Advertisement
Surgical Pathology Clinics

Applications of Digital and Computational Pathology and Artificial Intelligence in Genitourinary Pathology Diagnostics

  • Ankush Uresh Patel
    Affiliations
    Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
    Search for articles by this author
  • Author Footnotes
    1 Present address: CORE Diagnostics (India), 406, Phase III, Udyog Vihar, Sector 19, Gurugram, Haryana 122016, India.
    Sambit K. Mohanty
    Footnotes
    1 Present address: CORE Diagnostics (India), 406, Phase III, Udyog Vihar, Sector 19, Gurugram, Haryana 122016, India.
    Affiliations
    Surgical and Molecular Pathology, Advanced Medical Research Institute, Plot No. 1, Near Jayadev Vatika Park, Khandagiri, Bhubaneswar, Odisha 751019
    Search for articles by this author
  • Anil V. Parwani
    Correspondence
    Corresponding author.
    Affiliations
    Department of Pathology, The Ohio State University, Cooperative Human Tissue Network (CHTN) Midwestern Division Polaris Innovation Centre, 2001 Polaris Parkway Suite 1000, Columbus, OH 43240, USA
    Search for articles by this author
  • Author Footnotes
    1 Present address: CORE Diagnostics (India), 406, Phase III, Udyog Vihar, Sector 19, Gurugram, Haryana 122016, India.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Surgical Pathology Clinics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Park S.
        • Parwani A.V.
        • Aller R.D.
        • et al.
        The history of pathology informatics: A global perspective.
        J Pathol Inform. 2013; 4: 7
        • Pantanowitz L.
        • Sharma A.
        • Carter A.B.
        • et al.
        Twenty years of digital pathology: An overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives.
        J Pathol Inform. 2018; 9: 40
        • Eloy C.
        • Bychkov A.
        • Pantanowitz L.
        • et al.
        DPA–ESDIP–JSDP task force for worldwide adoption of digital pathology.
        J Pathol Inform. 2021; 12: 51
        • Dangott B.
        Whole slide image analysis.
        in: Parwani A.V. Whole slide imaging. Springer, Switzerland2022: 203-221
        • Nir G.
        • Hor S.
        • Karimi D.
        • et al.
        Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts.
        Med Image Anal. 2018; 50: 167-180
        • Litjens G.
        • Sanchez C.I.
        • Timofeeva N.
        • et al.
        Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.
        Sci Rep. 2016; 6: 26286
        • Li C.
        • Li X.
        • Rahaman M.
        • et al.
        A comprehensive review of computer-aided whole-slide image analysis: From datasets to feature extraction, segmentation, classification, and detection approaches. arXiv. Preprint posted online February 21, 2021.
        (Available at:) (Accessed January 7, 2022)
      1. Paige Receives First Ever FDA Approval for AI Product in Digital Pathology. 2021.
        (Available at:)
        • Campanella G.
        • Hanna M.G.
        • Geneslaw L.
        • et al.
        Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.
        Nat Med. 2019; 25: 1301-1309
        • Raciti P.
        • Sue J.
        • Ceballos R.
        • et al.
        Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies.
        Mod Pathol. 2020; 33: 2058-2066
        • da Silva L.M.
        • Pereira E.M.
        • Salles P.G.
        • et al.
        Independent real-world application of a clinical-grade automated prostate cancer detection system.
        J Pathol. 2021; 254: 147-158
      2. Ibex Medical Analytics. First U.S. Lab implements AI-based solution for cancer detection in pathology. PR Newswire. Updated September 1.
        (Available at:) (Accessed January 30, 2022)
        • Miller K.D.
        • Goding Sauer A.
        • Ortiz A.P.
        • et al.
        Cancer Statistics for Hispanics/Latinos, 2018.
        CA Cancer J Clin. 2018; 68: 425-445
        • 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.
        The Lancet Digital Health. 2020; 2: e407-e416
      3. Laifenfeld D, Sandbank J, Linhart C, et al. Performance of an AI-based cancer diagnosis system in France's largest network of pathology institutes. 2019:S177-S178.

