Background: A series of studies has investigated epidemiological, clinical and genetic characteristics of patients with multiple primary melanoma (MPM). However, comparison of the clinical and dermoscopic features of MPM within a given individual has been described only in case reports.
Objectives: To describe the dermoscopic features of MPM for each given patient, and to evaluate the characteristics eventually associated with similar or dissimilar appearance.
Methods: From the databases of three skin-lesion clinics in the U.S.A., Italy and Spain we collected the dermoscopic images of melanomas in patients diagnosed with MPM.
Results: Among 58 patients with MPM, we found that 53% of patients had dermoscopically similar melanomas and 47% of patients had dermoscopically different melanomas. In older patients 59% of melanomas were dermoscopically similar vs. 47% in younger patients (P=0·377). Similar thickness was associated with the occurrence of dermoscopically similar melanomas (19/30 cases, 63%; P=0·039). Most (65%) of the synchronous lesions were similar, compared with 36% of nonsynchronous lesions (P=0·029), and most (69%) of the melanomas on sun-damaged skin were similar, vs. 37% of melanomas on nonsun-damaged skin (P=0·015; odds ratio 3·88, 95% confidence interval 1·11-13·98). The percentage of dermoscopically different melanomas was higher in patients with a family history of melanoma (67% vs. 48%).
Conclusions: MPMs in a given patient have almost the same chance of looking dermoscopically similar or different. However, a subset of elderly patients with sun-damaged skin may present multiple, similar, thin melanomas characterized by pigment-network and regression structures.
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http://dx.doi.org/10.1111/bjd.12260 | DOI Listing |
Risk Manag Healthc Policy
January 2025
Department of Clinical Internal, Anesthesiological and Cardiovascular Sciences, Dermatology Clinic, Sapienza University of Rome, Rome, Italy.
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Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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AIM for Health Lab, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.
Traditional disease classification models often disregard the clinical significance of misclassifications and lack interpretability. To overcome these challenges, we propose a hierarchical prototypical decision tree (HPDT) for skin lesion classification. HPDT combines prototypical networks and decision trees, leveraging a class hierarchy to guide interpretable predictions from general to specific categories.
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Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320, Taiwan.
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