Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E-stained images for ovarian cancer prognosis.

Journal: Journal of translational medicine
PMID: 39856778

Abstract

BACKGROUND: The ability to predict the prognosis of patients with ovarian cancer can greatly improve disease management. However, the knowledge on the mechanism of the prediction is limited. We sought to deconvolute the attention feature learnt by a deep learning convolutional neural networks trained with whole-slide images (WSIs) of hematoxylin-and-eosin (H&E)-stained tumor samples using spatial transcriptomic data.

Authors

  • Chun Wai Ng
    Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
  • Kwong-Kwok Wong
    Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA. kkwong@mdanderson.org.
  • Barrett C Lawson
    Department of Anatomical Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
  • Sammy Ferri-Borgogno
    Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA. sferri@mdanderson.org.
  • Samuel C Mok
    Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA. scmok@mdanderson.org.

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