Deep Learning Algorithm‑Based MRI Radiomics and Pathomics for Predicting Microsatellite Instability Status in Rectal Cancer: A Multicenter Study.

Journal: Academic radiology
PMID: 39289097

Abstract

RATIONALE AND OBJECTIVES: To develop and validate multimodal deep-learning models based on clinical variables, multiparametric MRI (mp-MRI) and hematoxylin and eosin (HE) stained pathology slides for predicting microsatellite instability (MSI) status in rectal cancer patients.

Authors

  • Xiuzhen Yao
    Department of Ultrasound, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China (X.Y.).
  • Shuitang Deng
    Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234 Gucui Road, Zhejiang Province, 310012, Hangzhou, China.
  • Xiaoyu Han
    Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Laboratory Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Danjiang Huang
    Department of Radiology, Taizhou First People's Hospital, Taizhou, Zhejiang, China.
  • Zhengyu Cao
    Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.
  • Xiaoxiang Ning
    Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China (S.D., Z.C., X.N., W.A.).
  • Weiqun Ao
    Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234 Gucui Road, Zhejiang Province, 310012, Hangzhou, China. 78123858@qq.com.

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