        • Laifenfeld D.
        • Vecsler M.
        • Raoux D.
        • et al.
        AI-Based Solution for Cancer Diagnosis in Prostate Core Needle Biopsies: A Prospective Blinded Multi-Site Clinical Study.
        Lab Invest. 2021; 101: 580-581
        • Comperat E.
        • Rioux-Leclercq N.
        • Levrel O.
        • et al.
        Clinical level AI-based solution for primary diagnosis and reporting of prostate biopsies in routine use: a prospective reader study.
        Virchows Archiv. 2021; 479: S60-S61
        • Raoux D.
        • Sebag G.
        • Yazbin I.
        • et al.
        Novel AI based solution for supporting primary diagnosis of prostate cancer increases the accuracy and efficiency of reporting in clinical routine presented at: USCP 2021; 2021.
        (Available at:) (Accessed January 15, 2021)
        • Lucas M.
        • Jansen I.
        • Savci-Heijink C.D.
        • et al.
        Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.
        Virchows Arch. 2019; 475: 77-83
        • Esteban A.E.
        • Lopez-Perez M.
        • Colomer A.
        • et al.
        A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes.
        Comput Methods Programs Biomed. 2019; 178: 303-317
        • Kott O.
        • Linsley D.
        • Amin A.
        • et al.
        Development of a deep learning algorithm for the histopathologic diagnosis and gleason grading of prostate cancer biopsies: A pilot study.
        Eur Urol Focus. 2021; 7: 347-351
        • Nguyen T.H.
        • Sridharan S.
        • Macias V.
        • et al.
        Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning.
        J Biomed Opt. 2017; 22: 36015
        • Arvaniti E.
        • Fricker K.S.
        • Moret M.
        • et al.
        Automated Gleason grading of prostate cancer tissue microarrays via deep learning.
        Sci Rep. 2018; 8: 12054
        • Nir G.
        • Karimi D.
        • Goldenberg S.L.
        • et al.
        Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images.
        JAMA Netw Open. 2019; 2: e190442
        • Doyle S.
        • Feldman M.
        • Tomaszewski J.
        • et al.
        A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies.
        IEEE Trans Biomed Eng. 2012; 59: 1205-1218
        • Monaco J.P.
        • Tomaszewski J.E.
        • Feldman M.D.
        • et al.
        High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models.
        Med Image Anal. 2010; 14: 617-629
        • Gorelick L.
        • Veksler O.
        • Gaed M.
        • et al.
        Prostate histopathology: learning tissue component histograms for cancer detection and classification.
        IEEE Trans Med Imaging. 2013; 32: 1804-1818
        • Kothari S.
        • Phan J.H.
        • Stokes T.H.
        • et al.
        Pathology imaging informatics for quantitative analysis of whole-slide images.
        J Am Med Inform Assoc. 2013; 20: 1099-1108
      4. Schaumberg A, Rubin M, Fuchs T. H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer. 2018.

