Dermoscopy: A Review of the Structures That Facilitate Melanoma Detection

Preoperative bimodal imaging evaluation in finding histological correlations of in situ, superficial spreading and nodular melanoma

Background

The aim of this study is to correlate the diagnostic criteria described in dermoscopy, ultrasonography (US), and histology of the most common types of cutaneous melanoma (CM).

Methods

We conducted a prospective study including 40 CM cases, which were analyzed by dermoscopy using the Delta 30 dermatoscope and Vidix 4.0 videodermoscope, by ultrasound (US) using a high-resolution 20 MHz linear probe, along with histopathological analysis.

Results

The study involved 40 patients with histopathologically confirmed CM, comprising 10 nodular melanomas (NM), 21 superficial spreading melanomas (SSM), and nine in situ melanomas (MIS). US measurements of tumor thickness exhibited strong correlations with the histopathological Breslow index (BI), particularly in the NM and SSM groups. A notable correlation was observed between the presence of ulceration in histopathology and ultrasonography. Dermoscopic analysis revealed significant associations between specific features and CM types. For instance, the presence of an atypical network, irregular globules, irregular dots, prominent skin margins, angulated lines/polygons, dotted and short linear vessels, and negative network correlated with a median BI ≤ 0.5 mm. Conversely, the presence of blue-white veil, atypical vessels, blue-black color, and milky red color were associated with a median BI ≥ 2.3 mm. Furthermore, regression observed in histopathology correlated with regression identified in dermoscopy, we also found statistical correlations between the presence of vascularization at US with the high Clark level, and the presence of prominent skin markings at dermoscopy. The presence of histopathological regression was more frequently associated with tumors that had precise margins, absent vascularization and with those that did not have ulceration on US. The high mitotic rate was associated with tumors that presented imprecise margins, increased vascularization and US detectable ulceration.

Conclusion

Innovative CM diagnosis using non-invasive methods like dermoscopy and ultrasound may enhance accuracy and treatment guidance by assessing lesion characteristics.

From Lipid Signatures to Cellular Responses: Unraveling the Complexity of Melanoma and Furthering Its Diagnosis and Treatment

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, this review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

The Status of Dermoscopy in Chile: First National Study in Dermatologists

INTRODUCTION

Scientific evidence supports dermoscopy as an essential tool in dermatological diagnosis.

OBJECTIVES

The objective is to know the factors that influence its use in Chilean dermatologists.

METHODS

Analytical cross-sectional study. An adapted version of the survey was submitted from the pan-European study by Forsea et al to members of the Chilean Society of Dermatology, between September and December 2020. Analysis using descriptive statistics and multivariate analysis with ordinal logistic regression looking for factors associated with greater use of.

RESULTS

One hundred and ninety-eight responses, mean age 46.3 years and 14.6 years on average practicing as dermatologists. 61.6% trained in dermoscopy during their residency. 98% use a dermatoscope. More than 80% consider dermoscopy useful for the diagnosis of melanomas, follow-up of melanocytic lesions, and diagnosis of pigmented and non-pigmented tumors. Between 50% and 70% consider it useful for monitoring non-melanocytic lesions, nail and hair pathologies. Greater confidence when evaluating pigmented and non-pigmented tumors and capillary pathology. Adjusting for age, sex, confidence, and education, participation in teaching was associated with greater use of dermoscopy in non-pigmented and pigmented tumors, and capillary pathology.

CONCLUSIONS

Percentage of participation in the survey and training in dermoscopy higher than in the reference study, recognizing the usefulness of dermoscopy for the diagnosis and follow-up of tumor pathologies. Participating in teaching is a strong independent factor that is associated with a greater use of dermoscopy in Chile. Dermoscopy is positioned as a tool widely used by Chilean dermatologists in their daily practice.

