Effect of patient-contextual skin images in human- and artificial intelligence-based diagnosis of melanoma: Results from the 2020 SIIM-ISIC melanoma classification challenge.

Background: While the high accuracy of reported AI tools for melanoma detection is promising, the lack of holistic consideration of the patient is often criticized. Along with medical history, a dermatologist would also consider intra-patient nevi patterns, such that nevi that are different from others on a given patient are treated with suspicion.

Objective: To evaluate whether patient-contextual lesion-images improves diagnostic accuracy for melanoma in a dermoscopic image-based AI competition and a human reader study.

The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection.

AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world.

Photodynamic Therapy for the Treatment of Basal Cell Carcinoma: A Comprehensive Review of Randomized Controlled Trials.

Introduction: Basal cell carcinoma (BCC) is the most common skin cancer worldwide and has been reported to have a rising incidence in the last years. Multiple therapeutic modalities are approved for the treatment of BCC, making it difficult for physicians to choose the most suitable option for every patient. Photodynamic therapy (PDT) using either 5-aminolevulinic acid (ALA) or methyl aminolevulinate (MAL) as photosensitizing agents is an established treatment option for low-risk BCC.

International Skin Imaging Collaboration-Designated Diagnoses (ISIC-DX): Consensus terminology for lesion diagnostic labeling.

Background: A common terminology for diagnosis is critically important for clinical communication, education, research and artificial intelligence. Prevailing lexicons are limited in fully representing skin neoplasms.

Objectives: To achieve expert consensus on diagnostic terms for skin neoplasms and their hierarchical mapping.

Use of Dermoscopy among Greek Dermatologists in Everyday Clinical Practice: A National Questionnaire-Based Study.

: Dermatoscopy has been established as an important diagnostic tool for a wide range of skin diseases. This study aims to evaluate the use of dermatoscopy in clinical practice among Greek dermatologists. : A nationwide questionnaire-based survey was conducted collecting data on the frequency of dermatoscopic examinations, the types of lesions examined, training and educational resources, as well as factors influencing the choice to incorporate dermatoscopy into daily clinical routines.

Perifollicular linear projections: A dermatoscopic criterion for the diagnosis of lentigo maligna on the face.

Background: Lentigo maligna (LM) can mimic benign, flat, pigmented lesions and can be challenging to diagnose.

Objective: To describe a new dermatoscopic feature termed "perifollicular linear projections (PLP)" as a diagnostic criterion for LM on the face.

Methods: Retrospective study on reflectance confocal microscopy and dermatoscopy images of flat facial pigmented lesions originating from 2 databases.

The Natural Evolution of Nevi with Peripheral Globules.

Background: Peripheral globules (PG) in melanocytic lesions represent a concerning dermoscopic feature since they might be present in growing nevi and melanomas. Their natural evolution has not been fully elucidated, and an age-based management approach has been recommended.

Objectives: The aim of this study was to calculate the growth rate of lesions with PG and investigate possible association with age, sex, location, and the global dermoscopic pattern.

Clinical and Dermoscopic Characteristics of Cutaneous Sarcomas: A Literature Review.

Under the umbrella of cutaneous sarcomas (CS) we include a heterogeneous group of rare, malignant, mesenchymal neoplasia, such as dermatofibrosarcoma protuberans, atypical fibroxanthoma, cutaneous undifferentiated pleomorphic sarcoma, cutaneous angiosarcoma and leiomyosarcoma. Clinical presentation and histopathological examination are the cornerstone of CS diagnosis and classification. There are scarce data in the literature in regards to the clinical and dermatoscopic characteristics of CS and the role of dermatoscopy in their early identification.

Eccrine Porocarcinoma: A Review of the Literature.

Eccrine porocarcinoma (EPC) constitutes a rare malignant adnexal tumor, which accounts for about 0.005-0.01% of all cutaneous malignancies.

Dermoscopic Clues of Histopathologically Aggressive Basal Cell Carcinoma Subtypes.

The group of histopathologically aggressive BCC subtypes includes morpheaform, micronodular, infiltrative and metatypical BCC. Since these tumors are at increased risk of recurring, micrographically controlled surgery is considered the best therapeutic option. Although dermoscopy significantly improves the clinical recognition of BCC, scarce evidence exists on their dermoscopic criteria.

Paradoxical effect of epinephrine on lesion redness and vascularity.

Introduction: Epinephrine is commonly used in combination with local anesthetic (lidocaine/epinephrine) due to its beneficial vasoconstrictive properties. Typically, pallor is appreciated after injection as a sign of effect; however, we observed that some cutaneous malignancies paradoxically revealed increased redness and vascularity after injection of lidocaine/epinephrine. In this study, we investigate this phenomenon among a series of biopsied lesions to identify characteristics of lesions associated with increased redness and/or vascularity.

Dermoscopy of Actinic Keratosis: Is There a True Differentiation between Non-Pigmented and Pigmented Lesions?

Dermoscopic features of actinic keratosis (AK) have been widely studied, but there is still little evidence for their diagnostic accuracy. Our study investigates whether established dermoscopic criteria are reliable predictors in differentiating non-pigmented actinic keratosis (NPAK) from pigmented actinic keratosis (PAK). For this purpose, dermoscopic images of 83 clinically diagnosed AK (45 NPAK, 38PAK) were examined, and the sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) were assessed.

Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study.

Background: Dermoscopy is commonly used for the evaluation of pigmented lesions, but agreement between experts for identification of dermoscopic structures is known to be relatively poor. Expert labeling of medical data is a bottleneck in the development of machine learning (ML) tools, and crowdsourcing has been demonstrated as a cost- and time-efficient method for the annotation of medical images.

Objective: The aim of this study is to demonstrate that crowdsourcing can be used to label basic dermoscopic structures from images of pigmented lesions with similar reliability to a group of experts.

Dermoscopy of Linear Basal Cell Carcinomas, a Potential Mimicker of Linear Lesions: a Descriptive Case-series.

Introduction: Among the various widely recognized basal cell carcinoma (BCC) clinical patterns, linear basal cell carcinoma (LBCC) is an uncommon morphologic variant of BCC.

Objectives: Describe the clinical and dermoscopic characteristics of LBCC.

Methods: Retrospective study including LBCC cases from 5 dermatology centers in North and South America.

Artificial Intelligence in Dermatology: Challenges and Perspectives.

Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians' diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low.

The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search.

Model Dermatology ( https://modelderm.com ; Build2021) is a publicly testable neural network that can classify 184 skin disorders. We aimed to investigate whether our algorithm can classify clinical images of an Internet community along with tertiary care center datasets.

Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge.

Background: Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.

Methods: We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts.

Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group.

Importance: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety.

Objective: To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI.

Clinical and dermatoscopic predictors of squamous cell carcinoma of the lips: a case-control, multicentric study.

Background: Squamous cell carcinoma of the lip accounts for 20% of all oral carcinomas. Its diagnosis may be challenging because it clinically resembles actinic cheilitis and inflammatory lesions of the lips.

Objectives: To determine clinical and dermatoscopic predictors of squamous cell carcinoma of the lip vs.