Discordance, accuracy and reproducibility study of pathologists' diagnosis of melanoma and melanocytic tumors.

Accurate melanoma diagnosis is crucial for patient outcomes and reliability of AI diagnostic tools. We assess interrater variability among eight expert pathologists reviewing histopathological images and clinical metadata of 792 melanoma-suspicious lesions prospectively collected at eight German hospitals. Moreover, we provide access to the largest panel-validated dataset featuring dermoscopic and histopathological images with metadata.

A 3-dimensional histology computer model of malignant melanoma and its implications for digital pathology.

Background: Historically, cancer diagnoses have been made by pathologists using two-dimensional histological slides. However, with the advent of digital pathology and artificial intelligence, slides are being digitised, providing new opportunities to integrate their information. Since nature is 3-dimensional (3D), it seems intuitive to digitally reassemble the 3D structure for diagnosis.

Cuticular Poroma: A Rare Poroma Variant Simulating a Malignant Neoplasm That Often Harbors YAP1::NUTM1 Fusions Similar to Their Conventional Counterparts.

Cuticular poroma is a rare variant of poroma composed of exclusively or predominantly cuticular cells, namely of large cells with ample eosinophilic cytoplasm. We report 7 cases of this rare tumor identified among 426 neoplasms diagnosed as poroma or porocarcinoma. The patients were 4 males and 3 females, ranging in age from 18 to 88 years.

Dataset for the Reporting of Merkel Cell Carcinoma: Recommendations From the International Collaboration on Cancer Reporting (ICCR).

Accurate and complete pathology reports are critical for the optimal management of cancer patients. Protocols for the pathologic reporting of Merkel cell carcinoma (MCC) have been developed independently by the Royal College of Pathologists (UK) and the College of American Pathologists. In this study, data elements for pathologic reporting of MCC were analyzed by an international panel of pathologists and clinicians with the aim of developing a common, internationally agreed upon dataset useful for clinical practice.

Genetic and methylation profiles distinguish benign, malignant and spitzoid melanocytic tumors.

Accurate classification of melanocytic tumors is important for prognostic evaluation, treatment and follow-up protocols of patients. The majority of melanocytic proliferations can be classified solely based on clinical and pathological criteria, however in select cases a definitive diagnostic assessment remains challenging and additional diagnostic biomarkers would be advantageous. We analyzed melanomas, nevi, Spitz nevi and atypical spitzoid tumors using parallel sequencing (exons of 611 genes and 507 gene translocation analysis) and methylation arrays (850k Illumina EPIC).

A rare case of cutaneous metastasis of distal phalanx chondrosarcoma.

Chondrosarcoma is the second-most common primary malignant bone tumor but chondrosarcoma of small bones of the hand is extremely rare, representing less than 2% of all cases, with exceedingly rare skin metastases. Cutaneous metastases of chondrosarcoma represent less than 3% of all cutaneous metastases. According to PubMed, there are only four previous case reports of cutaneous metastases originating from chondrosarcoma of small bones of the hand.

Clinicopathological features, MCPyV status and outcomes of Merkel cell carcinoma in solid-organ transplant recipients: a retrospective, multicentre cohort study.

Background: The proportion of Merkel cell carcinomas (MCCs) in solid-organ transplant recipients (SOTR) harbouring Merkel cell polyomavirus (MCPyV) is unknown, as are factors affecting their outcomes.

Objective: To describe clinicopathological features of MCC in SOTR, investigate the tumoral MCPyV-status and identify factors associated with tumour outcomes.

Methods: Retrospective, international, cohort-study.

Explainable artificial intelligence in skin cancer recognition: A systematic review.

Background: Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence (XAI) is often suggested as a solution to this problem.

[Immunohistochemical analysis of a hypoxia-associated signature in melanomas with positive and negative sentinel lymph nodes : Hypoxia-associated signature of primary cutaneous melanomas].

Metabolic reprogramming mediated by hypoxia-inducible factors and its downstream targets plays a crucial role in many human malignancies. Excessive proliferation of tumor cells under hypoxic conditions leads to metabolic reprogramming and altered gene expression enabling tumors to adapt to their hypoxic environment. Here we analyzed the metabolic signatures of primary cutaneous melanomas with positive and negative sentinel node status in order to evaluate potential differences in their metabolic signature.

Cutaneous epithelioid haemangiomas show somatic mutations in the mitogen-activated protein kinase pathway.

Background: Epithelioid haemangioma (EH) arising from the skin is a benign vascular tumour with marked inflammatory cell infiltration, which exhibits a high tendency to persist and frequently recurs after resection. So far, the underlying pathogenesis is largely elusive.

Objectives: To identify genetic alterations by next-generation sequencing and/or droplet digital polymerase chain reaction (ddPCR) in cutaneous EH.

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.

Background: 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.

Objective: 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.

Methods: 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.

Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours.

Aim: Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this study, we therefore aimed to develop a digital biomarker that can predict lymph node metastasis non-invasively from digitised H&E slides of primary melanoma tumours.

Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.

Background: Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy.

A Problem of Classification: 2 Cases of Epstein-Barr Virus + Primary Cutaneous Plasmacytoma Arising in Immunocompetent Elderly Patients.

Primary extramedullary plasmacytoma is rare monoclonal proliferation of plasma cells, which arise in various nonosseous anatomic locations without detectable underlying systemic disease. Historically, cutaneous infiltrates rich in mature neoplastic plasma cells have fallen into one of the following categories, plasmacytoma, lymphoplasmacytic lymphoma, and marginal zone lymphoma, which included immunocytoma. Since 2005, each of these was subsumed under the marginal zone lymphoma umbrella, largely on the basis of acknowledged diagnostic difficulties in some of these cases.

Rubella vaccine-induced granulomas are a novel phenotype with incomplete penetrance of genetic defects in cytotoxicity.

Background: Rubella virus-induced granulomas have been described in patients with various inborn errors of immunity. Most defects impair T-cell immunity, suggesting a critical role of T cells in rubella elimination. However, the molecular mechanism of virus control remains elusive.

Multicentric dermatofibrosarcoma protuberans in a child with severe combined immunodeficiency due to adenosine deaminase deficiency.

We report the case of a 4-year-old boy, post-human stem cell transplantation for severe combined immunodeficiency (SCID) due to adenosine deaminase deficiency (ADA), who developed multiple dermatofibrosarcoma protuberans (DFSP). We hypothesize a role for chimerism leading to accumulation of toxic metabolites which can cause DNA strand breaks and inhibit lymphocyte activation. Patients with ADA-SCID should remain under lifelong dermatologic surveillance as DFSP lesions can be quite inconspicuous.

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification.

Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses.

Objectives: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone.

Methods: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques.

TERT promoter mutation in sebaceous neoplasms.

TERT promoter (TERTp) mutations widely occur in multiple human neoplasms, and they have been related to different clinicopathological features. To date, this mutation has not been identified in sebaceous tumors. Here, we analyzed TERTp mutations in 91 sebaceous neoplasms (17 adenomas, 45 sebaceomas, and 29 carcinomas).