Scholarlink

Purpose <p>Accurate staging of prostate cancer is essential for therapeutic decision-making. While PSMA PET-CT reports offer rich clinical data, their unstructured format hinders large-scale analysis. Recent advances in large language models (LLMs) offer new opportunities to extract structured information from narrative radiology reports. However, their ability to perform multi-step clinical reasoning, particularly for cancer staging, remains underexplored.</p> Methods <p>In this feasibility study, 80 anonymized, Turkish-language PSMA PET-CT reports were independently interpreted by two LLMs—Gemini 2.5 Pro (Google) and ChatGPT 4o (OpenAI). Using a structured prompt containing an embedded knowledge base (AJCC/CHAARTED criteria) and few-shot examples, both LLMs generated classifications for T, N, M, and overall clinical stage/disease volume. Outputs were benchmarked against expert classifications by a senior nuclear medicine specialist. Performance was evaluated using accuracy, precision, recall, F1-score, and Cohen’s kappa.</p> Results <p>For the composite task of classifying clinical stage and disease volume, Gemini 2.5 Pro achieved an accuracy of 93.8% (95% CI: 86.0–97.9) and a Cohen’s kappa of 0.910 (95% CI: 0.834–0.986), while ChatGPT 4o achieved 91.3% accuracy (95% CI: 82.8–96.4) with a kappa of 0.874 (95% CI: 0.786–0.962). For T staging, Gemini showed a higher accuracy point estimate (95.0% [95% CI: 87.7–98.6] vs. 91.3% [95% CI: 82.8–96.4]), while both models excelled at the binary N and M classifications, achieving accuracies above 95% and kappa values indicating near-perfect agreement (κ &gt; 0.900).</p> Conclusions <p>LLMs, when guided by expert-informed prompt engineering, can accurately stage prostate cancer from free-text PSMA PET-CT reports and may serve as a powerful assistive tool for data automation, research acceleration, and quality assurance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Staging Prostate Cancer with AI: A Comparative Study of Large Language Models and Expert Interpretation on PSMA PET-CT Reports

  • 期刊论文

  • 发表日期：

    2025年12月5日

查看全文
  • Rashad Ismayilov,
  • Ayse Aktas,
  • Esra Arzu Gencoglu,
  • Arzu Oguz,
  • Ozden Altundag,
  • Zafer Akcali

摘要

Purpose

Accurate staging of prostate cancer is essential for therapeutic decision-making. While PSMA PET-CT reports offer rich clinical data, their unstructured format hinders large-scale analysis. Recent advances in large language models (LLMs) offer new opportunities to extract structured information from narrative radiology reports. However, their ability to perform multi-step clinical reasoning, particularly for cancer staging, remains underexplored.

Methods

In this feasibility study, 80 anonymized, Turkish-language PSMA PET-CT reports were independently interpreted by two LLMs—Gemini 2.5 Pro (Google) and ChatGPT 4o (OpenAI). Using a structured prompt containing an embedded knowledge base (AJCC/CHAARTED criteria) and few-shot examples, both LLMs generated classifications for T, N, M, and overall clinical stage/disease volume. Outputs were benchmarked against expert classifications by a senior nuclear medicine specialist. Performance was evaluated using accuracy, precision, recall, F1-score, and Cohen’s kappa.

Results

For the composite task of classifying clinical stage and disease volume, Gemini 2.5 Pro achieved an accuracy of 93.8% (95% CI: 86.0–97.9) and a Cohen’s kappa of 0.910 (95% CI: 0.834–0.986), while ChatGPT 4o achieved 91.3% accuracy (95% CI: 82.8–96.4) with a kappa of 0.874 (95% CI: 0.786–0.962). For T staging, Gemini showed a higher accuracy point estimate (95.0% [95% CI: 87.7–98.6] vs. 91.3% [95% CI: 82.8–96.4]), while both models excelled at the binary N and M classifications, achieving accuracies above 95% and kappa values indicating near-perfect agreement (κ > 0.900).

Conclusions

LLMs, when guided by expert-informed prompt engineering, can accurately stage prostate cancer from free-text PSMA PET-CT reports and may serve as a powerful assistive tool for data automation, research acceleration, and quality assurance.