Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.

The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms that objectively categorise the most relevant features in order of importance. The data set comprises 122 tumours (39 benign, 44 atypical and 39 malignant) from four different countries. BRAF and NRAS mutation status was evaluated in 51. Analysis of variance score was performed to rank 22 clinicopathological variables. The Gaussian naive Bayes algorithm achieved in distinguishing Spitz naevus from malignant spitzoid tumours with an accuracy of 0.95 and kappa score of 0.87, utilising the 12 most important variables. For benign versus non-benign Spitz tumours, the test reached a kappa score of 0.88 using the 13 highest-scored features. Furthermore, for the atypical Spitz tumours (AST) versus Spitz melanoma comparison, the logistic regression algorithm achieved a kappa value of 0.66 and an accuracy rate of 0.85. When the three categories were compared most AST were classified as melanoma, because of the similarities on histological features between the two groups. Our results show promise in supporting the histological classification of these tumours in clinical practice, and provide valuable insight into the use of ML to improve the accuracy and objectivity of this process while minimising interobserver variability. These proposed algorithms represent a potential solution to the lack of a clear threshold for the Spitz/spitzoid tumour classification, and its high accuracy supports its usefulness as a helpful tool to improve diagnostic decision-making.

A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.

Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the diagnostic uncertainty remains until now, especially in the intermediate category known as Spitz tumor of unknown malignant potential (STUMP) or atypical Spitz tumor. Studies developing deep learning (DL) models to diagnose melanocytic tumors using whole slide imaging (WSI) are scarce, and few used ST for analysis, excluding STUMP. To address this gap, we introduce SOPHIE: the first ST dataset with WSIs, including labels as benign, malignant, and atypical tumors, along with the clinical information of each patient. Additionally, we explain two DL models implemented as validation examples using this database.

Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review.

The rise of Artificial Intelligence (AI) has shown promising performance as a support tool in clinical pathology workflows. In addition to the well-known interobserver variability between dermatopathologists, melanomas present a significant challenge in their histological interpretation. This study aims to analyze all previously published studies on whole-slide images of melanocytic tumors that rely on deep learning techniques for automatic image analysis. Embase, Pubmed, Web of Science, and Virtual Health Library were used to search for relevant studies for the systematic review, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Articles from 2015 to July 2022 were included, with an emphasis placed on the used artificial intelligence methods. Twenty-eight studies that fulfilled the inclusion criteria were grouped into four groups based on their clinical objectives, including pathologists versus deep learning models (n = 10), diagnostic prediction (n = 7); prognosis (n = 5), and histological features (n = 6). These were then analyzed to draw conclusions on the general parameters and conditions of AI in pathology, as well as the necessary factors for better performance in real scenarios.

An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images.

Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic lesions due to their ambiguous morphological features. The gold standard for its diagnosis and prognosis is the analysis of skin biopsies. In this process, dermatopathologists visualize skin histology slides under a microscope, in a highly time-consuming and subjective task. In the last years, computer-aided diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in daily clinical practice. Nevertheless, no automatic CAD systems have yet been proposed for the analysis of spitzoid lesions. Regarding common melanoma, no system allows both the selection of the tumor region and the prediction of the benign or malignant form in the diagnosis. Motivated by this, we propose a novel end-to-end weakly supervised deep learning model, based on inductive transfer learning with an improved convolutional neural network (CNN) to refine the embedding features of the latent space. The framework is composed of a source model in charge of finding the tumor patch-level patterns, and a target model focuses on the specific diagnosis of a biopsy. The latter retrains the backbone of the source model through a multiple instance learning workflow to obtain the biopsy-level scoring. To evaluate the performance of the proposed methods, we performed extensive experiments on a private skin database with spitzoid lesions. Test results achieved an accuracy of 0.9231 and 0.80 for the source and the target models, respectively. In addition, the heat map findings are directly in line with the clinicians' medical decision and even highlight, in some cases, patterns of interest that were overlooked by the pathologist.

Advantage of Z-stacking for teleconsultation between the USA and Colombia.

INTRODUCTION: There is an emerging need for telecytology in Colombia as the demand for cytopathology has increased. However, due to economic and technological constraints telecytology services are limited. Our aim was to evaluate the diagnostic feasibility of using whole slide imaging with and without Z-stacking for telecytology in Colombia, South America. METHODS: Archival glass slides from 17 fine needle aspiration smears were digitized employing whole slide imaging (WSI) (Nanozoomer 2.0 HT, Hamamatsu) in one Z-plane at 40x, and panoramic digital imaging (Panoptiq system, ViewsIQ) combining low-magnification digital maps with embedded 40x Z-stacks of representative regions of interest. Fourteen Colombian pathologists reviewed both sets of digital images. Diagnostic concordance, time to diagnosis, image quality (scale 1-10), usefulness of Z-stacking, and technical difficulties were recorded. RESULTS: Image quality scored by pathologists was on average 8.3 for WSI and 8.7 for panoramic images with Z-stacks (P = .03). However, diagnostic concordance was not impacted by image quality ranking. In the majority of cases (72.4%) pathologists deemed Z-stacking to be diagnostically helpful. Technical issues related to Z-stack video performance constituted only a minor proportion of technical problems reported. Slow downloads and crashing of files while viewing were mostly experienced with larger WSI files. CONCLUSION: This study demonstrated that international telecytology for diagnostic purposes is feasible. Panoramic images had to be acquired manually, but were of suitable diagnostic quality and generated smaller image files associated with fewer technical errors. Z-stacking proved to be useful in the majority of cases and is thus recommended for telecytology.

Primary Gastrointestinal Kaposi's Sarcoma in a Patient with Human Immunodeficiency Virus.

Gastrointestinal bleeding in HIV patients secondary to coinfection by HHV8 and development of Kaposi's sarcoma (KS) is a rare complication even if no skin lesions are detected on physical examination. This article indicates which patients might develop this type of clinical sign and also tries to recall that absence of skin lesions never rules out the presence of KS, especially if gastrointestinal involvement is documented. Gastrointestinal bleeding in terms of hematemesis has rarely been reported in the literature. We review some important clinical findings, diagnosis, and treatment approach. We present the case of an HIV patient who presented to the emergency department with hematemesis and gastrointestinal signs of KS on upper gastrointestinal endoscopy without any dermatological involvement.