Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience
Open accessAbstract
The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as basal cell carcinoma (BCC), seborrheic keratosis (SK) and dermal nevus. Furthermore, the difficulty in diagnosing particular melanocytic lesions, such as Spitz nevi and melanoma, considering the grade of interobserver variability among dermatopathologists, has led to an objective difficulty in training machine learning (ML) algorithms to a totally reliable, reportable and repeatable level. In this work we tried to train a fast random forest (FRF) algorithm, typically used for the classification of clusters of pixels in images, to highlight anomalous areas classified as melanoma “defects” following the Allen–Spitz criteria. The adopted image vision diagnostic protocol was structured in the following steps: image acquisition by selecting the best zoom level of the microscope; preliminary selection of an image with a good resolution; preliminary identification of macro-areas of defect in each preselected image; identification of a class of a defect in the selected macro-area; training of the supervised machine learning FRF algorithm by selecting the micro-defect in the macro-area; execution of the FRF algorithm to find an image vision performance indicator; and analysis of the output images by enhancing lesion defects. The precision achieved by the FRF algorithm proved to be appropriate with a discordance of 17% with respect to the dermatopathologist, allowing this type of supervised algorithm to be nominated as a help to the dermatopathologist in the challenging diagnosis of malignant melanoma.
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The supervised Fast Random Forest (FRF) algorithm is integrated into clinical protocols and can estimate an alerting threshold to discriminate benign from malignant lesions in the analysis of histological images.
“the supervised Fast Random Forest (FRF) algorithm is integrated into clinical protocols and can estimate an alerting threshold [39,40] to discriminate benign from malignant lesions in the analysis of histological images.”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [40]
Materials and Methods §2.2 describes a supervised FRF algorithm that analyzes histological images and uses threshold filtering to highlight anomalous areas. But that threshold adjusts image intensity; the paper does not establish an alerting cutoff that discriminates benign from malignant lesions. The Discussion §4 says the findings allow the authors to *consider* including FRF in the normal diagnostic workflow, not that it is already integrated into clinical protocols. (LLM, conf=0.88)
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · see citing works
Random forest in remote sensing: A review of applications and future directions
Belgiu, M., & Drăguţ, L. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.01.011 · Cited by 7,228 · see citing works
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification
Arganda‐Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K. W., Schindelin, J., Cardona, A., & Seung, H. S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx180 · Cited by 2,408 · see citing works
Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the Internet
Argenziano, G., Soyer, H. P., Chimenti, S., Talamini, R., Corona, R., Sera, F., Binder, M., Cerroni, L., Rosa, G. D., Ferrara, G., et al. (2003) · Journal of the American Academy of Dermatology · doi:10.1067/mjd.2003.281 · Cited by 1,211 · see citing works
Malignant melanoma. A clinicopathological analysis of the criteria for diagnosis and prognosis
Allen, A. C., & Spitz, S. (1953) · Cancer · doi:10.1002/1097-0142(195301)6:1 · Cited by 980 · see citing works
