Predicting Neoadjuvant Chemotherapy Response and High-Grade Serous Ovarian Cancer From CT Images in Ovarian Cancer with Multitask Deep Learning: A Multicenter Study.

Journal: Academic radiology
Published Date: Jun 17, 2023

Abstract

RATIONALE AND OBJECTIVES: Accurate prediction neoadjuvant chemotherapy (NACT) response in ovarian cancer (OC) is essential for personalized medicine. We aimed to develop and validate a deep learning (DL) model based on pretreatment contrast-enhanced CT (CECT) images for predicting NACT responses and classifying high-grade serous ovarian cancer (HGSOC) to identify patients who may benefit from NACT.

Authors

  • Rui Yin
    Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, College of Medicine, FL, USA. Electronic address: ruiyin@ufl.edu.
  • Yijun Guo
  • Yanyan Wang
    College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China. Electronic address: yanyanwangmail@126.com.
  • Qian Zhang
    The Neonatal Intensive Care Unit, Peking Union Medical College Hospital, Peking, China.
  • Zhaoxiang Dou
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China (Y.G., Z.D., W.M.).
  • Yigeng Wang
    Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China (Y.W.).
  • Lisha Qi
    Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China (L.Q.).
  • Ying Chen
    Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Chao Zhang
    School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
  • Huiyang Li
    Department of Gynecology and Obstetrics, Tianjin Medical University General Hospital, Tianjin, China (H.L.).
  • Xiqi Jian
    School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China (R.Y., X.J.).
  • Wenjuan Ma
    Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China. Electronic address: mawenjuan2008@163.com.

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