A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection.

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder-decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Tumores de colisión en la piel: un hallazgo incidental en la mayoría de los casos. Estudio retrospectivo y revisión de la literatura / Collision tumours in skin: usually an incidental finding. A retrospective study with literature revision

Se denomina tumor de colisión (TC) a la coexistencia de dos o más neoplasias independientes en la misma resección. Suelen ser hallazgos incidentales en la piel, de patogénesis y prevalencia desconocidas, con pocas referencias en la literatura. Aquí mostramos un estudio retrospectivo de TC diagnosticados por un dermatopatólogo entre los años 2019-2022 en nuestro centro. Se han definido las lesiones de manera independiente y organizado cada colisión en categorías: benigno-benigno (BB), benigno-maligno (BM) y maligno-maligno (MM). Del total de 108 TC (1,4% de las biopsias totales del dermatopatólogo en ese periodo), se detecta que la colisión más frecuente es la formada entre BM (48,5%), con un carcinoma basocelular (CBC) como lesión maligna más frecuente de forma global y con un nevus melanocítico (NM) como lesión benigna principal. Se ha realizado el análisis estadístico de los resultados con el software Stata 14.2, detectando una diferencia estadísticamente significativa entre edad y tipo de colisión. (AU)

A collision tumour (CT) is a neoplastic lesion comprised of two or more distinct cell populations that maintain distinct borders. Mostly, these are incidental findings in skin biopsies, whose pathologic mechanism and prevalence remain unknown, with few references among literature. Here, we present a retrospective study of CT, diagnosed by a dermatopathologist in our hospital between 2019-2022. Lesions have been defined individually and organized into three categories: benign-benign (BB), benign-malignant (BM) and malignant-malignant (MM). A total of 108 CT were diagnosed (1,4% of the biopsies from the dermatopathologist during this period), from which BM was the most frequent collision (48,5%). Globally, basal cell carcinoma (BCC) was the main malignant lesion and melanocytic nevus (MN) the main benign lesion. We have used the software Stata 14.2 in order to analyse results, and we have detected a statistically significant difference between age and collision type. (AU)

Tumores de colisión en la piel: un hallazgo incidental en la mayoría de los casos. Estudio retrospectivo y revisión de la literatura / Collision tumours in skin: usually an incidental finding. A retrospective study with literature revision

Se denomina tumor de colisión (TC) a la coexistencia de dos o más neoplasias independientes en la misma resección. Suelen ser hallazgos incidentales en la piel, de patogénesis y prevalencia desconocidas, con pocas referencias en la literatura. Aquí mostramos un estudio retrospectivo de TC diagnosticados por un dermatopatólogo entre los años 2019-2022 en nuestro centro. Se han definido las lesiones de manera independiente y organizado cada colisión en categorías: benigno-benigno (BB), benigno-maligno (BM) y maligno-maligno (MM). Del total de 108 TC (1,4% de las biopsias totales del dermatopatólogo en ese periodo), se detecta que la colisión más frecuente es la formada entre BM (48,5%), con un carcinoma basocelular (CBC) como lesión maligna más frecuente de forma global y con un nevus melanocítico (NM) como lesión benigna principal. Se ha realizado el análisis estadístico de los resultados con el software Stata 14.2, detectando una diferencia estadísticamente significativa entre edad y tipo de colisión. (AU)

A collision tumour (CT) is a neoplastic lesion comprised of two or more distinct cell populations that maintain distinct borders. Mostly, these are incidental findings in skin biopsies, whose pathologic mechanism and prevalence remain unknown, with few references among literature. Here, we present a retrospective study of CT, diagnosed by a dermatopathologist in our hospital between 2019-2022. Lesions have been defined individually and organized into three categories: benign-benign (BB), benign-malignant (BM) and malignant-malignant (MM). A total of 108 CT were diagnosed (1,4% of the biopsies from the dermatopathologist during this period), from which BM was the most frequent collision (48,5%). Globally, basal cell carcinoma (BCC) was the main malignant lesion and melanocytic nevus (MN) the main benign lesion. We have used the software Stata 14.2 in order to analyse results, and we have detected a statistically significant difference between age and collision type. (AU)

Restrospective reappraisal of the prognostic classification of spitzoid melanocytic neoplasms after BRAF and NRAS mutation characterisation: a single institution experience.

