Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

By Angel Cruz-Roa, Hannah Gilmore, Ajay Basavanhally, M. Feldman, Shridar Ganesan, Natalie N. C. Shih, John Tomaszewski, Fabio A. González, Anant Madabhushi. Scientific Reports, 2017

479 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biophysics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d5b91f292c611dea61f6e95b007ae53c2766a5f9

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Citations in the corpus

Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, background): “• Exclude papers focusing on detection (Cruz-Roa et al. 2017; Mina and Mat Isa 2015; Wang et al. 2014), segmentation (Chen et al. 2017a; Pan et al. 2017), cancer patient survivability (Jyh-Horng et al. 2014; Sivachitra and Vijayachitra 2015), or CAD analysis (Tan et al. 2013; van Zelst et al.…”
Artificial intelligence in radiology (2018, unknown): “Most deep learning efforts to detect lymph node involvement and distant metastasis — and ultimately obtain an accurate staging — have been carried out on pathology images 67,68 .”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “demonstrate that convolutional neural networks could be applied for accurately identifying cancer presence and extent on whole slide breast cancer pathology images [216].”
Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association (2019, methodology): “For example, algorithms have been described that identify images likely to contain tumor cells [21], compute mitotic counts [22], improve the accuracy and precision of immunohistochemistry scoring [23,24], or apply standardized histological scoring criteria such as the Gleason score [25], which can…”
Emerging role of deep learning‐based artificial intelligence in tumor pathology (2020, background): “Several DL-based networks have also been developed to recognize tumor regions in breast cancer [24, 44, 45].”