Unsupervised learning-based quantitative analysis of CT intratumoral subregions predicts risk stratification of bladder cancer patients.

Journal: BMC medicine
Published Date: Jun 2, 2025

Abstract

BACKGROUND: Preoperative diagnosis of muscle invasion and American Joint Committee on Cancer (AJCC) stage plays a crucial role in guiding treatment strategies for bladder cancer (BCa). Utilizing quantitative analysis of tumor subregions via CT imaging holds promise in identifying high-risk populations. Developing and evaluating the performance of an unsupervised clustering algorithm-based intratumoral subregion radiomics model for distinguishing between bladder muscle invasion and AJCC stage.

Authors

  • Ying Wang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Hexiang Wang
    Department of Radiology, The Affiliated Hospital of Qingdao University, Shinan Jiangsu 16 Rd, Qingdao, Shandong 266003, China.
  • Na Li
    School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Siyu Wu
    Department of Radiology, Shandong Provincial Hospital, No. 324, Jingwu Road, Jinan, 250021, Shandong, China.
  • Rongchao Shi
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Kui Sun
    Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Road, No. 324, Jinan, 250021, China.
  • Ximing Wang

Keywords

External Resources

Popular Topics
Recent Journals