AIMS: The current WHO classification of melanocytic tumours excludes neoplasms showing BRAF or NRAS mutations from the Spitz category. This study aimed to review and reclassify atypical melanocytic tumours with spitzoid morphological features diagnosed between 2009 and 2021 in our hospital after expanding the molecular profile, including BRAF and NRAS mutations in all cases. METHODS AND RESULTS: A total of 71 neoplasms showing spitzoid features (Spitz-like) and atypia were included. The risk of progression of tumours was first studied by integrating the morphology, immunohistochemistry (p16, Ki67, HMB45 and PRAME) and fluorescence in-situ hybridisation (FISH) results (melanoma multiprobe and 9p21). In a second step, after expanding the molecular study, including BRAF and NRAS mutational status, the neoplasms were finally classified into four subgroups: atypical Spitz tumour (AST, n = 45); BRAF-mutated naevus/low-grade melanocytoma with spitzoid morphology (BAMS, n = 2); Spitz melanoma (SM, n = 14); and BRAF or NRAS mutated melanoma with spitzoid features (MSF, n = 10). Follow-up of patients revealed uneventful results for AST and BAMS. Only one SM presented lymph node metastasis after 134 months. Conversely, patients with MSF showed an unfavourable outcome: three developed lymph node metastases after a mean time of 22 months, with one patient presenting distant metastasis and dying of the disease 64 months from diagnosis. The progression-free survival showed significant differences between the four groups of spitzoid tumours (P < 0.001) and between both melanoma subtypes (P = 0.012). CONCLUSIONS: The classification and prognostication of atypical neoplasms with spitzoid features requires the integration of histomorphology with the molecular investigation of tumours, which should include BRAF and NRAS mutational status.

Registro de linfomas cutáneos primarios (RELCP) de la AEDV: datos tras 5 años de funcionamiento / Primary Cutaneous Lymphoma Registry of the Spanish Academy of Dermatology and Venereology (AEDV): Data for the First 5 Years

Antecedentes y objetivos Los linfomas cutáneos primarios (LCP) son un conjunto de entidades poco frecuentes. En febrero del 2018 se describieron los resultados del primer año de funcionamiento del Registro de linfomas cutáneos primarios de la AEDV. En el presente trabajo actualizamos los resultados tras 5 años de funcionamiento. Pacientes y métodos Registro de enfermedad de pacientes con LCP. Se recogieron datos prospectivamente de los pacientes, incluyendo diagnóstico, tratamientos, pruebas realizadas y estado actual del paciente. Se realizó un análisis descriptivo. Resultados En diciembre del 2021 se había incluido a un total de 2020 pacientes en el Registro, pertenecientes a 33 hospitales españoles. El 59% fueron hombres, la edad media fue de 62,2 años. Se agruparon en 4grandes grupos diagnósticos: micosis fungoide/síndrome de Sézary (1.112, 55%), LCP de células B (547, 27,1%), trastornos linfoproliferativos de células T CD30+(222, 11%) y otros linfomas T (116, 5,8%). La mayoría presentó estadio T1, encontrándose actualmente casi el 75% en remisión completa (43,5%) o enfermedad estable (EE; 27%). Los tratamientos más usados fueron corticoides tópicos (1.369, 67,8%), fototerapia (890, 44,1%), cirugía (412, 20,4%) y radioterapia (384, 19%). Conclusión Las características del paciente con LCP en España no difieren de otras series. El mayor tamaño del registro permite precisar mejor los datos con respecto a los resultados del primer año. Este registro facilita al grupo de linfomas de la AEDV realizar investigación clínica, surgiendo ya trabajos publicados de dicho registro (AU)

