Deep Learning and Radiomic Signatures Associated with Tumor Immune Heterogeneity Predict Microvascular Invasion in Colon Cancer.

Journal: Academic radiology
Published Date: May 23, 2025

Abstract

RATIONALE AND OBJECTIVES: This study aims to develop and validate a deep learning radiomics signature (DLRS) that integrates radiomics and deep learning features for the non-invasive prediction of microvascular invasion (MVI) in patients with colon cancer (CC). Furthermore, the study explores the potential association between DLRS and tumor immune heterogeneity.

Authors

  • Jianye Jia
    Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
  • Jiahao Wang
    Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, 1301 MSRB III, 1150 W. Medical Dr, Ann Arbor, MI, 48109, USA.
  • Yongxian Zhang
    Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 DongJiaoMinXiang Street, DongCheng District, Beijing 100730, China (Y.Z.).
  • Genji Bai
    Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
  • Lei Han
    Jiangsu Province Center for Disease Prevention and Control, Institute of Occupational Disease Prevention, Nanjing, Jiangsu, China.
  • Yantao Niu
    Beijing Tongren Hospital, CMU, Beijing, China.

Keywords

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