Development and Validation of a Biparametric MRI Deep Learning Radiomics Model with Clinical Characteristics for Predicting Perineural Invasion in Patients with Prostate Cancer.

Journal: Academic radiology
PMID: 39043515

Abstract

RATIONALE AND OBJECTIVES: Perineural invasion (PNI) is an important prognostic biomarker for prostate cancer (PCa). This study aimed to develop and validate a predictive model integrating biparametric MRI-based deep learning radiomics and clinical characteristics for the non-invasive prediction of PNI in patients with PCa.

Authors

  • Yue-Yue Zhang
    Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China; Department of Radiology, Second Hospital of Soochow University, Suzhou 215004, China.
  • Hui-Min Mao
    Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China.
  • Chao-Gang Wei
    Department of Radiology, Second Hospital of Soochow University, Suzhou 215004, China.
  • Tong Chen
    Centre for Experimental Studies and Research, the first Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
  • Wen-Lu Zhao
    Department of Radiology, Second Hospital of Soochow University, Suzhou 215004, China.
  • Liang-Yan Chen
    Department of Pathology, Second Hospital of Soochow University, Suzhou 215004, China.
  • Jun-Kang Shen
    Department of Radiology, Second Hospital of Soochow University, Suzhou 215004, China.
  • Wan-Liang Guo
    Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China.

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