Image and statistical analysis of melanocytic histology

Abstract

Aims We apply digital image analysis techniques to study selected types of melanocytic lesions. Methods and Results We used advanced digital image analysis to compare melanocytic lesions. All comparisons were statistically significant (p < 0.0001) and we highlight four: 1) melanoma to nevi, 2) melanoma subtypes to nevi, 3) severely dysplastic nevi to other nevi, and 4) melanoma to severely dysplastic nevi. We were successful in differentiating melanoma from nevi (ROC area 0.95) using image-derived features. While many features contribute to separation, analysis revealed features related to nuclear size, shape, and distance between nuclei to be most important. Dividing melanoma into subtypes, even greater separation was obtained (ROC area 0.98 for superficial spreading melanoma; 0.95 for lentigo maligna melanoma; and 0.99 for unclassified). Severely dysplastic nevi were best differentiated from conventional and mildly dysplastic nevi by differences in cellular staining qualities (ROC area 0.84). We found that melanoma were separated from severely dysplastic nevi by features related to cell shape and cellular staining qualities (ROC area 0.95). Conclusions We offer a unique perspective into the evaluation of melanocytic lesions and demonstrate a technological application with increasing prevalence, with potential use as an adjunct to traditional diagnosis in the future.

Sizing and shaping the nucleus: mechanisms and significance

“…Tomographic imaging performed on fibrocystic and malignant mammary epithelial cells revealed abnormal nuclear shape, increased nuclear volume, greater numbers of nucleoli, and increased chromatin density and clumping, compared to normal cells [109]. Digital image analysis performed on melanocytic lesions comparing 62 features, including nuclear area, shape, and texture, allowed for effective differentiation of melanoma stages and subtypes [110]. Taken together, automated quantitative 3D nuclear morphometry could be useful as a novel diagnostic tool.…”

Appearance normalization of histology slides

“…Each image has approximately 10 regions identified by a pathologist. In each of these regions, cell nuclei are segmented and then features are extracted based on these nuclear segmentations using the process described in Miedema et al (2012). Classification was done using three different processing methods: (1) no appearance normalization, segmentation and feature extraction done on original slides, (2) normalized slides used for segmentation, features extracted from original slides, and (3) normalized slides used for both segmentation and feature extraction.…”

Use of computer‐assisted image analysis for semi‐quantitative histology of the hepatopancreas in whiteleg shrimp Penaeus vannamei (Boone)

“…In current practice, histological analysis is based most commonly on qualitative features as interpreted by pathologists (Miedema et al . ). Pathologists’ quantification is in general time‐consuming, poorly objective with significant discrepancies in scoring results reported between pathologists (He et al .…”