Dataset Information

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 Hospital, UK.

Methods

The cohort was split case-wise into training (n?=?159, 31 with 10-year recurrence) and validation (n?=?185, 26 with 10-year recurrence) sets. The sections from primary tumors were stained with H&E, then digitized and analyzed by the pipeline. In the first step, a classifier trained manually by pathologists was applied to digital slides to annotate the areas of stroma, normal/benign ducts, cancer ducts, dense lymphocyte region, and blood vessels. In the second step, a recurrence risk classifier was trained on eight select architectural and spatial organization tissue features from the annotated areas to predict recurrence risk.

Results

The recurrence classifier significantly predicted the 10-year recurrence risk in the training [hazard ratio (HR)?=?11.6; 95% confidence interval (CI) 5.3-25.3, accuracy (Acc)?=?0.87, sensitivity (Sn)?=?0.71, and specificity (Sp)?=?0.91] and independent validation [HR?=?6.39 (95% CI 3.0-13.8), p?ConclusionsOur machine learning-based model fills an unmet clinical need for accurately predicting the recurrence risk for lumpectomy-treated DCIS patients.

SUBMITTER: Klimov S

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

REPOSITORIES: biostudies-literature

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Publications

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

Klimov Sergey S Miligy Islam M IM Gertych Arkadiusz A Jiang Yi Y Toss Michael S MS Rida Padmashree P Ellis Ian O IO Green Andrew A Krishnamurti Uma U Rakha Emad A EA Aneja Ritu R

Breast cancer research : BCR 20190729 1

<h4>Background</h4>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 is ...[more]

PMID: 31358020

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Dataset Information

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

Background

Methods

Results

Publications

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

Similar Datasets

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