Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.

Journal: Cancer medicine
PMID: 39166457

Abstract

BACKGROUND: Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time-consuming and expensive high-throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significant challenges in biology and clinical settings. Our aimed to develop a self-supervised attention-based multiple instance learning (SSL-ABMIL) model to predict TMB and VHL mutation status from hematoxylin and eosin-stained histopathological images.

Authors

  • Qingyuan Zheng
    Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
  • Xinyu Wang
    Department of Endocrinology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.
  • Rui Yang
    Department of Biomedical Informatics, Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore.
  • Junjie Fan
  • Jingping Yuan
    Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China.
  • Xiuheng Liu
    Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Zhuoni Xiao
    Centre for Reproductive Science, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
  • Zhiyuan Chen
    Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.

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