Elmore, J. G., Barnhill, R. L., Elder, D. E., Longton, G., PEPE, M. S., Reisch, L. M., Carney, P. A., Titus, L. J., Nelson, H. D., Onega, T., et al. (2017) · BMJ · doi:10.1136/bmj.j2813 · Cited by 449 · see citing works
Pathologist-level classification of histopathological melanoma images with deep neural networks
Hekler, A., Utikal, J. S., Enk, A. H., Berking, C., Klode, J., Schadendorf, D., Jansen, P., Franklin, C., Holland-Letz, T., Krahl, D., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.04.021 · Cited by 231 · see citing works
Introduction to Artificial Intelligence and Machine Learning for Pathology
Harrison, J. H., Gilbertson, J. R., Hanna, M. G., Olson, N. H., Seheult, J. N., Sorace, J. M., & Stram, M. N. (2021) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0541-cp · Cited by 83 · see citing works
Analyzing Training Information From Random Forests for Improved Image Segmentation
Mahapatra, D. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2305073 · Cited by 77 · see citing works
Hart, S. N., Flotte, W., Norgan, A. F., Shah, K. K., Buchan, Z. R., Mounajjed, T., & Flotte, T. J. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_32_18 · Cited by 69 · see citing works
Lormand, C., Zellmer, G. F., Németh, K., Kilgour, G., Mead, S. R., Palmer, A., Sakamoto, N., Yurimoto, H., & Moebis, A. (2018) · Microscopy and Microanalysis · doi:10.1017/s1431927618015428 · Cited by 64 · see citing works
Witten, I. H. 1., Frank, E., Hall, M. A., et al. (2016) · Elsevier eBooks · doi:10.1016/c2015-0-02071-8 · Cited by 48 · see citing works
Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks
Raza, R., Zulfiqar, F., Tariq, S., Anwar, G. B., Sargano, A. B., & Habib, Z. (2021) · Mathematics · doi:10.3390/math10010026 · Cited by 40 · see citing works
AI for development: implications for theory and practice
Bjola, C. (2021) · Oxford Development Studies · doi:10.1080/13600818.2021.1960960 · Cited by 34 · see citing works
Venerito, V., Angelini, O., Cazzato, G., Lopalco, G., Maiorano, E., Cimmino, A., & Iannone, F. (2021) · Internal and Emergency Medicine · doi:10.1007/s11739-020-02583-x · Cited by 32 · see citing works
Self-citationElectronics in Advanced Research Industries
Massaro, A. S. (2021) · Wiley eBooks · doi:10.1002/9781119716907 · Cited by 32 · see citing works
Self-citationUse of Digital Whole Slide Imaging in Dermatopathology
Onega, T., Reisch, L. M., Frederick, P. D., Geller, B. M., Nelson, H. D., Lott, J. P., Radick, A. C., Elder, D. E., Barnhill, R. L., Piepkorn, M. W., et al. (2015) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-015-9836-y · Cited by 25 · see citing works
Cazzato, G., Colagrande, A., Cimmino, A., Demarco, A., Lospalluti, L., Arezzo, F., Resta, L., & Ingravallo, G. (2021) · Dermatopathology · doi:10.3390/dermatopathology8020019 · Cited by 17 · see citing works
Self-citationRoush, G. C., Kirkwood, J. M., Ernstoff, M. S., Somma, S., Duray, P. H., Klaus, S. N., Stenn, K. S., Titus, L. J., & Lerner, A. B. (1986) · Recent results in cancer research/Recent Results in Cancer Research · doi:10.1007/978-3-642-82641-2_13 · Cited by 14 · see citing works
Lay-Ekuakille, A., Spano, F., Mvemba, P. K., Massaro, A. S., Galiano, A. M., Casciaro, S., & Conversano, F. (2018) · International Conference Nanotechnology for Instrumentation and Measurement · doi:10.1109/nanofim.2018.8688617 · Cited by 6 · see citing works
Self-citationGrowth of mixed cancer cell population – in silico the size matters
Kłóś, A., & Plonka, P. M. (2020) · Acta Biochimica Polonica · doi:10.18388/abp.2020_5500 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [5]Massaro A. Electronic in Advanced Research Industry: From Industry 4.0 to Industry 5.0 Advances Wiley Hoboken, NJ, USA IEEE New York, NY, USA 2021 9781119716877
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Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal
