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. OBJECTIVE: To develop the first human-3D-melanoma-histology-model with full data and code availability. Further, to evaluate the 3D-simulation together with experienced pathologists in the field and discuss the implications of digital 3D-models for the future of digital pathology. METHODS: A malignant melanoma of the skin was digitised via 3 µm cuts by a slide scanner; an open-source software was then leveraged to construct the 3D model. A total of nine pathologists from four different countries with at least 10 years of experience in the histologic diagnosis of melanoma tested the model and discussed their experiences as well as implications for future pathology. RESULTS: We successfully constructed and tested the first 3D-model of human melanoma. Based on testing, 88.9% of pathologists believe that the technology is likely to enter routine pathology within the next 10 years; advantages include a better reflectance of anatomy, 3D assessment of symmetry and the opportunity to simultaneously evaluate different tissue levels at the same time; limitations include the high consumption of tissue and a yet inferior resolution due to computational limitations. CONCLUSIONS: 3D-histology-models are promising for digital pathology of cancer and melanoma specifically, however, there are yet limitations which need to be carefully addressed.

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. 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. RESULTS: 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. CONCLUSIONS: 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.

Agar pre-embedding of small skin biopsies: real-life benefits and challenges in high throughput pathology laboratories.

Paraffin embedding of small, thin tissue samples requires specific expertise for optimal orientation before tissue sectioning. This study evaluates the real-life utility of the agar pre-embedding technique for small skin biopsies with regards to lengthening of work times, problems in orientation (re-embedding) and ancillary techniques (immunohistochemistry and in situ hybridisation) between two high work flow pathology laboratories, one of which routinely uses the agar pre-embedding technique and one which does not. The mean time required for pre-embedding in agar was 30.4 s, but time for paraffin embedding for agar pre-embedded samples was shorter than the traditional method (177 vs 296 s; p<0.005). The number of skin samples requiring re-embedding was significantly higher with the traditional embedding method (p<0.005). No problems in immunoreactivity were observed in all 1900 reactions performed with 17 different antibodies. Fluorescence in situ hybridisation analysis was optimised with a prolonged protease K incubation time (21 vs 18 min).

The impact of molecular morphology techniques on the expert diagnosis in melanocytic skin neoplasms.

Thirteen melanocytic skin neoplasms with a consultation diagnosis by A. Bernard Ackerman were submitted to immunohistochemistry for HMB-45, Ki67, cyclin D1, e-cadherin, and p16; 9/13 cases underwent fluorescence in situ hybridization (FISH) test targeting 6p25 (RREB1), 6q23 (MYB), centromere 6 (Cep6), and 11q13 (CCND1), as well as the centromere 7 (Cep7). A "consensus diagnosis" among 3 experts was also advocated both before and after morphomolecular information. Three neoplasms with a consultation diagnosis of Spitz nevus showed at least 3 abnormal immunohistochemical patterns; 2 of these cases were also FISH-positive for CCND1 gain, but none of them had a final consensus diagnosis of melanoma. Two neoplasms with a consultation diagnosis of congenital nevus received a consensus diagnosis of melanoma. Molecular morphology techniques can highlight the atypical features of melanocytic neoplasms and support existence of a morphobiologic "spectrum": This should be mirrored in the final report by abandoning the dichotomic (benign vs malignant) diagnostic approach.

Bilateral myoepithelioma of the nasopharynx: a case report.

Myoepithelioma is a rare salivary tumour which is usually encountered in the parotid gland. Outside the parotid, myoepithelioma may arise from minor salivary glands of the hard palate and less frequently in bronchi or breast. In this report we describe the unique case of a metachronous bilateral nasopharyngeal myoepithelioma arising from the tubaric regions. Its microscopic features, immunophenotype, and the differential diagnosis together with a review of literature are discussed.