Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma.

Journal: La Radiologia medica
Published Date: Mar 21, 2024

Abstract

PURPOSE: To investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC).

Authors

  • Jiwen Huo
    Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu Zhong District, Chongqing, 400016, China.
  • Xuhong Min
    Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, Anhui Province, China.
  • Tianyou Luo
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
  • Fajin Lv
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Yibo Feng
    Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District, Beijing, 100025, China.
  • Qianrui Fan
    Institute of Research, Ocean International Center, InferVision, Chaoyang District, Beijing, 100025, China.
  • Dawei Wang
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
  • Dongchun Ma
    Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, Anhui Province, China. ma_dongchun@163.com.
  • Qi Li
    The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.

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