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      Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

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      Annual Review of Biomedical Engineering

      Annual Reviews

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          Abstract

          Pathology is essential for research in disease and development, as well as for clinical decision making. For more than 100 years, pathology practice has involved analyzing images of stained, thin tissue sections by a trained human using an optical microscope. Technological advances are now driving major changes in this paradigm toward digital pathology (DP). The digital transformation of pathology goes beyond recording, archiving, and retrieving images, providing new computational tools to inform better decision making for precision medicine. First, we discuss some emerging innovations in both computational image analytics and imaging instrumentation in DP. Second, we discuss molecular contrast in pathology. Molecular DP has traditionally been an extension of pathology with molecularly specific dyes. Label-free, spectroscopic images are rapidly emerging as another important information source, and we describe the benefits and potential of this evolution. Third, we describe multimodal DP, which is enabled by computational algorithms and combines the best characteristics of structural and molecular pathology. Finally, we provide examples of application areas in telepathology, education, and precision medicine. We conclude by discussing challenges and emerging opportunities in this area.

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          Most cited references 142

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          A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.

          The likelihood of distant recurrence in patients with breast cancer who have no involved lymph nodes and estrogen-receptor-positive tumors is poorly defined by clinical and histopathological measures. We tested whether the results of a reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay of 21 prospectively selected genes in paraffin-embedded tumor tissue would correlate with the likelihood of distant recurrence in patients with node-negative, tamoxifen-treated breast cancer who were enrolled in the National Surgical Adjuvant Breast and Bowel Project clinical trial B-14. The levels of expression of 16 cancer-related genes and 5 reference genes were used in a prospectively defined algorithm to calculate a recurrence score and to determine a risk group (low, intermediate, or high) for each patient. Adequate RT-PCR profiles were obtained in 668 of 675 tumor blocks. The proportions of patients categorized as having a low, intermediate, or high risk by the RT-PCR assay were 51, 22, and 27 percent, respectively. The Kaplan-Meier estimates of the rates of distant recurrence at 10 years in the low-risk, intermediate-risk, and high-risk groups were 6.8 percent (95 percent confidence interval, 4.0 to 9.6), 14.3 percent (95 percent confidence interval, 8.3 to 20.3), and 30.5 percent (95 percent confidence interval, 23.6 to 37.4). The rate in the low-risk group was significantly lower than that in the high-risk group (P<0.001). In a multivariate Cox model, the recurrence score provided significant predictive power that was independent of age and tumor size (P<0.001). The recurrence score was also predictive of overall survival (P<0.001) and could be used as a continuous function to predict distant recurrence in individual patients. The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor-positive breast cancer. Copyright 2004 Massachusetts Medical Society.
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            Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations

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              Snakes: Active contour models

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                Author and article information

                Affiliations
                [1 ]Departments of Bioengineering, Chemical and Biomolecular Engineering, Electrical and Computer Engineering, Mechanical Science and Engineering, and Chemistry, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801; email:
                [2 ]Center for Computational Imaging and Personalized Diagnostics; Departments of Biomedical Engineering, Urology, Pathology, Radiology, Radiation Oncology, General Medical Sciences, Electrical Engineering, and Computer Science; and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106; email:
                Journal
                Annual Review of Biomedical Engineering
                Annu. Rev. Biomed. Eng.
                Annual Reviews
                1523-9829
                1545-4274
                July 11 2016
                July 11 2016
                : 18
                : 1
                : 387-412
                10.1146/annurev-bioeng-112415-114722
                © 2016

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