A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.

Journal: Journal of translational medicine
Published Date: Nov 7, 2023

Abstract

BACKGROUND: The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks present continuous difficulties due to the nonuniform intensity distributions, ambiguous boundaries, and variable shapes of brain metastasis (BM) in MR images.The existing approaches for tackling these challenges mainly rely on single-task algorithms, which overlook the interdependence between these two tasks.

Authors

  • Zichun Zhou
    Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, 250117, Shandong, China.
  • Min Wang
    National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, Wenzhou University, Wenzhou 325035, China.
  • Rubin Zhao
    Department of Radiation Oncology and Technology, Linyi People's Hospital, 27 Jiefang Road, Linyi, 276003, Shandong, China.
  • Yan Shao
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Ligang Xing
    Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong, 250117, China.
  • Qingtao Qiu
    Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China.
  • Yong Yin
    Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong, 250117, China.

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