The role of dual-specificity phosphatase 3 in melanocytic oncogenesis.

Dual-specificity phosphatase 3 (DUSP3), also known as Vaccinia H1-related phosphatase, is a protein tyrosine phosphatase that typically performs its major role in the regulation of multiple cellular functions through the dephosphorylation of its diverse and constantly expanding range of substrates. Many of the substrates described so far as well as alterations in the expression or the activity of DUSP3 itself are associated with the development and progression of various types of neoplasms, indicating that DUSP3 may be an important player in oncogenesis and a promising therapeutic target. This review focuses exclusively on DUSP3's contribution to either benign or malignant melanocytic oncogenesis, as many of the established culprit pathways and mechanisms constitute DUSP3's regulatory targets, attempting to synthesize the current knowledge on the matter. The spectrum of the DUSP3 interactions analysed in this review covers substrates implicated in cellular growth, cell cycle, proliferation, survival, apoptosis, genomic stability/repair, adhesion and migration of tumor melanocytes. Furthermore, the speculations raised, based on the evidence to date, may be considered a fundament for potential research regarding the oncogenesis, evolution, management and therapeutics of melanocytic tumors.

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma.

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists' decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists' diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists' confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists' willingness to adopt such XAI systems, promoting future use in the clinic.

Topographical and Chronological Analysis of Thin Cutaneous Melanoma's Progressions: A Multicentric Study.

A great portion of cutaneous melanoma's diagnoses nowadays is attributed to thin tumors with up to 1 mm in Breslow thickness (hereafter thin CMs), which occasionally metastasize. The objective of this study was to identify thin CM's metastatic patterns from a topographical and chronological standpoint. A total of 204 cases of metastatic thin CMs from five specialized centers were included in the study, and corresponding data were collected (clinical, epidemiological, histopathological information of primary tumor and the number, anatomical site, and time intervals of their progressions). First progressions occurred locally, in regional lymph nodes, and in a distant site in 24%, 15% and 61% of cases, respectively, with a median time to first progression of 3.10 years (IQR: 1.09-5.24). The median elapsed time between the first and second progression and between the second and third progression was 0.82 (IQR: 0.34-1.97) and 0.49 (IQR: 0.21-2.30) years, respectively, while the median survival time was about 4 years since first progression. Furthermore, the sequences of locations and time intervals of the progressions were associated with the clinicopathological and demographic features of the primary tumors along with the features of the preceding progressions. In conclusion, the findings of this study describe the natural history of thin CMs, thus highlighting the necessity to identify subgroups of thin CMs at a higher risk for metastasis and contributing to the optimization of the management and follow-up of thin CM patients.

Analyzing the Spatial Randomness in the Distribution of Acquired Melanocytic Neoplasms.

On the basis of the clinical impression and current knowledge, acquired melanocytic nevi and melanomas may not occur in random localizations. The goal of this study was to identify whether their distribution on the back is random and whether the location of melanoma correlates with its adjacent lesions. Therefore, patient-level and lesion-level spatial analyses were performed using the Clark‒Evans test for complete spatial randomness. A total of 311 patients with three-dimensional total body photography (average age of 40.08 [30‒49] years; male/female ratio: 128/183) with 5,108 eligible lesions in total were included in the study (mean sum of eligible lesions per patient of 16.42 [3‒199]). The patient-level analysis revealed that the distributions of acquired melanocytic neoplasms were more likely to deviate toward clustering than dispersion (average z-score of ‒0.55 [95% confidence interval = ‒0.69 to ‒0.41; P < 0.001]). The lesion-level analysis indicated a higher portion of melanomas (n = 57 of 72, 79.2% [95% confidence interval = 69.4‒88.9%]) appearing in proximity to neighboring melanocytic neoplasms than to nevi (n = 2,281 of 5,036, 45.3% [95% confidence interval = 43.9‒46.7%]). In conclusion, the nevi and melanomas' distribution on the back tends toward clustering as opposed to dispersion. Furthermore, melanomas are more likely to appear proximally to their neighboring neoplasms than to nevi. These findings may justify various oncogenic theories and improve diagnostic methodology.

A patient-centric dataset of images and metadata for identifying melanomas using clinical context.

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.