Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer.

Journal: Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PMID: 39046568

Abstract

PURPOSE: Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor outcomes in ovarian cancer. Assessing muscle radiodensity is a real-world clinical challenge owing to the requirement for computed tomography (CT) with consistent protocols and labor-intensive processes. This study aimed to use interpretable machine learning (ML) to predict muscle radiodensity loss.

Authors

  • Wan-Chun Lin
    Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Beitou District, Taipei, 112304, Taiwan.
  • Chia-Sui Weng
    Department of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei, Taiwan.
  • Ai-Tung Ko
    Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Beitou District, Taipei, 112304, Taiwan.
  • Ya-Ting Jan
    Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan.
  • Jhen-Bin Lin
    Department of Radiation Oncology, Changhua Christian Hospital, Changhua, Taiwan.
  • Kun-Pin Wu
    Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan.
  • Jie Lee
    Department of Medicine, MacKay Medical College, New Taipei City, Taiwan. sinus.5706@mmh.org.tw.

Keywords

External Resources

Popular Topics
Recent Journals