Background and objective Primary cutaneous lymphomas (PCL) are uncommon. Observations based on the first year of data from the Spanish Registry of Primary Cutaneous Lymphomas (RELCP, in its Spanish abbreviation) of the Spanish Academy of Dermatology and Venereology (AEDV) were published in February 2018. This report covers RELCP data for the first 5 years. Patients and methods RELCP data were collected prospectively and included diagnosis, treatments, tests, and the current status of patients. We compiled descriptive statistics of the data registered during the first 5 years. Results Information on 2020 patients treated at 33 Spanish hospitals had been included in the RELCP by December 2021. Fifty-nine percent of the patients were men; the mean age was 62.2 years. The lymphomas were grouped into 4 large diagnostic categories: mycosis fungoides/Sézary syndrome, 1112 patients (55%); primary B-cell cutaneous lymphoma, 547 patients (27.1%); primary CD30+lymphoproliferative disorders, 222 patients (11%), and other T-cell lymphomas, 116 patients (5.8%). Nearly 75% of the tumors were registered in stage I. After treatment, 43.5% achieved complete remission and 27% were stable at the time of writing. Treatments prescribed were topical corticosteroids (1369 [67.8%]), phototherapy (890 patients [44.1%]), surgery (412 patients [20.4%]), and radiotherapy (384 patients [19%]). Conclusion The characteristics of cutaneous lymphomas in Spain are similar to those reported for other series. The large size of the RELCP registry at 5 years has allowed us to give more precise descriptive statistics than in the first year. This registry facilitates the clinical research of the AEDV's lymphoma interest group, which has already published articles based on the RELCP data (AU)

[Translated article] Primary Cutaneous Lymphoma Registry of the Spanish Academy of Dermatology and Venereology (AEDV): Data for the First 5 Years / Registro de linfomas cutáneos primarios (RELCP) de la AEDV: datos tras 5 años de funcionamiento

Background and objective Primary cutaneous lymphomas (PCL) are uncommon. Observations based on the first year of data from the Spanish Registry of Primary Cutaneous Lymphomas (RELCP, in its Spanish abbreviation) of the Spanish Academy of Dermatology and Venereology (AEDV) were published in February 2018. This report covers RELCP data for the first 5 years. Patients and methods RELCP data were collected prospectively and included diagnosis, treatments, tests, and the current status of patients. We compiled descriptive statistics of the data registered during the first 5 years. Results Information on 2020 patients treated at 33 Spanish hospitals had been included in the RELCP by December 2021. Fifty-nine percent of the patients were men; the mean age was 62.2 years. The lymphomas were grouped into 4 large diagnostic categories: mycosis fungoides/Sézary syndrome, 1112 patients (55%); primary B-cell cutaneous lymphoma, 547 patients (27.1%); primary CD30+lymphoproliferative disorders, 222 patients (11%), and other T-cell lymphomas, 116 patients (5.8%). Nearly 75% of the tumors were registered in stage I. After treatment, 43.5% achieved complete remission and 27% were stable at the time of writing. Treatments prescribed were topical corticosteroids (1369 [67.8%]), phototherapy (890 patients [44.1%]), surgery (412 patients [20.4%]), and radiotherapy (384 patients [19%]). Conclusion The characteristics of cutaneous lymphomas in Spain are similar to those reported for other series. The large size of the RELCP registry at 5 years has allowed us to give more precise descriptive statistics than in the first year. This registry facilitates the clinical research of the AEDV's lymphoma interest group, which has already published articles based on the RELCP data (AU)

Antecedentes y objetivos Los linfomas cutáneos primarios (LCP) son un conjunto de entidades poco frecuentes. En febrero del 2018 se describieron los resultados del primer año de funcionamiento del Registro de linfomas cutáneos primarios de la AEDV. En el presente trabajo actualizamos los resultados tras 5 años de funcionamiento. Pacientes y métodos Registro de enfermedad de pacientes con LCP. Se recogieron datos prospectivamente de los pacientes, incluyendo diagnóstico, tratamientos, pruebas realizadas y estado actual del paciente. Se realizó un análisis descriptivo. Resultados En diciembre del 2021 se había incluido a un total de 2020 pacientes en el Registro, pertenecientes a 33 hospitales españoles. El 59% fueron hombres, la edad media fue de 62,2 años. Se agruparon en 4grandes grupos diagnósticos: micosis fungoide/síndrome de Sézary (1.112, 55%), LCP de células B (547, 27,1%), trastornos linfoproliferativos de células T CD30+(222, 11%) y otros linfomas T (116, 5,8%). La mayoría presentó estadio T1, encontrándose actualmente casi el 75% en remisión completa (43,5%) o enfermedad estable (EE; 27%). Los tratamientos más usados fueron corticoides tópicos (1.369, 67,8%), fototerapia (890, 44,1%), cirugía (412, 20,4%) y radioterapia (384, 19%). Conclusión Las características del paciente con LCP en España no difieren de otras series. El mayor tamaño del registro permite precisar mejor los datos con respecto a los resultados del primer año. Este registro facilita al grupo de linfomas de la AEDV realizar investigación clínica, surgiendo ya trabajos publicados de dicho registro (AU)

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks.

Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs). DUNEScan allows users to upload a skin lesion image, and quickly compares the mean and the variance estimates provided by a number of new and traditional CNN models. Moreover, our web server uses the Grad-CAM and UMAP algorithms to visualize the classification manifold for the user's input, hence providing crucial information about its closeness to skin lesion images  from the popular ISIC database. DUNEScan is freely available at: https://www.dunescan.org .

Multi-features extraction based on deep learning for skin lesion classification.

For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features. In this paper, we investigate the efficiency of using 17 commonly pre-trained convolutional neural networks (CNN) architectures as feature extractors and of 24 machine learning classifiers to evaluate the classification of skin lesions from two different datasets: ISIC 2019 and PH2. In this research, we find out that a DenseNet201 combined with Fine KNN or Cubic SVM achieved the best results in accuracy (92.34% and 91.71%) for the ISIC 2019 dataset. The results also show that the suggested method outperforms others approaches with an accuracy of 99% on the PH2 dataset.

Characterization of multiple diagnostic terms in melanocytic skin lesion pathology reports.

BACKGROUND: Histopathologically ambiguous melanocytic lesions lead some pathologists to list multiple diagnostic considerations in the pathology report. The frequency and circumstance of multiple diagnostic considerations remain poorly characterized. METHODS: Two hundred and forty skin biopsy samples were interpreted by 187 pathologists (8976 independent diagnoses) and classified according to a diagnostic/treatment stratification (MPATH-Dx). RESULTS: Multiple diagnoses in different MPATH-Dx classes were used in n = 1320 (14.7%) interpretations, with 97% of pathologists and 91% of cases having at least one such interpretation. Multiple diagnoses were more common for intermediate risk lesions and are associated with greater subjective difficulty and lower confidence. We estimate that 6% of pathology reports for melanocytic lesions in the United States contain two diagnoses of different MPATH-Dx prognostic classes, and 2% of cases are given two diagnoses with significant treatment implications. CONCLUSIONS: Difficult melanocytic diagnoses in skin may necessitate multiple diagnostic considerations; however, as patients increasingly access their health records and retrieve pathology reports (as mandated by US law), uncertainty should be expressed unambiguously.

Deep learning data augmentation for Raman spectroscopy cancer tissue classification.

Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data.

Assessment of a Diagnostic Classification System for Management of Lesions to Exclude Melanoma.

