A 3-Dimensional Histology Computer Model of Malignant Melanoma and Its Implications for Digital Pathology
Open accessAbstract
BACKGROUND: Historically, cancer diagnoses have been made by pathologists using two-dimensional histological slides. However, with the advent of digital pathology and artificial intelligence, slides are being digitised, providing new opportunities to integrate their information. Since nature is 3-dimensional (3D), it seems intuitive to digitally reassemble the 3D structure for diagnosis. OBJECTIVE: To develop the first human-3D-melanoma-histology-model with full data and code availability. Further, to evaluate the 3D-simulation together with experienced pathologists in the field and discuss the implications of digital 3D-models for the future of digital pathology. METHODS: A malignant melanoma of the skin was digitised via 3 µm cuts by a slide scanner; an open-source software was then leveraged to construct the 3D model. A total of nine pathologists from four different countries with at least 10 years of experience in the histologic diagnosis of melanoma tested the model and discussed their experiences as well as implications for future pathology. RESULTS: We successfully constructed and tested the first 3D-model of human melanoma. Based on testing, 88.9% of pathologists believe that the technology is likely to enter routine pathology within the next 10 years; advantages include a better reflectance of anatomy, 3D assessment of symmetry and the opportunity to simultaneously evaluate different tissue levels at the same time; limitations include the high consumption of tissue and a yet inferior resolution due to computational limitations. CONCLUSIONS: 3D-histology-models are promising for digital pathology of cancer and melanoma specifically, however, there are yet limitations which need to be carefully addressed.
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3D image processing is adopted to construct 3D lesion models.
“or 3D lesion models [60]”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [60]
Cites
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232 · see citing works
A survey of methods for 3D histology reconstruction
Pichat, J., Iglesias, J. E., Yousry, T., Ourselin, S., & Modat, M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.004 · Cited by 207 · see citing works
Brinker, T. J., Schmitt, M., Krieghoff-Henning, E. I., Barnhill, R., Beltraminelli, H., Braun, S. A., Carr, R., Fernandez-Figueras, M.-T., Ferrara, G., Fraitag, S., et al. (2022) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2021.02.009 · Cited by 70 · see citing works
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Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
“Three-dimensional image processing is also adopted to construct microanatomical structures of skin [59] or 3D lesion models [60], or to perform AI-based virtual staining for non-invasive approaches [61].”
- Authors
- Kurz, A., Krahl, D., Kutzner, H., Barnhill, R., Perasole, A., Figueras, M. T. F., Ferrara, G., Braun, S. A., Starz, H., Llamas-Velasco, M., Utikal, J. S., Fröhling, S., von Kalle, C., Kather, J. N., Schneider, L., & Brinker, T. J.
- Affiliations
- German Cancer Research Center (DE)
- Dermatopathologie Friedrichshafen (DE)
- Institut Curie (FR)
- Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (IT)
- University of Münster (DE)
- Else Kröner Fresenius Center for Digital Health (DE)
- Corresponding author
- Brinker, Titus J.
- ORCID
- Venue
- European Journal of Cancer
- Publisher
- Elsevier BV
- Published
- 2023-11
- Volume
- 193
- Pages
- 113294
- ISSN
- 0959-8049 (ISSN-L)
- OpenAlex
- W4385981717
- Cited by
- 10
- References
- 3
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3 · 2023: 1
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Journal of Cancer (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Gesundheit
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- melanoma, digital pathology, histopathological diagnosis, slide scanning, 3D reconstruction, cancer diagnosis
- MeSH terms
- Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Melanoma, Skin Neoplasms, Imaging, Three-Dimensional
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