Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm
Open accessAbstract
An increasing number of pathology laboratories are now fully digitised, using whole slide imaging (WSI) for routine diagnostics. WSI paves the road to use artificial intelligence (AI) that will play an increasing role in computer-aided diagnosis (CAD). In melanocytic skin lesions, the presence of a dermal mitosis may be an important clue for an intermediate or a malignant lesion and may indicate worse prognosis. In this study a mitosis algorithm primarily developed for breast carcinoma is applied to melanocytic skin lesions. This study aimed to assess whether the algorithm could be used in diagnosing melanocytic lesions, and to study the added value in diagnosing melanocytic lesions in a practical setting. WSI’s of a set of hematoxylin and eosin (H&E) stained slides of 99 melanocytic lesions (35 nevi, 4 intermediate melanocytic lesions, and 60 malignant melanomas, including 10 nevoid melanomas), for which a consensus diagnosis was reached by three academic pathologists, were subjected to a mitosis algorithm based on AI. Two academic and six general pathologists specialized in dermatopathology examined the WSI cases two times, first without mitosis annotations and after a washout period of at least 2 months with mitosis annotations based on the algorithm. The algorithm indicated true mitosis in lesional cells, i.e., melanocytes, and non-lesional cells, i.e., mainly keratinocytes and inflammatory cells. A high number of false positive mitosis was indicated as well, comprising melanin pigment, sebaceous glands nuclei, and spindle cell nuclei such as stromal cells and neuroid differentiated melanocytes. All but one pathologist reported more often a dermal mitosis with the mitosis algorithm, which on a regular basis, was incorrectly attributed to mitoses from mainly inflammatory cells. The overall concordance of the pathologists with the consensus diagnosis for all cases excluding nevoid melanoma (n = 89) appeared to be comparable with and without the use of AI (89% vs. 90%). However, the concordance increased by using AI in nevoid melanoma cases (n = 10) (75% vs. 68%). This study showed that in general cases, pathologists perform similarly with the aid of a mitosis algorithm developed primarily for breast cancer. In nevoid melanoma cases, pathologists perform better with the algorithm. From this study, it can be learned that pathologists need to be aware of potential pitfalls using CAD on H&E slides, e.g., misinterpreting dermal mitoses in non-melanotic cells.
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In Sturm et al.'s study, overall diagnostic concordance with expert consensus was similar (~90%) with and without algorithm annotations.
“While overall diagnostic concordance with expert consensus was similar (~90%)”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [32]
The Results report similar concordance with and without AI (89% versus 90%), but that comparison excludes the 10 nevoid melanoma cases. The unqualified claim implies the figure covers all cases, whereas the paper reports improved concordance with AI for nevoid melanoma. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe AI mitosis detection tool flagged numerous false positives (e.g., pigmented or keratinocyte nuclei).
“The AI flagged numerous false positives (e.g., pigmented or keratinocyte nuclei), underscoring the need for cautious interpretation.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [32]
The Materials and Methods (§2.2) and Discussion (§4) report numerous false-positive detections, including melanin pigment and several kinds of nuclei. But the Results (§3) describe detections in keratinocytes as non-lesional mitoses, not keratinocyte nuclei falsely identified as mitoses. The paper also cautions against misinterpreting such detections. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn nevoid melanoma cases, the AI mitosis detection algorithm improved diagnostic concordance from 68% to 75%.
“in nevoid melanoma cases, the algorithm improved concordance from 68% to 75%.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [32]
Mitosis detection algorithms may be helpful in particularly challenging cases like nevoid melanoma but offer limited benefit in routine lesion interpretation.
“These findings suggest that mitosis detection algorithms may be helpful in particularly challenging cases like nevoid melanoma but offer limited benefit in routine lesion interpretation.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [32]
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- Authors
- Sturm, B., Creytens, D., Smits, J., Ooms, A. H. A. G., Eijken, E., Kurpershoek, E., Küsters-Vandevelde, H. V. N., Wauters, C., Blokx, W. A. M., & van der Laak, J. A. W. M.
- Affiliations
- Radboud University Medical Center (NL)
- Ghent University Hospital (BE)
- Pathan B.V., 3045 PM Rotterdam, The Netherlands
- Laboratory for Pathology Oost Nederland (LabPON), 7550 AM Hengelo, The Netherlands
- Department of Pathology, Canisius Wilhelmina Hospital, 6500 GS Nijmegen, The Netherlands
- Canisius-Wilhelmina Ziekenhuis (NL)
- University Medical Center Utrecht (NL)
- Department of Pathology, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2022-02-08
- Volume
- 12
- Issue
- 2
- Pages
- 436
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC8871065
- OpenAlex
- W4211247273
- Cited by
- 13
- References
- 43
- Citations by year
- 2026: 1 · 2025: 6 · 2023: 4 · 2022: 2
- FWCI
- 0.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- European Federation of Pharmaceutical Industries and Associations
- European Commission (945358)
- Knut och Alice Wallenbergs Stiftelse
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- melanocytic lesions, computer-aided diagnosis, whole slide imaging, nevoid melanoma, melanoma, melanocytic nevi, dermatopathology, algorithm transferability, H&E staining
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