Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice. The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians. PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included. A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images. All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice.

Aims We performed an audit to evaluate the impact of the COVID-19 pandemic-related delay in the diagnosis of major cancers at a Pathology Unit of a Secondary Care Hospital Network in Italy. Methods A comparison was made among the number of first cellular pathological diagnoses of malignancy made from the 11th to the 20th week of the years 2018–2020. Results Cancer diagnoses fell in 2020 by 39% compared with the average number recorded in 2018 and 2019. Prostate cancer (75%) bladder cancer (66%) and colorectal cancer (CRC; 62%) had the greatest decrease. CRC was identified as carrying a potentially important diagnostic delay. Conclusions For CRC corrective procedures (continuing mass screening tests; patient triage by family physicians; diagnostic procedures alternative to colonoscopy; predictive evaluation on biopsy samples) were advised. Our simple audit model is widely applicable to avoid pandemic-related delay in clinical diagnosis of cancer.

Objectives: We performed data collection concerning the coronavirus disease 2019 (COVID-19) pandemic-related delay in the diagnosis of cancers to individuate proper corrective procedures. Methods: A comparison was made among the number of first pathologic diagnoses of malignancy made from weeks 11 to 20 of 2018, 2019, and 2020 at seven anatomic pathology units serving secondary care hospitals in northern-central Italy. Results: Cancer diagnoses fell in 2020 by 44.9% compared with the average number recorded in 2018 and 2019. Melanoma and nonmelanoma skin cancer represented 56.7% of all missing diagnoses. The diagnostic decrease in colorectal (–46.6%), prostate (–45%), and bladder (–43.6%) cancer was the most relevant among internal malignancies; for prostate, however, high-grade tumors were only moderately affected (–21.7%). Conclusions: Diagnosis of cutaneous malignancies was mostly affected by the lockdown; among internal malignancies, corrective actions were mostly needed for colorectal cancer and invasive bladder cancer.

The “multidimensional” World Health Organization (WHO) classification 2018 of melanocytic tumors encompasses nine melanoma pathways (seven of which for cutaneous melanoma) according to a progression model in which morphologically intermediate melanocytic tumors are cosidered as simulators and/or precursors to melanoma. These “intermediates” can be subclassified into: i) a “classical” subgroup (superficial/thin compound: dysplastic nevus), which is placed within the morphologic and molecular progression spectrum of classical (Clark’s and McGovern’s) melanoma subtypes (superficial spreading and, possibly, nodular); and ii) a “non-classical” subgroup (thick compound/dermal: “melanocytomas”) whose genetic pathways diverge from classical melanoma subtypes. Such a progression model is aimed at giving a conceptual framework for a histopathological classification; however, routine clinicopathological practice strongly suggests that most melanomas arise de novo and that the vast majority of nevi are clinically stable or even involuting over time. Clinicopathological correlation can help identify some severely atypical but benign tumors (e.g.: sclerosing nevus with pseudomelanomatous features) as well as some deceptively bland melanomas (e.g.: lentiginous melanoma; nested melanoma), thereby addressing some ambiguous cases to a correct clinical management. The recently available adjuvant therapy regimens for melanoma raise the problem of a careful distinction between severely atypical (high grade) melanocytoma and “classical” melanoma: conventional morphology can guide an algorithmic approach based on an antibody panel (anti-mutated BRAF, BAP1, PRAME, ALK, TRKA, MET, HRAS-WT, ROS; beta catenin; R1alpha; p16; HMB45; Ki67), a first-line molecular study (identification of hot spot mutations of BRAF and NRAS) and an advanced molecular study (sequencing of NF1, KIT, BRAF, MAP2K1, GNAQ, GNA11, PLCB4, CYSLTR2, HRAS; fusions studies of BRAF, RET, MAP3K8, PRKCA); as a final step, next-generation sequencing can identify melanocytic tumors with rare genetic signatures and melanocytic tumors with a high tumor mutation burden which should be definitely ascribed to the category of classical melanoma with the respective therapeutic options.

Background:  In recent years, several histopathologic variants of blue nevus have been identified, whose clinical and dermoscopic correlates need further clarification.Methods:  A comparative evaluation of histopathologic and dermoscopic features was carried out on 52 melanocytic proliferations belonging to the morphologic spectrum of blue nevus.Results:  On dermoscopy, all lesions showed a homogeneous, structureless pigment pattern, with a curious variety of colors (blue, white–blue, black, brown, and polychromatic). Histopathologically, the majority of blue lesions were common blue nevi (11/19); the majority of white–blue lesions were ‘hypochromic’ (sclerotic, hypomelanotic, and amelanotic) blue nevi (17/22); all the black lesions were ‘compound’ blue nevi (2/2); the majority of brown lesions were combined blue nevi (3/4); the unusual polychromatic dermoscopic appearance was often associated with a histopathologic diagnosis of deep penetrating nevus (2/5).Conclusion:  A dermoscopic–pathologic approach now allows us to identify ‘blue’ (common) blue nevi, ‘white’ (hypochromic) blue nevi, ‘black’ (compound) blue nevi, ‘brown’ (combined) blue nevi, and ‘polychromatic’ (deep penetrating) blue nevi. A better recognition of the many dermoscopic faces of blue nevi is expected to give a morphologic guideline for the clinical management of these lesions.