Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

Journal: International journal of medical informatics
PMID: 38678674

Abstract

OBJECTIVES: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF.

Authors

  • Binh D Le
    Department of Biomedical Sciences, Chonnam National University, Hwasun-gun, Jeollanam-do, South Korea; Department of Urology, Saint Paul Hospital, Hanoi, Viet Nam.
  • Sook Hee Heo
    Department of Radiology, Chonnam National University, Gwangju, South Korea; Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun-gun, Jeollanam-do, South Korea.
  • Ho Seok Chung
    Department of Urology, Chonnam National University Medical School, Gwangju, Republic of Korea. hschung615@gmail.com.
  • Ilwoo Park
    Department of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Korea.

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