Evaluating generative AI models for explainable pathological feature extraction in lung adenocarcinoma: grading assessment and prognostic model construction

作者：Junyi Shen, Suyin Feng, Zhenfa Zhang, Qi Chang, Zaoqu Liu, Yuying Feng, Chunrong Dong, Zhenyu Xie, Wenyi Gan, Lingxuan Zhu, Weiming Mou, Dongqiang Zeng, Bufu Tang, Mingjia Xiao, Guangdi Chu, Quan Cheng, Jian Zhang, Shengkun Peng, Yifeng Bai, Hank Z. H. Wong, Aimin Jiang, Peng Luo, Anqi Lin · 发表于：International Journal of Surgery · 年份：2025 · DOI：10.1097/js9.0000000000002507 · 被引用次数：6 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Artificial Intelligence in Healthcare and Education

BACKGROUND: Given the increasing prevalence of generative AI (GenAI) models, a systematically evaluation of their performance in lung adenocarcinoma histopathological assessment is crucial. This study aimed to evaluate and compare three visual-capable GenAI models (GPT-4o, Claude-3.5-Sonnet, and Gemini-1.5-Pro) for lung adenocarcinoma histological pattern recognition and grading, as well as to explore prognostic prediction models based on GenAI feature extraction. MATERIALS AND METHODS: In this retrospective study, we analyzed 310 diagnostic slides from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) database to evaluate GenAI models and to develop and internally validate machine learning-based prognostic models. For independent external validation, we utilized 95 and 87 slides from obtained different institutions. The primary endpoints comprised GenAI grading accuracy (area under the receiver operating characteristic curve, AUC) and stability (intraclass correlation coefficient, ICC). Secondary endpoints included developing and assessing machine learning-based prognostic models using GenAI-extracted features from the TCGA-LUAD dataset, evaluated by Concordance index (C-index). RESULTS: Among the evaluated models, claude-3.5-Sonnet demonstrated the best overall performance, achieving high grading accuracy (average AUC = 0.823) with moderate stability (ICC = 0.585) The optimal machine learning-based prognostic model, developed using features extracted by Claude-3.5-Son...