Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 38627828

Abstract

BACKGROUND: Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection.

Authors

  • Zhen Tian
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
  • Yifan Cheng
    Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
  • Shuai Zhao
    Xi'an Medical University, Xi'an Shaanxi, 710068, P.R.China.
  • Ruiqi Li
    Department of Radiation Oncology, UT Health San Antonio, San Antonio, TX, USA.
  • Jiajie Zhou
    Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Yangzhou, 225001, China.
  • Qiannan Sun
    Injury Prevention Research Institute, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China.
  • Daorong Wang
    Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Yangzhou, 225001, China. wdaorong666@sina.com.

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