Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

Journal: Journal of cancer research and therapeutics
PMID:

Abstract

BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) oncogene status and predict targeted therapy response in colorectal cancer liver metastases (CRLM).

Authors

  • Baogen Zhang
    Department of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University. Xinhua South Road, Tongzhou District, Beijing, China.
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Ting Xu
    Bioresources Green Transformation Collaborative Innovation Center of Hubei Province, College of Life Sciences, Hubei University, Wuhan 430062, Hubei, China.
  • Haibin Zhu
    Department of Statistics and Data Science, School of Economics, Jinan University, Guangzhou, 510632, China.
  • Kangjie Wang
    Division of Vascular Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510800, China; National-Guangdong Joint Engineering Laboratory for Diagnosis and Treatment of Vascular Disease, First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Yaoxian Xiang
    Department of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University. Xinhua South Road, Tongzhou District, Beijing, China.
  • Xuelei He
  • Siyu Zhu
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Chao An
    Department of Minimal invasive intervention, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
  • Dong Yan
    School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.