Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach.

Journal: PloS one
PMID: 40373044

Abstract

BACKGROUND: Prostate cancer is a common malignancy in men, and accurately distinguishing between benign and malignant nodules at an early stage is crucial for optimizing treatment. Multimodal imaging (such as ADC and T2) plays an important role in the diagnosis of prostate cancer, but effectively combining these imaging features for accurate classification remains a challenge.

Authors

  • Huadi Zhou
    Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang Province, China.
  • Mei Xie
    School of Electronic Engineering, University of Electronic Science and Technology of China, Xiyuan Ave. 2006, West Hi-Tech Zone, Chengdu, Sichuan, 611731, China. mxie@uestc.edu.cn.
  • Hemiao Shi
    Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang Province, China.
  • Chenhan Shou
    Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang Province, China.
  • Meng Tang
    Department of computer science, University of Waterloo, ON, Canada.
  • Yue Zhang
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Yue Hu
    Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Xiao Liu

Keywords

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