Digital quantification of Ki67 and PRAME in challenging melanocytic lesions—A novel diagnostic tool
Open accessAbstract
The interpretation of immunohistochemical markers in melanocytic lesions possesses difficulties due to expression in non-melanocytic cells and the time-consuming, non-reproducible nature of manual assessment. A digital tool that accurately quantifies Ki67 and PRAME may valuably aid pathologists in the diagnostic classification of melanocytic lesions. The aim of this study was to assess the diagnostic performance of digitally quantified Ki67 and PRAME in challenging melanocytic lesions utilizing double nuclear staining methods for accurate identification of melanocytic cells. We explored the difference in Ki67 and PRAME expression by WHO-lesion-groups and Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis version 2.0 (MPATH-Dx V2.0). Tissue slides from a cohort of 156 melanocytic lesions were stained with the Ki67/SOX10 double nuclear stain and the PRAME/SOX10 virtual double nuclear stain. Melanocytic cell specific Ki67/SOX10- and PRAME/SOX10-indexes were quantified by AI-driven digital image analysis (DIA) and compared to non-specific Ki67- and PRAME-indexes. The results showed that ROC AUC of the Ki67/SOX10-index was increased compared to the non-specific Ki67-index (p < 0.001), as opposed to the AUC of the PRAME/SOX10-index compared to non-specific PRAME-index (p = 0.090). The medians of digitally quantified Ki67- and PRAME-indexes differed significantly for the overall WHO-groups and MPATH-Dx V2.0 classes (p < 0.001). In conclusion, we found that double nuclear staining improved the diagnostic performance of Ki67, but not PRAME. The combination of digitally quantified Ki67- and PRAME-indexes may potentially serve as a tool for diagnostic classification of challenging melanocytic lesions. The proposed diagnostic tool presents the results visually, graphically, and quantitatively to optimally aid the pathologist.
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Deep learning algorithms applied to whole-slide images can identify positive versus negative cells and measure staining intensity.
“Deep learning algorithms applied to WSI can identify positive versus negative cells and measure staining intensity.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [37]
AI-based quantification of immunohistochemical stains in dermatopathology offers a reproducible alternative to manual scoring, which is often limited by interobserver variability.
“AI-based quantification of immunohistochemical (IHC) stains in dermatopathology offers a reproducible alternative to manual scoring, which is often limited by interobserver variability.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [37]
In dermatopathology, AI applications for IHC quantification include assessing proliferation markers (e.g., Ki-67) in melanocytic lesions, quantifying lineage markers (Melan-A, SOX10) for melanocytic lesion evaluation, and analyzing immune-related markers (CD3, CD8, PD-L1) in cutaneous tumors and inflammatory dermatoses.
“In dermatopathology, applications include assessing proliferation markers (e.g., Ki-67) in melanocytic lesions [37], quantifying lineage markers (Melan-A, SOX10) [38] for melanocytic lesion evaluation, and analyzing immune-related markers (CD3, CD8, PD-L1) [39,40] in both cutaneous tumors and inflammatory dermatoses.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [37]
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References 53 · Cited by 1
- Authors
- Brogård, M. B., Steiniche, T., Lade-Keller, J., Wandler, A., Christensen, K. B., Georgsen, J. B., & Nielsen, P. S.
- Affiliations
- Aarhus University (DK)
- Aarhus University Hospital (DK)
- Corresponding author
- Brogård, Mette Bak
- ORCID
- Venue
- Pathology - Research and Practice
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 270
- Pages
- 155953
- ISSN
- 0344-0338 (ISSN-L)
- OpenAlex
- W4409088301
- Cited by
- 1
- References
- 53
- Citations by year
- 2026: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Pathology - Research and Practice (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (publishedVersion, cc-by)
- Funders
- Harboe Foundation (20128)
- Torben and Alice Frimodts Fund
- Dagmar Marshall Fund
- Health Research Foundation of Central Denmark Region (A3139)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
- Keywords
- melanocytic lesions, Ki-67, PRAME, SOX10, digital image analysis, melanoma diagnosis, immunohistochemical markers, diagnostic classification
- MeSH terms
- Adult, Antigens, Neoplasm, Female, Humans, Immunohistochemistry, Male, Melanoma, Middle Aged, Skin Neoplasms, Biomarkers, Tumor, Ki-67 Antigen, SOXE Transcription Factors
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