Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.

Journal: European journal of radiology
PMID: 40147164

Abstract

OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers associated with poor pathology, ultimately developing a multi-omics marker model for better clinical risk stratification.

Authors

  • Wenhao Zhu
    School of Computer Engineering and Science, Shanghai University, Shanghai, China.
  • Yongxiang Tang
    Department of Nuclear Medicine, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, Hunan, 410008, P.R. China. xyyf0401@qq.com.
  • Lin Qi
    a Sino-Dutch Biomedical and Information Engineering School , Northeastern University , Shenyang , Liaoning , China.
  • Xiaomei Gao
    Department of Pathology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, PR China.
  • Shuo Hu
    School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China.
  • Min-Feng Chen
    Guangdong Province Key Laboratory of Pharmacodynamic Constituents of Traditional Chinese Medicine and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, China.
  • Yi Cai
    College of Veterinary Medicine, Hebei Agricultural University, Baoding, Hebei 071000, China.

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