Luo, N., Zhong, X., Su, L., Cheng, Z., Ma, W., & Hao, P. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107413 · Cited by 91 · see citing works
Malcangi, G., Inchingolo, A., Casamassima, L., Trilli, I., Ferrante, L., Inchingolo, F., Palermo, A., Inchingolo, A., & Dipalma, G. (2025) · Nutrients · doi:10.3390/nu17050762 · Cited by 55 · see citing works
Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM
Mahum, R., & Aladhadh, S. (2022) · Diagnostics · doi:10.3390/diagnostics12122974 · Cited by 40 · see citing works
Artificial Intelligence in Cervical Cancer Screening: Opportunities and Challenges
Dellino, M., Cerbone, M., d’Amati, A., Bochicchio, M. A., Laganà, A. S., Etrusco, A., Malvası, A., Vitagliano, A., Pinto, V., Cicinelli, E., et al. (2024) · AI · doi:10.3390/ai5040144 · Cited by 29 · see citing works
Self-citationArtificial intelligence in dermatopathology: Updates, strengths, and challenges
Cazzato, G., & Rongioletti, F. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.010 · Cited by 24 · see citing works
Self-citationGAN-Driven Data Poisoning Attacks and Their Mitigation in Federated Learning Systems
Psychogyios, K., Velivassaki, T.-H., Bourou, S., Voulkidis, A. C., Skias, D., & Zahariadis, T. B. (2023) · Electronics · doi:10.3390/electronics12081805 · Cited by 21 · see citing works
Cazzato, G., Massaro, A., Colagrande, A., Trilli, I., Ingravallo, G., Casatta, N., Lupo, C., Ronchi, A., Franco, R., Maiorano, E., et al. (2023) · Current Oncology · doi:10.3390/curroncol30070452 · Cited by 18 · see citing works
Self-citationRecent Use of Hyaluronic Acid in Dental Medicine
Malcangi, G., Inchingolo, A. D., Trilli, I., Ferrante, L., Casamassima, L., Nardelli, P., Inchingolo, F., Palermo, A., Severino, M., Inchingolo, A. M., et al. (2025) · Materials · doi:10.3390/ma18081863 · Cited by 17 · see citing works
Yacob, Y. M., Alquran, H., Mustafa, W. A., Alsalatie, M., Sakim, H. A. M., & Lola, M. S. (2023) · Diagnostics · doi:10.3390/diagnostics13030336 · Cited by 15 · see citing works
Yang, X., Wu, M., Yan, X., Zhang, C., Luo, Y., & Yu, J. (2023) · Molecules · doi:10.3390/molecules28093682 · Cited by 14 · see citing works
PRAME Is an Effective Tool for the Diagnosis of Nevus-Associated Cutaneous Melanoma
Ronchi, A., Cazzato, G., Ingravallo, G., D’Abbronzo, G., Argenziano, G., Moscarella, E., Brancaccio, G., & Franco, R. (2024) · Cancers · doi:10.3390/cancers16020278 · Cited by 11 · see citing works
Self-citationPrivacy-Enhanced Federated Learning for Non-IID Data
Tan, Q., Wu, S., & Tao, Y.-H. (2023) · Mathematics · doi:10.3390/math11194123 · Cited by 10 · see citing works
Process Mining Organization (PMO) Modeling and Healthcare Processes
Rosa, A., & Massaro, A. S. (2023) · Knowledge · doi:10.3390/knowledge3040041 · Cited by 10 · see citing works
Self-citationMini review on skin biopsy: traditional and modern techniques
Alwahaibi, N., & Alwahaibi, M. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1476685 · Cited by 12 · see citing works
Bottrighi, A., & Pennisi, M. A. (2023) · Information · doi:10.3390/info14090513 · Cited by 9 · see citing works
Malcangi, G., Inchingolo, A. M., Inchingolo, A. D., Ferrante, L., Latini, G., Trilli, I., Nardelli, P., Longo, M., Palermo, A., Inchingolo, F., et al. (2025) · Medicina · doi:10.3390/medicina61010084 · Cited by 8 · see citing works
Cocuz, I. G., Niculescu, R., Popelea, M. C., Cocuz, M. E., Sabău, A.-H., Tinca, A. C., Cozac-Szőke, A. R., Chiorean, D. M., Budin, C. E., & Cotoi, O. S. (2025) · Diagnostics · doi:10.3390/diagnostics15172196 · Cited by 7 · see citing works
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
“For example, the supervised Fast Random Forest (FRF) algorithm is integrated into clinical protocols and can estimate an alerting threshold [39,40] to discriminate benign from malignant lesions in the analysis of histological images.”
