Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis.

The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.

Association of metformin use and survival in patients with cutaneous melanoma and diabetes.

BACKGROUND: Metformin use has been associated with improved survival in patients with different types of cancer, but research regarding the effect of metformin on cutaneous melanoma (CM) survival is sparse and inconclusive. OBJECTIVES: To investigate the association between metformin use and survival among patients with CM and diabetes. METHODS: All adult patients with a primary invasive CM between 2007 and 2014 were identified in the Swedish Melanoma Registry and followed until death, or end of follow-up on 31 December 2017 in this population-based cohort study. Patients with both CM and type 2 diabetes mellitus were assessed further. Overall survival (OS) and melanoma-specific survival (MSS) were the primary endpoints. Cox proportional hazard models estimating crude and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were used comparing peridiagnostic use vs. nonuse of metformin. Dose response was evaluated based on defined daily doses. RESULTS: Among a total of 23 507 patients, 1162 patients with CM and type 2 diabetes mellitus were included in the final cohort, with a median follow-up time of 4.1 years (interquartile range 2.4-6.1). Peridiagnostic metformin use was associated with a significantly decreased risk of death by any cause (HR 0.68, 95% CI 0.57-0.81). Cumulative pre- and postdiagnostic metformin use was also associated with improved OS: the HR for prediagnostic use was 0.90 (95% CI 0.86-0.95) for every 6 months of use and the HR for postdiagnostic use ranged from 0.98 (95% CI 0.97-0.98) for 0-6 months to 0.59 (0.49-0.70) for 24-30 months of use. No association was found for metformin use and MSS. CONCLUSIONS: Metformin use was associated with improved OS in patients with CM and diabetes regardless of timing (pre-, post- or peridiagnostic use) and followed a dose-response pattern. However, further research regarding the underlying mechanisms is warranted.

Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy.

The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale" of Napoli, Italy (INT-NA). To compare patients' clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm-survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.

The Effect of Beta-Adrenergic Blocking Agents in Cutaneous Melanoma-A Nation-Wide Swedish Population-Based Retrospective Register Study.

Previous studies have demonstrated an anti-tumoral effect of beta-adrenergic blocking agents on cutaneous melanoma (CM). The aim of this study was to investigate if beta-adrenergic blocking agents have an impact on survival in Swedish patients with melanoma. A population-based retrospective registry study including all patients diagnosed with a primary invasive melanoma between 2009 and 2013 was performed. Data from the Swedish Melanoma Register were linked to the Swedish Prescribed Drug Registry and the Swedish Cause of Death Register. Cox regression analyses including competing risk assessments were performed. There were 12,738 patients included, out of which 3702 were exposed to beta-blockers vs. 9036 non-exposed patients. Age, male sex, Breslow thickness, ulceration, and nodal status were independent negative prognostic factors for melanoma-specific survival (MSS). Adding beta-blockers to the analysis did not add any prognostic value to the model (HR 1.00, p = 0.98), neither when adjusting for competing risks (HR 0.97, p = 0.61). When specifically analyzing the use of non-selective beta-blockers, the results were still without statistical significance (HR 0.76, p = 0.21). In conclusion, this population-based registry study could not verify that the use of beta-adrenergic blocking agents improve survival in patients with melanoma.

Reply to Comment on Katsarelias, D., et al. "The Effect of Beta-Adrenergic Blocking Agents in Cutaneous Melanoma-A Nation-Wide Swedish Population-Based Retrospective Register Study." Cancers 2020, 12, 3228.

We thank De Giorgi et al for their interest in our study, and for raising important and relevant questions [...].