of American Pathologists (CAP) cancer protocols into 8 sections, 86 subsections, and 229 discrete fields. Seven leading LLMs (gemini-2.5-pro, llama3.3-70b, phi4-14b, deepseek-r1 14B/70B, gemma3-27b, gemini-2.0-flash-lite) were validated using dual evaluation: synthetic validation (864 controlled test cases) and real-world ground truth (6,651 annotated fields from 90 pathology reports).
Results
Synthetic validation demonstrated strong performance (accuracy: 93.8-99.0%). Real-world evaluation revealed field extraction accuracy ranging from 61.8% to 87.7%, demonstrating a substantial “reality gap” with accuracy drops of 11-32 percentage points. The gemini-2.5-pro model achieved the highest real-world accuracy (87.7%). Model size did not predict performance: the 14B-parameter deepseek-r1 (77.6%) outperformed its 70B-parameter counterpart (70.4%).
Discussion
The substantial performance degradation from synthetic to real-world data underscores the complexity of authentic clinical documentation. Smaller models can achieve competitive or superior accuracy, reducing computational costs. With even the best models missing 12-38% of annotated fields, mandatory human verification is essential for clinical deployment.
Conclusion
While LLM-based extraction systems show promise for pathology data extraction, synthetic validation alone provides false confidence. Rigorous real-world ground truth evaluation with expert annotation is essential before clinical deployment. These systems are best positioned as screening tools with mandatory human oversight rather than autonomous decision-making systems.
