ArticleCureus2025
Precision Oncology in Non-small Cell Lung Cancer: A Comparative Study of Contextualized ChatGPT Models.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Lung Cancer: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A multidimensional benchmarking framework for large language models in oncologic decision making.Scientific reports · 2026Article
- Feasibility and Concordance of a Large Language Model (ChatGPT-5) as a Clinical Decision Support Tool in Gynecologic Oncology Tumor Boards: A Blinded, Multi-Observer Study.Journal of clinical medicine · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe growing adoption of Large Language Models (LLMs) in medicine has raised important questions about their potential utility for clinical decision support within oncology. This study aimed to evaluate the effects of various contextualization methods on ChatGPT's ability to provide National Comprehensive Cancer Network (NCCN) guideline-aligned recommendations on managing non-small cell lung cancer (NSCLC). METHODOLOGY: GPT-4o, base GPT-4, and GPT-4 models contextualized with prompts and PDF documents were asked to identify preferred chemotherapies for twelve advanced lung cancers given molecular profiles derived from the 2024 NCCN Clinical Practice Guidelines in Oncology for NSCLC. GPT responses were subsequently compared to NCCN guidelines using readability scores and qualitative reviewer assessments of (1) recommendation of specific targeted therapy, (2) agreement with NCCN-guideline-preferred therapies, (3) recommendation of guideline non-concordant therapies, and (4) provision of supplementary information.
resultsThe PDF+Prompt contextualized model demonstrated elevated agreement scores of 23/24 versus 17/24 for GPT-4 (
conclusionsThe performance gains observed following contextualization suggest that broader applications of LLMs in oncology may exist than current literature indicates. This study provides proof of concept for the use of contextualized GPT models in oncology and showcases their accessibility. Future studies validating this application within additional cancer types or real-life patient encounters could provide an important bridge to eventual adoption.
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Registered trials
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.