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

Journal: International journal of surgery (London, England)
Published Date:

Abstract

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.

Authors

  • Junyi Shen
    Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610044, China.
  • Suyin Feng
    Department of Neurosurgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, 214062, China.
  • Pengpeng Zhang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA. Electronic address: zhangp@mskcc.org.
  • Chang Qi
    Institute of Logic and Computation, TU Wien, Austria.
  • Zaoqu Liu
    Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Yuying Feng
    Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, P. R. China.
  • Chunrong Dong
    Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Zhenyu Xie
    Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Wenyi Gan
    The First Clinical Medical College of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Lingxuan Zhu
    Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Changping Laboratory, Beijing, China.
  • Weiming Mou
    Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Dongqiang Zeng
    Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Bufu Tang
    Department of Radiation Oncology, Zhongshan Hospital Affiliated to Fudan University, Shanghai, China.
  • Mingjia Xiao
    Hepatobiliary Surgery Department, Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, China.
  • Guangdi Chu
    Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China.
  • Quan Cheng
    Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
  • Jian Zhang
    College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.
  • Shengkun Peng
    Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China.
  • Yifeng Bai
    Department of Oncology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. baiyifeng@med.uestc.edu.cn.
  • Hank Z H Wong
    Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.
  • Aimin Jiang
    Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China.
  • Peng Luo
    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, PR China.
  • Anqi Lin
    Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.

Keywords

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