Basic elements of artificial intelligence tools in the diagnosis of cutaneous melanoma
Abstract
Cutaneous melanoma (CM) incidence has dramatically increased in the last years. Early diagnosis is of paramount importance in terms of prognosis. Artificial Intelligence (AI) tools are being proposed for clinicians and pathologists as an adjunct support in the diagnostic process. We described herein an overview of the most important parameters that a potential AI tool should take into consideration in histopathology to evaluate a skin lesion. First of all, recognition of a melanocytic or non-melanocytic nature. Furthermore, melanocytic lesions should be stratified according to at least four parameters: silhouette and asymmetry; identification and spatial distribution of the cells; mitosis count; presence of ulceration. According to the number of parameters the AI tools might stratify the risk of CM and prioritize the pathologist's work.
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In cutaneous melanoma, AI models must initially differentiate between melanocytic and non-melanocytic lesions.
“In the context of cutaneous melanoma, AI models must initially differentiate between melanocytic and non-melanocytic lesions.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [7]
For melanocytic lesions, evaluating silhouette and asymmetry, cellular distribution, mitotic count, and ulceration is essential to stratify risk and optimize pathologists' workloads.
“For melanocytic lesions, it is essential to evaluate critical histopathological parameters—silhouette and asymmetry, cellular distribution, mitotic count, and ulceration—to stratify risk and optimize pathologists’ workloads (7).”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [7]
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Exploring the Common Mutational Landscape in Cutaneous Melanoma and Pancreatic Cancer
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“For melanocytic lesions, it is essential to evaluate critical histopathological parame-ters—silhouette and asymmetry, cellular distribution, mitotic count, and ulceration—to stratify risk and optimize pathologists’ workloads (7).”
Reply to ‘Risk stratification of cutaneous melanoma using deep learning on whole slide images’
Magnaterra, E., Venturi, F., Scotti, B., Baraldi, C., Vaccari, S., Alessandrini, A. M., & Dika, E. (2025) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.70092 · Cited by 1 · see citing works
Self-citationTowards Accurate Breslow Measurements: Mitigating Issues in Histopathological Imaging
Curti, N., Dall’Olio, L., Veronesi, G., Querzoli, G., Venturi, F., Sisi, A., Peluso, S., Castellani, G., & Dika, E. (2026) · Entropy · doi:10.3390/e28060643
Self-citation
- Authors
- Querzoli, G., Veronesi, G., Corti, B., Nottegar, A., & Dika, E.
- Affiliations
- IRCCS Azienda Ospedliero-Universitaria di Bologna Policlinico di Sant'Orsola (IT)
- Azienda Ospedaliera Universitaria Integrata Verona (IT)
- Corresponding author
- Querzoli, Giulia
- Venue
- Critical Reviews™ in Oncogenesis
- Publisher
- Begell House
- Published
- 2023
- Volume
- 28
- Issue
- 3
- Pages
- 37-41
- ISSN
- 0893-9675 (ISSN-L)
- OpenAlex
- W4386903095
- Cited by
- 6
- References
- 13
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Critical Reviews™ in Oncogenesis (publishedVersion)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- cutaneous melanoma, artificial intelligence, melanoma diagnosis, histopathology, melanocytic lesions, non-melanocytic skin lesions, mitotic count, ulceration, risk stratification, computer-aided diagnosis
- MeSH terms
- Melanoma, Cutaneous Malignant, Cutaneous Malignant Melanoma, Artificial Intelligence, Humans, Melanoma, Prognosis, Skin Neoplasms
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