Self-citationGalambus, J., Smythe, C., & Cockerell, C. J. (2025) · Dermatologic Clinics · doi:10.1016/j.det.2025.03.008 · Cited by 6 · see citing works
Marozzi, M. S., Cicco, S., Mancini, F., Corvasce, F., Lombardi, F. A., Desantis, V., Loponte, L., Giliberti, T., Morelli, C. M., Longo, S., et al. (2024) · Diagnostics · doi:10.3390/diagnostics14020155 · Cited by 6 · see citing works
Self-citationMachine learning to detect melanoma exploiting nuclei morphology and Spatial organization
Veronesi, G., Curti, N., Gardini, A., Querzoli, G., Castellani, G., & Dika, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-02913-z · Cited by 6 · see citing works
Artificial intelligence in medicine: a position paper by the Italian Society of Internal Medicine
Balsano, C., Cabitza, F., Cicco, S., Gori, M., Malerba, D., Montagna, M., Tarquini, R., Vacca, A., & The Working Group on Artificial Intelligence, D. T. O. T. I. S. O. I. M. (. (2025) · Internal and Emergency Medicine · doi:10.1007/s11739-025-04146-4 · Cited by 6 · see citing works
Self-citationHerzum, A., Occella, C., Vellone, V. G., Gariazzo, L., Pastorino, C., Ferro, J., Sementa, A. R., Mazzocco, K., Vercellino, N., & Viglizzo, G. (2023) · Diagnostics · doi:10.3390/diagnostics13142380 · Cited by 5 · see citing works
Christensen, J. M. M., Tømmerås, M. N., Kromann, B., Haulrig, M. B., Manolache, M., Løvendorf, M. B., & Dyring‐Andersen, B. (2026) · JID Innovations · doi:10.1016/j.xjidi.2026.100497 · Cited by 5 · see citing works
Mosquera‐Zamudio, A., Launet, L., Colomer, A., Wiedemeyer, K., López-Takegami, J. C. H., Palma, L. F., Undersrud, E., Janssen, E., Brenn, T., Naranjo, V., et al. (2024) · Histopathology · doi:10.1111/his.15187 · Cited by 4 · see citing works
A Review of Artificial Intelligence in Dermatopathology
Prestwood, C. A., Gibbs, D. C., Wolner, Z. J., & Stoff, B. K. (2025) · Dermatologic Clinics · doi:10.1016/j.det.2025.05.006 · Cited by 3 · see citing works
Muthu, S. P. V., & Devadoss, A. K. V. (2023) · Traitement du signal · doi:10.18280/ts.400432 · Cited by 3 · see citing works
Artificial Intelligence in Melanoma Dermatopathology: A Review of Literature
Neimy, H., Helmy, J., Snyder, A., & Valdebran, M. (2023) · American Journal of Dermatopathology · doi:10.1097/dad.0000000000002593 · Cited by 5 · see citing works
Massaro, A. S., Cazzato, G., Ingravallo, G., Casatta, N., Lupo, C., Vacca, A., Iannone, F., & Girolamo, F. (2025) · Diagnostic Pathology · doi:10.1186/s13000-025-01608-3 · Cited by 3 · see citing works
Self-citationEditorial on Special Issue “Artificial Intelligence in Pathological Image Analysis”
Tsuneki, M. (2023) · Diagnostics · doi:10.3390/diagnostics13050828 · Cited by 3 · see citing works
The Current State and Future Prospects for Artificial Intelligence in Dermatology
Thang, C. J., Duffy, C., Khattab, S., & Semenov, Y. R. (2025) · Dermatologic Clinics · doi:10.1016/j.det.2025.05.001 · Cited by 1 · see citing works
McLean, M. (2007) · Current Oncology · doi:10.3747/co.2007.115
The Terry Fox Research Institute’s West Coast Dialogue: Are We Prepared for Personalized Medicine?
Curwin, K., Smith, C., & Sutcliffe, S. B. J. (2010) · Current Oncology · doi:10.3747/co.v17i6.768
Abdallah, O. A., Radic, B., Almasri, L., Palermo, A., GIACOMO, A. D., Asselti, M. G., Guerra, F. L. B., Guerra, A. J. D. P., Ferrante, L., Semjonova, A., et al. (2025) · Oral & Implantology · doi:10.11138/oi.v17i2.123
- Authors
- Cazzato, G., Massaro, A., Colagrande, A., Lettini, T., Cicco, S., Parente, P., Nacchiero, E., Lospalluti, L., Cascardi, E., Giudice, G., Ingravallo, G., Resta, L., Maiorano, E., & Vacca, A.
- Affiliations
- University of Bari Aldo Moro (IT)
- LUM Jean Monnet University (IT)
- Casa Sollievo della Sofferenza (IT)
- Candiolo Cancer Institute (IT)
- Corresponding author
- Cazzato, Gerardo
- ORCID
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2022-08-15
- Volume
- 12
- Issue
- 8
- Pages
- 1972
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC9407151
- OpenAlex
- W4291743884
- Cited by
- 34
- References
- 21
- Citations by year
- 2026: 1 · 2025: 13 · 2024: 5 · 2023: 12 · 2022: 1
- FWCI
- 3.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, cc-by)
- 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
- melanoma, dermatopathology, artificial intelligence, computer-aided diagnosis, interobserver variability
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