ArticleJapanese journal of radiology2025
Artificial intelligence for immunotherapy response assessment in lung cancer using PET/CT reports.
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
- Accuracy of large language models in head and neck cancers: a comparative analysis of ChatGPT and Gemini in TNM staging and clinical decision support.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate and timely assessment of immunotherapy response is vital for optimizing lung cancer management. This study evaluates the efficacy of large language models (LLMs) in automating response assessment using positron emission tomography/computed tomography (PET/CT) reports based on the European Organization for Research and Treatment of Cancer (EORTC) criteria.
methodsAn effective prompting strategy was developed using Google Gemini 2.5 Pro Experimental 03-25, with explicit instructions for applying EORTC criteria via few-shot prompting. This prompt was then tested with both Gemini 2.5 Pro and OpenAI ChatGPT 4o to assess cross-model performance. Pre- and post-immunotherapy PET-CT reports in text format from 36 lung cancer patients were independently classified by the LLMs and an experienced nuclear medicine specialist. Performance metrics, including precision, recall, F1-score, and support, were calculated for each response category. Inter-rater agreement was assessed using Cohen's Kappa.
resultsThe nuclear medicine specialist classified 5, 21, 6, and 4 reports as complete metabolic response (CMR), progressive metabolic disease (PMD), partial metabolic response (PMR), and stable metabolic disease (SMD), respectively, while Gemini 2.5 Pro classified 4, 21, 8, and 3 of them. Gemini achieved an overall accuracy of 94% and demonstrated strong agreement with the expert (overall Cohen's Kappa: 0.907). F1-scores were 0.86 for PMR and SMD, 0.89 for CMR, and 1.00 for PMD, with per-label Kappa scores ranging from 0.824 (PMR) to 1.00 (PMD). In comparison, ChatGPT 4o achieved perfect agreement with the expert across all 36 cases (accuracy = 100%, Cohen's Kappa = 1.000).
conclusionsWhen guided by a structured and task-specific prompt, both Gemini 2.5 Pro and ChatGPT 4o demonstrated strong capability for automating accurate immunotherapy response assessment in lung cancer using PET-CT reports. These results underscore the potential of LLMs to streamline clinical workflows and improve efficiency. Validation with larger data sets is warranted to support clinical implementation.
Indexed as
Identifiers
41091339What OpenQuestion holds
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.