ArticleAnnals of surgical treatment and research2025
Using large language models for clinical staging of colorectal cancer from imaging reports: a pilot study.
Article in Annals of surgical treatment and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Commercial large language models for oral cavity cancer staging using descriptive pre-treatment MRI reports: ready for standalone use in clinical practice?Insights into imaging · 2026Article
- Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation.Diagnostics (Basel, Switzerland) · 2026Article
- AI vs. MD: Benchmarking ChatGPT and Gemini for Complex Wound Management.Journal of clinical medicine · 2025Article
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Authors and funding
7 authors.
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Abstract
Purpose: Accurate data collection and analysis are crucial in clinical research, particularly for extracting information from unstructured medical records in cancer research. Traditional methods often struggle with this task. Large language models (LLMs) specializing in natural language processing (NLP), like ChatGPT (OpenAI), show potential for automating this process. This study evaluated whether GPT-4 could accurately extract clinical stages of colorectal cancer (CRC) from imaging reports. Methods: Using specific prompts based on the American Joint Committee on Cancer TNM staging system, GPT-4 was tested on the unstructured abdominal imaging reports of 100 CRC patients. The results were evaluated by a colorectal surgical oncologist and compared with data manually extracted by a nonspecialist data manager. Results: GPT-4 demonstrated high accuracy in extracting lesion locations (96.0%) and T (89.0%), N (90.0%), and M (85.0%) stages, with an overall TNM stage extraction accuracy of 69.0%. The combined accuracy for TNM stage and lesion location was 67.0%. Human data managers had similar TNM stage accuracy but lower lesion-location accuracy (76.0%). Higher accuracy was observed when reports directly mentioned stages and were in English only. Conclusion: This study confirms that LLM-based NLP, with proper prompt engineering, can accurately extract clinical stages from CRC imaging reports, particularly in English-only contexts.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.