Abstract
With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.
Highlights
Digital pathology refers to the process of digitization of tissue slides
In the context of breast cancer pathology, a number of computational imaging approaches have been recently applied for problems such as (i) detection of mitoses[13,14,15,16,17], tubules[18,19], nuclei[19,20], and lymphocytes[21], (ii) cancer grading[19,22], (iii) correlation of quantitative histologic image features and molecular features of breast cancer aggressiveness[23], and (iv) identification of histologic image features that are predictive of breast cancer outcome and survival[24]
The experimental results show that the method is able to detect invasive breast cancer regions on whole slide histopathology images with a high degree of precision, even when tested on cases from a cohort different to the one used for training
Summary
Digital pathology refers to the process of digitization of tissue slides. The process of slide digitization could enable more efficient storage, visualization, and pathologic analysis of tissue slides and could potentially improve overall efficiency of routine diagnostic pathology workflow[11]. In the context of breast cancer pathology, a number of computational imaging approaches have been recently applied for problems such as (i) detection of mitoses[13,14,15,16,17], tubules[18,19], nuclei[19,20], and lymphocytes[21], (ii) cancer grading[19,22], (iii) correlation of quantitative histologic image features and molecular features of breast cancer aggressiveness[23], and (iv) identification of histologic image features that are predictive of breast cancer outcome and survival[24]. The goal of this study was to quantitatively evaluate the accuracy and robustness of a deep learning based machine classifier to automatically identify the extent of invasive breast cancer on digitized whole slide images
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