Quantitative histopathology identifies patients with thin melanomas who are at risk for metastases. Issue 3 (June 2016)

• Record Type: Journal Article
• Authors: Glazer, Evan S.; Bartels, Peter H.; Lian, Fangru; Kha, Stephanie T.; Morgan, Sherif S.; da Silva, Vinicius D.; Yozwiak, Michael L.; Bartels, Hubert G.; Cranmer, Lee D.; de Oliveira, Jefferson K.; Alberts, David S.; Warneke, James A.; Krouse, Robert S.
• Is Part Of: Melanoma research. Volume 26:Issue 3(2016)
• Publication Date: 2016-06
• Subjects: aggressive melanoma -- karyometry -- quantitative histopathology -- thin melanoma; Melanoma -- Periodicals; Melanoma -- Periodicals; Melanomen; 616.99477
• DOI: 10.1097/CMR.0000000000000236
• Languages: English
• ISSNs: 0960-8931

Abstract

Abstract : This small exploratory study was designed to test the hypothesis that thin melanoma lesions contain nuclei of two similar phenotypes, in different proportions. In lesions likely to progress to metastatic disease, one of these phenotypes predominates. Histopathological sections from 18 cases of thin melanomas which did not progress to metastasis, and from 10 cases which did progress were imaged and digitized at high resolution, with a total of 2084 and 1148 nuclei, respectively, recorded. Five karyometric features were used to discriminate between nuclei from indolent and from potentially metastatic lesions. For each case, the percentage of nuclei classified by the discriminant function as having come from a potentially metastatic lesion was determined and termed as case classification criterion. Standard histopathological criteria, such as ulceration and high mitotic index, indicated in this material the need for intensive therapy for only one of the 10 participants, as compared with 7/10 identified correctly by the karyometric measure. Using a case classification criterion threshold of 40%, the overall accuracy was 86% in the test set. The proportion of nuclei of an aggressive phenotype may lend itself as an effective prognostic clue for thin melanoma lesions. The algorithm developed in this training set appears to identify those patients at high risk for metastatic disease, and demonstrates a basis for a further study to assess the utility of prognostic clues for thin melanomas.