Habitat Radiomics and Deep Learning Features Based on CT for Predicting Lymphovascular Invasion in T1-stage Lung Adenocarcinoma: A Multicenter Study.

Journal: Academic radiology
Published Date: Apr 18, 2025

Abstract

RATIONALE AND OBJECTIVES: The research aims to examine how CT-derived habitat radiomics can be used to predict lymphovascular invasion (LVI) in patients with T1-stage lung adenocarcinoma (LUAD), and compare its effectiveness to traditional radiomics and deep learning (DL) models.

Authors

  • Pengliang Xu
    Department of Thoracic Surgery, The First People's Hospital of Huzhou, Huzhou, China.
  • Fandi Yao
    Department of General Surgery, The First People's Hospital of Huzhou, Huzhou, China (F.Y.).
  • Yunyu Xu
    Department of Thoracic Surgery, The First People's Hospital of Huzhou, Huzhou, China (P.X., Y.X., H.Y., W.L., S.Z.).
  • Huanming Yu
    Department of Thoracic Surgery, The First People's Hospital of Huzhou, Huzhou, China.
  • Wenhui Li
    College of Computer Science and Technology, Jilin University.
  • Shengxu Zhi
    Department of Thoracic Surgery, The First People's Hospital of Huzhou, Huzhou, China.
  • Xiuhua Peng
    Department of Radiology, The First Affiliated Hospital of Huzhou Normal University, Huzhou, Zhejiang, People's Republic of China.

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