Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.

Journal: Clinical lung cancer
PMID:

Abstract

BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC.

Authors

  • Zhaojuan Jiang
    Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China.
  • Qingwan Li
    Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China.
  • Jinqiu Ruan
    Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China.
  • Yanli Li
    Intuitive Surgical, Inc., Sunnyvale, California, USA.
  • Dafu Zhang
    Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China.
  • Yongzhou Xu
    Department of Clinical & Technique Support, Philips Healthcare, Guangzhou, 510220, China.
  • Yuting Liao
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Depei Gao
    Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China. Electronic address: gaodepei311@sohu.com.
  • Zhenhui Li
    College of Information Sciences and Technology, Pennsylvania State University.