Importance: The proposed MOLEM (Management of Lesion to Exclude Melanoma) schema is more clinically relevant than Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MATH-Dx) for the management classification of melanocytic and nonmelanocytic lesions excised to exclude melanoma. A more standardized way of establishing diagnostic criteria will be crucial in the training of artificial intelligence (AI) algorithms. Objective: To examine pathologists' variability, reliability, and confidence in reporting melanocytic and nonmelanocytic lesions excised to exclude melanoma using the MOLEM schema in a population of higher-risk patients. Design, Setting, and Participants: This cohort study enrolled higher-risk patients referred to a primary care skin clinic in New South Wales, Australia, between April 2019 and December 2019. Baseline demographic characteristics including age, sex, and related clinical details (eg, history of melanoma) were collected. Patients with lesions suspicious for melanoma assessed by a primary care physician underwent clinical evaluation, dermoscopy imaging, and subsequent excision biopsy of the suspected lesion(s). A total of 217 lesions removed and prepared by conventional histologic method and stained with hematoxylin-eosin were reviewed by up to 9 independent pathologists for diagnosis using the MOLEM reporting schema. Pathologists evaluating for MOLEM schema were masked to the original histopathologic diagnosis. Main Outcomes and Measures: Characteristics of the lesions were described and the concordance of cases per MOLEM class was assessed. Interrater agreement and the agreement between pathologists' ratings and the majority MOLEM diagnosis were calculated by Gwet AC1 with quadratic weighting applied. The diagnostic confidence of pathologists was then assessed. Results: A total of 197 patients were included in the study (102 [51.8%] male; 95 [48.2%] female); mean (SD) age was 64.2 (15.8) years (range, 24-93 years). Overall, 217 index lesions were assessed with a total of 1516 histological diagnoses. Of 1516 diagnoses, 677 (44.7%) were classified as MOLEM class I; 120 (7.9%) as MOLEM class II; 564 (37.2%) as MOLEM class III; 114 (7.5%) as MOLEM class IV; and 55 (3.6%) as MOLEM class V. Concordance rates per MOLEM class were 88.6% (class I), 50.8% (class II), 76.2% (class III), 77.2% (class IV), and 74.2% (class V). The quadratic weighted interrater agreement was 91.3%, with a Gwet AC1 coefficient of 0.76 (95% CI, 0.72-0.81). The quadratic weighted agreement between pathologists' ratings and majority MOLEM was 94.7%, with a Gwet AC1 coefficient of 0.86 (95% CI, 0.84-0.88). The confidence in diagnosis data showed a relatively high level of confidence (between 1.0 and 1.5) when diagnosing classes I (mean [SD], 1.3 [0.3]), IV (1.3 [0.3]) and V (1.1 [0.1]); while classes II (1.8 [0.2]) and III (1.5 [0.4]) were diagnosed with a lower level of pathologist confidence (≥1.5). The quadratic weighted interrater confidence rating agreement was 95.2%, with a Gwet AC1 coefficient of 0.92 (95% CI, 0.90-0.94) for the 1314 confidence ratings collected. The confidence agreement for each MOLEM class was 95.0% (class I), 93.5% (class II), 95.3% (class III), 96.5% (class IV), and 97.5% (class V). Conclusions and Relevance: The proposed MOLEM schema better reflects clinical practice than the MPATH-Dx schema in lesions excised to exclude melanoma by combining diagnoses with similar prognostic outcomes for melanocytic and nonmelanocytic lesions into standardized classification categories. Pathologists' level of confidence appeared to follow the MOLEM schema diagnostic concordance trend, ie, atypical naevi and melanoma in situ diagnoses were the least agreed upon and the most challenging for pathologists to confidently diagnose.

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories.

The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC's early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist's experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.

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.

A benchmark for neural network robustness in skin cancer classification.

BACKGROUND: One prominent application for deep learning-based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data. OBJECTIVE: The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured. METHODS: Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it. RESULTS: The benchmark contains three data sets-Skin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)-and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations. CONCLUSIONS: This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers.

¿Cuánta carga asistencial supone la enfermedad tumoral cutánea en la actividad ambulatoria en la Dermatología española? Resultados del muestreo aleatorio nacional DIADERM / What Proportion of the Caseload at Dermatology Outpatient Clinics in Spain Do Skin Tumors Account for? Results from the DIADERM National Random Sampling Project

Introducción y objetivo La patología tumoral conforma una parte esencial de la actividad dermatológica. El presente estudio pretende analizar la carga de los tumores cutáneos en la actividad dermatológica pública y privada del sistema de salud español. Material y método Estudio observacional de corte transversal de 2 períodos de tiempo describiendo los diagnósticos realizados en consultas externas dermatológicas, obtenidos a través de la encuesta anónima DIADERM, realizada a una muestra aleatoria y representativa de dermatólogos. A partir de la codificación de diagnósticos CIE-10, se seleccionó toda la patología tumoral (165 diagnósticos codificados en los 2 períodos), que se clasificó en 24 grupos, posteriormente subclasificada en patología benigna y maligna, melanocítica y no melanocítica. Resultados El 46,2% de los diagnósticos fueron de patología tumoral. El 18,5% de los diagnósticos globales se debió a tumores malignos (incluyendo entre estos diagnósticos los tumores queratinocíticos in situ). De los primeros 10 diagnósticos de patología tumoral en frecuencia, 4 eran malignos: tumores queratinocíticos in situ, carcinoma basocelular, melanoma y carcinoma espinocelular. Se encontraron algunas diferencias significativas entre tumores benignos y malignos atendiendo al ámbito de su asistencia (público/privado), así como a factores geográficos. Conclusión El cáncer cutáneo tiene un peso importante en la asistencia dermatológica en España. Se pueden observar algunas diferencias en función del ámbito de atención público/privado y de otros factores (AU)Introduction and objective

