Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

Journal: Journal of magnetic resonance imaging : JMRI
PMID: 39167019

Abstract

BACKGROUND: Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance.

Authors

  • Haolin Huang
    School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, China.
  • Yiping Huang
    Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Joshua D Kaggie
    Department of Radiology, University of Cambridge, Cambridge, United Kingdom. Electronic address: jk636@cam.ac.uk.
  • Qian Cai
    Department of Otolaryngology-Head and Neck, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Peng Yang
  • Jie Wei
    Department of Computer Science, City College of New York, New York, USA.
  • Lijuan Wang
    HBISolutions Inc., Palo Alto, CA 94301, USA.
  • Yan Guo
    State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China.
  • Hongbing Lu
    The Fourth Medical University, Department of of Biomedical Engineering, Xi'an, China.
  • Huanjun Wang
    Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. wanghj45@mail.sysu.edu.cn.
  • Xiaopan Xu
    School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China.

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