Chain-of-Thought for Cancer Pathology Interpretation and Summarization

Surgical pathology reports are foundational to cancer diagnosis, staging, and treatment planning, yet they remain among the most complex and opaque documents in clinical practice. Their dense language, inconsistent structure, and use of domain-specific terminology pose significant barriers not only to patients but also to clinical teams seeking standardized, guideline-concordant care. In this study, we investigate the use of chain-of-thought (CoT) prompting with large language models (LLMs) to improve the interpretability and utility of pathology reports in endometrial cancer―the most common gynecologic malignancy in developed countries. Our approach targets three key objectives: (1) evaluating whether CoT-guided LLMs can extract and infer clinically relevant features from unstructured pathology narratives with high accuracy; (2) generating dual-layered summaries tailored for both oncology teams and patients, preserving diagnostic rigor while enhancing accessibility; and (3) assessing the impact of expert-in-the-loop feedback in refining reasoning quality and staging alignment with AJCC and FIGO guidelines. To benchmark model performance and evaluate clinical relevance, we utilized annotated cases from The Cancer Genome Atlas (TCGA) Endometrial Carcinoma Study, leveraging its curated pathology and genomic data to assess extraction fidelity and staging concordance. We demonstrate that GPT-4o, when augmented with CoT prompts, reduces optical character recognition (OCR) word error rate from 11.2% to 6.7% and improves character accuracy from 94.1% to 96.5%. Information extraction fidelity improves substantially, with histologic type classification rising from 82.0% to 94.1% and staging accuracy from 63.1% to 85.6%. These findings suggest that CoT-guided LLMs offer a scalable framework for enhancing diagnostic communication, improving clinical workflow efficiency, and supporting precision oncology through consistent, interpretable, and patient-centered information delivery.