Journal of Pathology Informatics Journal of Pathology Informatics
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RESEARCH ARTICLE
Year : 2014  |  Volume : 5  |  Issue : 1  |  Page : 23

Automated grading of renal cell carcinoma using whole slide imaging


1 Department of Biomedical Engineering; Department of Biological Science, Pittsburgh NMR Center for Biomedical Research, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
2 Department of Pathology, Division of Pathology Informatics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA

Correspondence Address:
Chien Ho
Department of Biomedical Engineering; Department of Biological Science, Pittsburgh NMR Center for Biomedical Research, Carnegie Mellon University, Pittsburgh, Pennsylvania
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.137726

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Introduction: Recent technology developments have demonstrated the benefit of using whole slide imaging (WSI) in computer-aided diagnosis. In this paper, we explore the feasibility of using automatic WSI analysis to assist grading of clear cell renal cell carcinoma (RCC), which is a manual task traditionally performed by pathologists. Materials and Methods: Automatic WSI analysis was applied to 39 hematoxylin and eosin-stained digitized slides of clear cell RCC with varying grades. Kernel regression was used to estimate the spatial distribution of nuclear size across the entire slides. The analysis results were correlated with Fuhrman nuclear grades determined by pathologists. Results: The spatial distribution of nuclear size provided a panoramic view of the tissue sections. The distribution images facilitated locating regions of interest, such as high-grade regions and areas with necrosis. The statistical analysis showed that the maximum nuclear size was significantly different (P < 0.001) between low-grade (Grades I and II) and high-grade tumors (Grades III and IV). The receiver operating characteristics analysis showed that the maximum nuclear size distinguished high-grade and low-grade tumors with a false positive rate of 0.2 and a true positive rate of 1.0. The area under the curve is 0.97. Conclusion: The automatic WSI analysis allows pathologists to see the spatial distribution of nuclei size inside the tumors. The maximum nuclear size can also be used to differentiate low-grade and high-grade clear cell RCC with good sensitivity and specificity. These data suggest that automatic WSI analysis may facilitate pathologic grading of renal tumors and reduce variability encountered with manual grading.


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