Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology


In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and ‘hand-crafted’ feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.

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Fig. 1: Milestones in computational pathology.
Fig. 2: Workflow and general framework for artificial intelligence (AI) approaches in digital pathology.
Fig. 3: Visual representations of hand-crafted features across cancer types.
Fig. 4: Artificial intelligence (AI) and machine learning approaches complement the expertise and support the pathologist and oncologist.


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Research discussed in this publication was supported by the Department of Defence and Department of Veterans, the National Cancer Institute of the National Institutes of Health, the National Centre for Research Resources, the Ohio Third Frontier Technology Validation Fund, and the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the institutions named.

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K.B. and A.M. researched data for the article and discussed the article contents. All authors wrote, reviewed and edited the manuscript before submission.

Correspondence to Anant Madabhushi.

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Competing interests

K.A.S. has received fees as a speaker for Merck and Takeda, is a consultant for Celgene, Moderna and Shattuck Labs, and receives research funding from Genoptix (Novartis), Onkaido, Pierre Fabre, Surface Oncology, Takeda, Tesaro and Tioma. D.L.R. is a consultant and research adviser to Agendia, Agilent, AstraZeneca, Biocept, BMS, Cell Signalling Technology, Cepheid, Merck, PAIGE, Perkin Elmer and Ultivue, owns equity in AstraZeneca, Cepheid, Lilly, Navigate–Novartis, NextCure, PixelGear and Ultivue, and receives research funding from Perkin Elmer. A.M. is a consultant and scientific adviser to Elucid Bioimaging and Inspirata, owns stock options in Elucid Bioimaging and Inspirata, and receives research funding from Philips. K.B. and V.V. declare no competing interests.

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