Applications and implications of whole-slide imaging in breast pathology

Published:December 28, 2021DOI:


      Technological advances in whole slide imaging (WSI) technology and artificial intelligence (AI) applications in recent years have resulted in increasing adoption of this paradigm shift technology. This brings with it many advantages, new challenges, and potential adaptations to the microscopic assessment of specimens that pathologists need to be aware of. This article describes the applications and implications of WSI within the context of the reporting of breast pathology specimens. Challenging diagnostic entities in digital breast pathology are presented and the key areas in which AI could be useful in breast pathology are highlighted.


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        • Rakha E.A.
        • Toss M.
        • Shiino S.
        • et al.
        Current and future applications of artificial intelligence in pathology: a clinical perspective.
        J Clin Pathol. 2021; 74: 409-414
        • Ibrahim A.
        • Gamble P.
        • Jaroensri R.
        • et al.
        Artificial intelligence in digital breast pathology: techniques and applications.
        Breast. 2020; 49: 267-273
        • Janowczyk A.
        • Zuo R.
        • Gilmore H.
        • Feldman M.
        • Madabhushi A.
        HistoQC: an open-source quality control tool for digital pathology slides.
        JCO Clin Cancer Inform. 2019; 3: 1-7
        • Pantanowitz L.
        • Michelow P.
        • Hazelhurst S.
        • et al.
        A digital pathology solution to resolve the tissue floater conundrum.
        Arch Pathol Lab Med. 2021; 145: 359-364
      1. A comprehensive and vendor-neutral solution for primary diagnostics in pathology [].

      2. Philips intellisite pathology solution - clinical digital pathology system [].

        • Abel J.T.
        • Ouillette P.
        • Williams C.L.
        • et al.
        Display characteristics and their impact on digital pathology: a current review of pathologists' future "microscope".
        J Pathol Inf. 2020; 11: 23
        • Clarke E.L.
        • Munnings C.
        • Williams B.
        • Brettle D.
        • Treanor D.
        Display evaluation for primary diagnosis using digital pathology.
        J Med Imaging (Bellingham). 2020; 7027501
        • Clarke E.L.
        • Brettle D.
        • Sykes A.
        • Wright A.
        • Boden A.
        • Treanor D.
        Development and evaluation of a novel point-of-use quality assurance tool for digital pathology.
        Arch Pathol Lab Med. 2019; 143: 1246-1255
        • Corista
        Corista receives patent grant for new virtual slide stage.
        • Borowsky A.D.
        • Glassy E.F.
        • Wallace W.D.
        • et al.
        Digital whole slide imaging compared with light microscopy for primary diagnosis in surgical pathology.
        Arch Pathol Lab Med. 2020; 144: 1245-1253
        • Hanna M.G.
        • Reuter V.E.
        • Hameed M.R.
        • et al.
        Whole slide imaging equivalency and efficiency study: experience at a large academic center.
        Mod Pathol. 2019; 32: 916-928
        • Araujo A.L.D.
        • Arboleda L.P.A.
        • Palmier N.R.
        • et al.
        The performance of digital microscopy for primary diagnosis in human pathology: a systematic review.
        Virchows Arch. 2019; 474: 269-287
        • Williams B.J.
        • DaCosta P.
        • Goacher E.
        • Treanor D.
        A systematic analysis of discordant diagnoses in digital pathology compared with light microscopy.
        Arch Pathol Lab Med. 2017; 141: 1712-1718
        • Azam A.S.
        • Miligy I.M.
        • Kimani P.K.
        • et al.
        Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis.
        J Clin Pathol. 2021; 74: 448-455
        • Williams B.J.
        • Hanby A.
        • Millican-Slater R.
        • Nijhawan A.
        • Verghese E.
        • Treanor D.
        Digital pathology for the primary diagnosis of breast histopathological specimens: an innovative validation and concordance study on digital pathology validation and training.
        Histopathology. 2018; 72: 662-671
        • Williams B.
        • Hanby A.
        • Millican-Slater R.
        • et al.
        Digital pathology for primary diagnosis of screen-detected breast lesions - experimental data, validation and experience from four centres.
        Histopathology. 2020; 76: 968-975
      3. Is the use of digital pathology in routine diagnosis reliable and safe in comparison to standard microscopy? [].

      4. Best practice recommendations for implementing digital pathology [].

        • Stathonikos N.
        • Veta M.
        • Huisman A.
        • van Diest P.J.
        Going fully digital: perspective of a Dutch academic pathology lab.
        J Pathol Inf. 2013; 4: 15
        • Cree I.A.
        • Tan P.H.
        • Travis W.D.
        • et al.
        Counting mitoses: SI(ze) matters!.
        Mod Pathol. 2021; 34: 1651-1657
        • Tan P.H.
        • Ellis I.
        • Allison K.
        • et al.
        The 2019 World Health Organization classification of tumours of the breast.
        Histopathology. 2020; 77: 181-185
        • Stathonikos N.
        • van Varsseveld N.C.
        • Vink A.
        • et al.
        Digital pathology in the time of corona.
        J Clin Pathol. 2020; 73: 706-712
        • Hanna M.G.
        • Reuter V.E.
        • Samboy J.
        • et al.
        Implementation of digital pathology offers clinical and operational increase in efficiency and cost savings.
        Arch Pathol Lab Med. 2019; 143: 1545-1555
        • NHSBSP
        Reporting, recording and auditing B5 core biopsies with normal/benign surgery.
        • Browning L.
        • Colling R.
        • Rittscher J.
        • Winter L.
        • McEntyre N.
        • Verrill C.
        Implementation of digital pathology into diagnostic practice: perceptions and opinions of histopathology trainees and implications for training.
        J Clin Pathol. 2020; 73: 223-227
      5. [].

        • Pantanowitz L.
        • Quiroga-Garza G.M.
        • Bien L.
        • et al.
        An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study.
        Lancet Digit Health. 2020; 2: e407-e416
        • Rawat R.R.
        • Ortega I.
        • Roy P.
        • et al.
        Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images.
        Sci Rep. 2020; 10: 7275
        • Baltres A.
        • Al Masry Z.
        • Zemouri R.
        • et al.
        Prediction of Oncotype DX recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, HER2-negative breast cancer.
        Breast Cancer. 2020; 27: 1007-1016