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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Project description:Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Whole-slide imaging (WSI), i.e., the scanning and digitization of entire histology slides, are now being adopted across the world in pathology labs. Trained histopathologists can provide an accurate diagnosis of biopsy specimens based on WSI data. Given the dimensionality of WSIs and the increase in the number of potential cancer cases, analyzing these images is a time-consuming process. Automated segmentation of tumorous tissue helps in elevating the precision, speed, and reproducibility of research. In the recent past, deep learning-based techniques have provided state-of-the-art results in a wide variety of image analysis tasks, including the analysis of digitized slides. However, deep learning-based solutions pose many technical challenges, including the large size of WSI data, heterogeneity in images, and complexity of features. In this study, we propose a generalized deep learning-based framework for histopathology tissue analysis to address these challenges. Our framework is, in essence, a sequence of individual techniques in the preprocessing-training-inference pipeline which, in conjunction, improve the efficiency and the generalizability of the analysis. The combination of techniques we have introduced includes an ensemble segmentation model, division of the WSI into smaller overlapping patches while addressing class imbalances, efficient techniques for inference, and an efficient, patch-based uncertainty estimation framework. Our ensemble consists of DenseNet-121, Inception-ResNet-V2, and DeeplabV3Plus, where all the networks were trained end to end for every task. We demonstrate the efficacy and improved generalizability of our framework by evaluating it on a variety of histopathology tasks including breast cancer metastases (CAMELYON), colon cancer (DigestPath), and liver cancer (PAIP). Our proposed framework has state-of-the-art performance across all these tasks and is ranked within the top 5 currently for the challenges based on these datasets. The entire framework along with the trained models and the related documentation are made freely available at GitHub and PyPi. Our framework is expected to aid histopathologists in accurate and efficient initial diagnosis. Moreover, the estimated uncertainty maps will help clinicians to make informed decisions and further treatment planning or analysis.

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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