A significant part of a dermatologist's activity involves the diagnosis and management of tumors. The aim of this study was to analyze the caseload at public and private dermatology outpatient clinics in Spain to determine the proportion of tumor diagnoses. Material and method Observational cross-sectional study of diagnoses made in dermatology outpatient clinics during 2 data-collection periods in the DIADERM study, an anonymous survey of a random, representative sample of dermatologists across Spain. Diagnoses made during the 2 periods were coded according to the CIE-10. There were 165 tumor-related codes, classified into 24 groups. For the purpose of this study, these groups were then reduced to benign melanocytic lesions, malignant melanocytic lesions, benign nonmelanocytic lesions, and malignant nonmelanocytic lesions. Results Tumors accounted for 46.2% of all diagnoses; 18.5% of the tumors were malignant (a category that included in situ forms of keratinocyte cancers). Four of the 10 most common diagnoses were of malignant tumors: in situ keratinocyte cancers, basal cell carcinoma, melanoma, and squamous cell carcinoma. Significant differences were observed between malignant and benign tumors according to type of practice (public vs. private) and geographic region. Conclusion Skin cancer accounts for a significant part of the dermatologist's caseload in Spain. Differences can be observed depending on the public/private healthcare setting and other factors (AU)

Expression Profiles of ASIC1/2 and TRPV1/4 in Common Skin Tumors.

The acid-sensing ion channels ASIC1 and ASIC2, as well as the transient receptor potential vanilloid channels TRPV1 and TRPV4, are proton-gated cation channels that can be activated by low extracellular pH (pHe), which is a hallmark of the tumor microenvironment in solid tumors. However, the role of these channels in the development of skin tumors is still unclear. In this study, we investigated the expression profiles of ASIC1, ASIC2, TRPV1 and TRPV4 in malignant melanoma (MM), squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and in nevus cell nevi (NCN). We conducted immunohistochemistry using paraffin-embedded tissue samples from patients and found that most skin tumors express ASIC1/2 and TRPV1/4. Striking results were that BCCs are often negative for ASIC2, while nearly all SCCs express this marker. Epidermal MM sometimes seem to lack ASIC1 in contrast to NCN. Dermal portions of MM show strong expression of TRPV1 more frequently than dermal NCN portions. Some NCN show a decreasing ASIC1/2 expression in deeper dermal tumor tissue, while MM seem to not lose ASIC1/2 in deeper dermal portions. ASIC1, ASIC2, TRPV1 and TRPV4 in skin tumors might be involved in tumor progression, thus being potential diagnostic and therapeutic targets.

An Optimized Method for Skin Cancer Diagnosis Using Modified Thermal Exchange Optimization Algorithm.

Skin cancer is the most common cancer of the body. It is estimated that more than one million people worldwide develop skin cancer each year. Early detection of this cancer has a high effect on the disease treatment. In this paper, a new optimal and automatic pipeline approach has been proposed for the diagnosis of this disease from dermoscopy images. The proposed method includes a noise reduction process before processing for eliminating the noises. Then, the Otsu method as one of the widely used thresholding method is used to characterize the region of interest. Afterward, 20 different features are extracted from the image. To reduce the method complexity, a new modified version of the Thermal Exchange Optimization Algorithm is performed to the features. This improves the method precision and consistency. To validate the proposed method's efficiency, it is implemented to the American Cancer Society database, its results are compared with some state-of-the-art methods, and the final results showed the superiority of the proposed method against the others.

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy.

