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

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

SUBMITTER: Klimov S 

PROVIDER: S-EPMC6664779 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature