Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer.

Journal: PloS one
Published Date:

Abstract

PURPOSE: Radical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could enable more aggressive surgical approaches, shorter surveillance intervals, and intensified adjuvant treatments. This study aims to analyze lung cancer sites in CT images to predict potential recurrences in high-risk individuals.

Authors

  • Jihwan Park
    School of Software Convergence, College of Software Convergence, Dankook University, Korea.
  • Mi Jung Rho
    College of Health Science, Dankook University, Cheonan-si, Korea.
  • Mi Hyoung Moon
    Department of Thoracic and Cardiovascular Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.