Genetic splice variants have become of central interest in recent years, as they play an important role in different cancers. Little is known about splice variants in melanoma. Here, we analyzed a genome-wide transcriptomic dataset of benign melanocytic nevi and primary melanomas (n = 80) for the expression of specific splice variants. Using kallisto, a map for differentially expressed splice variants in melanoma vs. benign melanocytic nevi was generated. Among the top genes with differentially expressed splice variants were Ras-related in brain 6B (RAB6B), a member of the RAS family of GTPases, Macrophage Scavenger Receptor 1 (MSR1), Collagen Type XI Alpha 2 Chain (COLL11A2), and LY6/PLAUR Domain Containing 1 (LYPD1). The Gene Ontology terms of differentially expressed splice variants showed no enrichment for functional gene sets of melanoma vs. nevus lesions, but between type 1 (pigmentation type) and type 2 (immune response type) melanocytic lesions. A number of genes such as Checkpoint Kinase 1 (CHEK1) showed an association of mutational patterns and occurrence of splice variants in melanoma. Moreover, mutations in genes of the splicing machinery were common in both benign nevi and melanomas, suggesting a common mechanism starting early in melanoma development. Mutations in some of these genes of the splicing machinery, such as Serine and Arginine Rich Splicing Factor A3 and B3 (SF3A3, SF3B3), were significantly enriched in melanomas as compared to benign nevi. Taken together, a map of splice variants in melanoma is presented that shows a multitude of differentially expressed splice genes between benign nevi and primary melanomas. The underlying mechanisms may involve mutations in genes of the splicing machinery.

Prognostic variables in patients with thick melanomas. Analysis of 362 cases.

BACKGROUND: Melanoma epidemiological and prognostic studies are based on Caucasian populations, in whom the predominant subtype is superficially-spreading melanoma and in whom thin melanomas (Breslow < 3 mm) predominate. Mexican patients show a predominance of thick melanomas (Breslow ≥ 3 mm), and the acral subtype is the most common. There are no publications on prognostic factors in thick melanomas. We hypothesize that we will identify factors that determine the prognosis in this group of patients. OBJECTIVE: To identify clinical-pathological factors associated with the prognosis of patients with thick melanomas in the Mexican population. MATERIAL AND METHODS: Data on melanomas with Breslow > 3 mm were collected from 2010 to 2015. The prognostic influence of various clinical-pathological factors was analyzed. RESULTS: The most common subtypes were acral melanoma in 271 patients (74.9 %) and nodular melanoma in 49 (13.5 %). Median Breslow thickness was 7 mm. 56.6 % of the patients had lymph node metastases (clinical stage [CS] III), 269 (74.3 %) had ulceration, and surgical margins were positive in 15 (4.1 %). Elevated neutrophil: lymphocyte ratio (≥ 2) was found in 188 (51.9 %). The variables associated with lower overall survival were CS (p < 0.001), Breslow thickness (p = 0.044), ulceration (p = 0.004), mitotic activity (p < 0.001), < 2-cm margin (p < 0.001) and an increased neutrophil: lymphocyte ratio (p = 0.037). In the multivariate analysis, the factors associated with overall survival were CS, mitotic activity, and surgical margin. CONCLUSIONS: In patients with thick melanomas, overall survival is influenced by mitotic activity, a positive margin, and clinical stage.

ANTECEDENTES: Los estudios sobre factores pronóstico de melanoma están basados en poblaciones cau­cásicas, con predominio de melanomas delgados (Breslow < 3 mm). Los pacientes mexicanos muestran predominio de melanomas gruesos (Breslow ≥ 3 mm). OBJETIVO: Identificar factores asocia­dos al pronóstico de pacientes con melanomas gruesos. MATERIAL Y MÉTODOS: Se analizó la influencia pronóstica de factores clinico­patológicos en 362 melanomas gruesos. RESULTADOS: La mediana de Breslow fue de 7 mm, 271 (74.9 %) pacientes tuvieron melanoma acral y 49 (13.5 %) melanoma nodular. El 56.6 % de los pacientes se encontró en etapa clínica [EC] III), 269 (74.3 %) tenía ulceración y 15 (4.1 %) márgenes positivos. Las variables asociadas con menor supervivencia global [SG] fueron la EC (p < 0.001), Breslow (p = 0.044), ulceración (p = 0.004), mitosis (p < 0.001) y margen < 2 cm (p < 0.001) . En el análisis multivariante los factores que influyen en SG fueron la EC, mitosis y el margen quirúrgico. CONCLUSIONES: En pacientes con melanomas gruesos la SG es influida por un margen positive, mitosis y EC.