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

Journal: Nature reviews. Clinical oncology
Published Date: Aug 9, 2019

Abstract

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.

Authors

  • Kaustav Bera
    Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
  • Kurt A Schalper
    Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
  • David L Rimm
    Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
  • Vamsidhar Velcheti
    Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
  • Anant Madabhushi
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.

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