A PET/CT-based 3D deep learning model for predicting spread through air spaces in stage I lung adenocarcinoma.

Journal: Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Published Date: Feb 24, 2025

Abstract

PURPOSE: This study evaluates a three-dimensional (3D) deep learning (DL) model based on fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for predicting the preoperative status of spread through air spaces (STAS) in patients with clinical stage I lung adenocarcinoma (LUAD).

Authors

  • Cheng Zheng
    Department of Computer Science, University of California, Los Angeles.
  • Yujie Cai
    The Affiliated Cancer Hospital of Wenzhou Medical University, Wenzhou Central Hospital, Wenzhou, 325000, PR China.
  • Jiangfeng Miao
    Department of Nuclear Medicine, Affiliated Hospital of Nantong University, No. 20 of Xisi Road, ChongChuan District, Nantong, 226001, Jiangsu, China.
  • BingShu Zheng
    Department of Nuclear Medicine, Affiliated Hospital of Nantong University, No. 20 of Xisi Road, ChongChuan District, Nantong, 226001, Jiangsu, China.
  • Yan Gao
    Department of Rehabilitation Medicine, The First Affiliated Hospital of Shenzhen University, The Second People's Hospital of Shenzhen, Shenzhen, Guangdong, China.
  • Chen Shen
    Department of Foreign Languages, Xi'an Jiaotong University City College, Xi'an, China.
  • ShanLei Bao
    Department of Nuclear Medicine, Affiliated Hospital of Nantong University, No. 20 of Xisi Road, ChongChuan District, Nantong, 226001, Jiangsu, China.
  • ZhongHua Tan
    Department of Nuclear Medicine, Affiliated Hospital of Nantong University, No. 20 of Xisi Road, ChongChuan District, Nantong, 226001, Jiangsu, China.
  • ChunFeng Sun
    Department of Nuclear Medicine, Affiliated Hospital of Nantong University, No. 20 of Xisi Road, ChongChuan District, Nantong, 226001, Jiangsu, China. sunchunfeng-nt@ntu.edu.cn.

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