A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk.

Journal: Breast cancer research : BCR
Published Date: Jul 29, 2019

Abstract

BACKGROUND: Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopathological markers are insufficient to accurately assess the recurrence risk. To address this issue, we developed a novel machine learning (ML) pipeline to predict risk of ipsilateral recurrence using digitized whole slide images (WSI) and clinicopathologic long-term outcome data from a retrospectively collected cohort of DCIS patients (n = 344) treated with lumpectomy at Nottingham University Hospital, UK.

Authors

  • Sergey Klimov
    Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Islam M Miligy
    Department of Cellular Pathology, University of Nottingham, Nottingham, UK.
  • Arkadiusz Gertych
  • Yi Jiang
    Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou 325035, China.
  • Michael S Toss
    Department of Cellular Pathology, University of Nottingham, Nottingham, UK.
  • Padmashree Rida
    Department of Biology, Georgia State University, Atlanta, GA, 30303, USA.
  • Ian O Ellis
  • Andrew Green
    Department of Cellular Pathology, University of Nottingham, Nottingham, UK.
  • Uma Krishnamurti
    Department of Pathology, Emory University, Atlanta, GA, USA.
  • Emad A Rakha
    Department of Cellular Pathology, University of Nottingham, Nottingham, UK. Emad.Rakha@Nottingham.Ac.Uk.
  • Ritu Aneja
    Department of Biology, Georgia State University, Atlanta, GA, 30303, USA. raneja@gsu.edu.

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