        • Somanchi S.
        • Neill D.B.
        • Parwani A.V.
        Discovering anomalous patterns in large digital pathology images.
        Stat Med. 2018; 37: 3599-3615
        • Strom P.
        • Kartasalo K.
        • Olsson H.
        • et al.
        Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.
        Lancet Oncol. 2020; 21: 222-232
        • Han W.
        • Johnson C.
        • Gaed M.
        • et al.
        Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens.
        Sci Rep. 2020; 10: 9911
        • Tolkach Y.
        • Dohmgörgen T.
        • Toma M.
        • et al.
        High-accuracy prostate cancer pathology using deep learning.
        Nat Machine Intelligence. 2020; 2: 411-418
        • Zelic R.
        • Giunchi F.
        • Lianas L.
        • et al.
        Interchangeability of light and virtual microscopy for histopathological evaluation of prostate cancer.
        Sci Rep. 2021; 11: 3257
        • Singh M.
        • Kalaw E.M.
        • Jie W.
        • et al.
        Cribriform pattern detection in prostate histopathological images using deep learning models.
        arXiv. 2019; 1910: 04030
        • Leo P.
        • Chandramouli S.
        • Farre X.
        • et al.
        Computationally derived cribriform area index from prostate cancer hematoxylin and eosin images is associated with biochemical recurrence following radical prostatectomy and is most prognostic in gleason grade group 2.
        Eur Urol Focus. 2021; 7: 722-732
        • Choi H.K.
        • Jarkrans T.
        • Bengtsson E.
        • et al.
        Image analysis based grading of bladder carcinoma. Comparison of object, texture and graph based methods and their reproducibility.
        Anal Cell Pathol. 1997; 15: 1-18
        • Spyridonos P.
        • Cavouras D.
        • Ravazoula P.
        • et al.
        Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma.
        Anal Quant Cytol Histol. 2002; 24: 317-324
        • Jansen I.
        • Lucas M.
        • Bosschieter J.
        • et al.
        Automated Detection and Grading of Non-Muscle-Invasive Urothelial Cell Carcinoma of the Bladder.
        Am J Pathol. 2020; 190: 1483-1490
        • Linder N.
        • Taylor J.C.
        • Colling R.
        • et al.
        Deep learning for detecting tumour-infiltrating lymphocytes in testicular germ cell tumours.
        J Clin Pathol. 2019; 72: 157-164
        • Bhalla S.
        • Chaudhary K.
        • Kumar R.
        • et al.
        Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer.
        Sci Rep. 2017; 7: 44997
        • Li F.
        • Yang M.
        • Li Y.
        • et al.
        An improved clear cell renal cell carcinoma stage prediction model based on gene sets.
        BMC Bioinformatics. 2020; 21: 232
        • Giulietti M.
        • Cecati M.
        • Sabanovic B.
        • et al.
        The role of artificial intelligence in the diagnosis and prognosis of renal cell tumors.
        Diagnostics (Basel). 2021; 11: 206
        • Fenstermaker M.
        • Tomlins S.A.
        • Singh K.
        • et al.
        Development and validation of a deep-learning model to assist with renal cell carcinoma histopathologic interpretation.
        Urology. 2020; 144: 152-157
        • Tabibu S.
        • Vinod P.K.
        • Jawahar C.V.
        Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning.
        Sci Rep. 2019; 9: 10509
        • Singh N.P.
        • Bapi R.S.
        • Vinod P.K.
        Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma.
        Comput Biol Med. 2018; 100: 92-99
        • Singh N.P.
        • Vinod P.K.
        Integrative analysis of DNA methylation and gene expression in papillary renal cell carcinoma.
        Mol Genet Genomics. 2020; 295: 807-824
        • Kim H.
        • Lee S.J.
        • Park S.J.
        • et al.
        Machine learning approach to predict the probability of recurrence of renal cell carcinoma after surgery: Prediction model development study.
        JMIR Med Inform. 2021; 9: e25635
        • Cheng J.
        • Han Z.
        • Mehra R.
        • et al.
        Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma.
        Nat Commun. 2020; 11: 1778
        • Bulten W.
        • Kartasalo K.
        • Chen P.-H.C.
        • et al.
        Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.
        Nat Med. 2022; 28: 154-163
        • Kang L.
        • Li X.
        • Zhang Y.
        • et al.
        Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining.
        Photoacoustics. 2022; 25: 100308
        • Liu Y.
        • Levenson R.M.
        • Jenkins M.W.
        Slide over: Advances in slide-free optical microscopy as drivers of diagnostic pathology.
        Am J Pathol. 2022; https://doi.org/10.1016/j.ajpath.2021.10.010
        • Tampu I.E.
        • Maintz M.
        • Koller D.
        • et al.
        Optical coherence tomography for thyroid pathology: 3D analysis of tissue microstructure.
        Biomed Opt Express. 2020; 11: 4130-4149
        • Glaser A.K.
        • Reder N.P.
        • Chen Y.
        • et al.
        Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues.
        Nat Commun. 2019; 10: 2781
        • Xie W.
        • Reder N.P.
        • Koyuncu C.
        • et al.
        Prostate cancer risk stratification via non-destructive 3D pathology with annotation-free gland segmentation and analysis.
        medRxiv. 2021; https://doi.org/10.1101/2021.08.30.21262847
        • Glaser A.K.
        • Reder N.P.
        • Chen Y.
        • et al.
        Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens.
        Nat Biomed Eng. 2017; 1: 0084
        • Salto-Tellez M.
        • Maxwell P.
        • Hamilton P.
        Artificial intelligence—the third revolution in pathology.
        Histopathology. 2019; 74: 372-376
      5. Paxton A. Quantitative image analysis: In guideline, preliminary rules for pathology’s third revolution. Cap Today2019.

        • Fraggetta F.
        • Caputo A.
        • Guglielmino R.
        • et al.
        A Survival Guide for the Rapid Transition to a Fully Digital Workflow: The "Caltagirone Example.
        Diagnostics (Basel). 2021; : 11https://doi.org/10.3390/diagnostics11101916
        • Chen A.B.
        • Haque T.
        • Roberts S.
        • et al.
        Artificial intelligence applications in urology: Reporting standards to achieve fluency for urologists.
        Urol Clin North Am. 2022; 49: 65-117
        • Topol E.J.
        High-performance medicine: the convergence of human and artificial intelligence.
        Nat Med. 2019; 25: 44-56