Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2017  |  Volume : 8  |  Issue : 1  |  Page : 15

Predictive nuclear chromatin characteristics of melanoma and dysplastic nevi


1 Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA; Department of Pathology and Laboratory Medicine, The Mount Sinai Hospital and Icahn School of Medicine at Mount Sinai, NY, USA
2 Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
3 Department of Biomedical Engineering; Department of Charles L Brown Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA
4 Department of Pathology and Laboratory Medicine, The Mount Sinai Hospital and Icahn School of Medicine at Mount Sinai, NY, USA

Correspondence Address:
Rajendra Singh
Department of Pathology and Laboratory Medicine, The Mount Sinai Hospital and Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, NY 10029
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_84_16

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Background: The diagnosis of malignant melanoma (MM) is among the diagnostic challenges pathologists encounter on a routine basis. Melanoma may arise in patients with preexisting dysplastic nevi (DN) and it is still the cause of 1.7% of all cancer-related deaths. Melanomas often have overlapping histological features with DN, especially those with severe dysplasia. Nucleotyping for identifying nuclear textural features can analyze nuclear DNA structure and organization. The aim of this study is to differentiate MM and DN using these methodologies. Methods: Dermatopathology slides diagnosed as MM and DN were retrieved. The glass slides were scanned using an Aperio ScanScopeXT at ×40 (0.25 μ/pixel). Whole slide images (WSI) were annotated for nuclei selection. Nuclear features to distinguish between MM and DN based on chromatin distributions were extracted from the WSI. The morphological characteristics for each nucleus were quantified with the optimal transport-based linear embedding in the continuous domain. Label predictions for individual cell nucleus are achieved through a modified version of linear discriminant analysis, coupled with the k-nearest neighbor classifier. Label for an unknown patient was set by the voting strategy with its pertaining cell nuclei. Results: Nucleotyping of 139 patient cases of melanoma (n = 67) and DN (n = 72) showed that our method had superior classification accuracy of 81.29%. This is a 6.4% gain in differentiating MM and DN, compared with numerical feature-based method. The distribution differences in nuclei morphology between MM and DN can be visualized with biological interpretation. Conclusions: Nucleotyping using quantitative and qualitative analyses may provide enough information for differentiating MM from DN using pixel image data. Our method to segment cell nuclei may offer a practical and inexpensive solution in aiding in the accurate diagnosis of melanoma.


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