Artificial intelligence applied to breast pathology
Yousif, Mustafa; van Diest, Paul J.; Laurinavicius, Arvydas; Rimm, David; van der Laak, Jeroen; Madabhushi, Anant; Schnitt, Stuart; Pantanowitz, Liron
(2022) Virchows Archiv, volume 480, issue 1, pp. 191 - 209
(Article)
Abstract
The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns
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from labeled digital data based on “deep learning” neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.
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Keywords: Artificial intelligence, Breast, Breast cancer, Computational pathology, Convolutional neural network, Deep learning, Handcrafted features, Machine learning, Quantitative image analysis, Molecular Biology, Pathology and Forensic Medicine, Cell Biology
ISSN: 0945-6317
Publisher: Springer Verlag
Note: Funding Information: Paul J. van Diest serves on the scientific advisory of Sectra (non-paid). Arvydas Laurinavicius is an independent scientific advisor (non-paid) to the portal https://pathologynews.com/ , and a co-author on international patent application (no commercial interest). Anant Madabhushi is an equity holder in Elucid Bioimaging and in Inspirata Inc. In addition, Madabhushi has served as a scientific advisory board member for Inspirata Inc., Astrazeneca, Bristol Meyers-Squibb, and Merck. Currently, he serves on the advisory board of Aiforia Inc., has sponsored research agreements with Philips, AstraZeneca, Boehringer-Ingelheim, and Bristol Meyers-Squibb. Madabhushi’s technology has been licensed to Elucid Bioimaging and he is also involved in a NIH U24 grant with PathCore Inc, and 3 different R01 grants with Inspirata Inc. Liron Pantanowitz is on the scientific advisory board for Ibex and NTP and serves as a consultant for Hamamatsu. David L. Rimm has served as an advisor for Astra Zeneca, Agendia, Amgen, BMS, Cell Signaling Technology, Cepheid, Danaher, Daiichi Sankyo, Konica Minolta, Merck, NanoString, PAIGE.AI, Perkin Elmer, Roche, Sanofi, Ventana, and Ultivue. Amgen, Cepheid, NavigateBP, NextCure, and Konica Minolta fund research in David L. Rimm’s lab. Stuart J. Schnitt is on the scientific advisory boards of PathAI and Ibex. Jeroen van der Laak is a member of the advisory boards of Philips, The Netherlands, and ContextVision, Sweden, and received research funding from Philips, The Netherlands; ContextVision, Sweden; and Sectra, Sweden in the last five years. Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
(Peer reviewed)