Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Feb 19, 2025

Abstract

PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [F]PSMA-1007 PET/CT and multiparametric MRI (mpMRI) to predict the presence of AP, and investigate whether the model that integrates [F]PSMA-1007 PET/CT and mpMRI outperforms the individual PET/CT or mpMRI models in predicting AP.

Authors

  • Heng Lin
    The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
  • Fei Yao
    The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
  • Xinwen Yi
    The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
  • Yaping Yuan
    The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
  • Jian Xu
    Department of Cardiology, Lishui Central Hospital and the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
  • Lixuan Chen
  • Hongyan Wang
    State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200432, China.
  • Yuandi Zhuang
    The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
  • Qi Lin
    Shandong University, School of Mechanical, Electrical and Information Engineering, Weihai, 264209, China. Electronic address: 202100800115@mail.sdu.edu.cn.
  • Yingnan Xue
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Yunjun Yang
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China. yyjunjim@163.com.
  • Zhifang Pan
    Information Technology Center, Wenzhou Medical University, 325035, China.

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