Artificial Intelligence in Dermoscopy: Enhancing Diagnosis to Distinguish Benign and Malignant Skin Lesions

This study presents an innovative application of artificial intelligence (AI) in distinguishing dermoscopy images depicting individuals with benign and malignant skin lesions. Leveraging the collaborative capabilities of Google's platform, the developed model exhibits remarkable efficiency in achieving accurate diagnoses. The model underwent training for a mere one hour and 33 minutes, utilizing Google's servers to render the process both cost-free and carbon-neutral. Utilizing a dataset representative of both benign and malignant cases, the AI model demonstrated commendable performance metrics. Notably, the model achieved an overall accuracy, precision, recall (sensitivity), specificity, and F1 score of 92%. These metrics underscore the model's proficiency in distinguishing between benign and malignant skin lesions. The use of Google's Collaboration platform not only expedited the training process but also exemplified a cost-effective and environmentally sustainable approach. While these findings highlight the potential of AI in dermatopathology, it is crucial to recognize the inherent limitations, including dataset representativity and variations in real-world clinical scenarios. This study contributes to the evolving landscape of AI applications in dermatologic diagnostics, showcasing a promising tool for accurate lesion classification. Further research and validation studies are recommended to enhance the model's robustness and facilitate its integration into clinical practice.

DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework

Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel 'F' Flag feature for early detection. This novel 'F' indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma. The article proposes an architecture that is built in a Double Decker Convolutional Neural Network called DDCNN future fusion. The network's deck one, known as a Convolutional Neural Network (CNN), finds difficult-to-classify hairy images using a confidence factor termed the intra-class variance score. These hirsute image samples are combined to form a Baseline Separated Channel (BSC). By eliminating hair and using data augmentation techniques, the BSC is ready for analysis. The network's second deck trains the pre-processed BSC and generates bottleneck features. The bottleneck features are merged with features generated from the ABCDE clinical bio indicators to promote classification accuracy. Different types of classifiers are fed to the resulting hybrid fused features with the novel 'F' Flag feature. The proposed system was trained using the ISIC 2019 and ISIC 2020 datasets to assess its performance. The empirical findings expose that the DDCNN feature fusion strategy for exposing malignant melanoma achieved a specificity of 98.4%, accuracy of 93.75%, precision of 98.56%, and Area Under Curve (AUC) value of 0.98. This study proposes a novel approach that can accurately identify and diagnose fatal skin cancer and outperform other state-of-the-art techniques, which is attributed to the DDCNN 'F' Feature fusion framework. Also, this research ascertained improvements in several classifiers when utilising the 'F' indicator, resulting in the highest specificity of + 7.34%.

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians

The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers. By altering image attributes to produce analogous images that elicit a different prediction by the classifiers, and by asking physicians to identify medically meaningful features in the images, the counterfactual images revealed that the classifiers rely both on features used by human dermatologists, such as lesional pigmentation patterns, and on undesirable features, such as background skin texture and colour balance. The framework can be applied to any specialized medical domain to make the powerful inference processes of machine-learning models medically understandable.

Accuracy of a Smartphone-Based Artificial Intelligence Application for Classification of Melanomas, Melanocytic Nevi, and Seborrheic Keratoses

Current artificial intelligence algorithms can classify melanomas at a level equivalent to that of experienced dermatologists. The objective of this study was to assess the accuracy of a smartphone-based "You Only Look Once" neural network model for the classification of melanomas, melanocytic nevi, and seborrheic keratoses. The algorithm was trained using 59,090 dermatoscopic images. Testing was performed on histologically confirmed lesions: 32 melanomas, 35 melanocytic nevi, and 33 seborrheic keratoses. The results of the algorithm's decisions were compared with those of two skilled dermatologists and five beginners in dermatoscopy. The algorithm's sensitivity and specificity for melanomas were 0.88 (0.71-0.96) and 0.87 (0.76-0.94), respectively. The algorithm surpassed the beginner dermatologists, who achieved a sensitivity of 0.83 (0.77-0.87). For melanocytic nevi, the algorithm outclassed each group of dermatologists, attaining a sensitivity of 0.77 (0.60-0.90). The algorithm's sensitivity for seborrheic keratoses was 0.52 (0.34-0.69). The smartphone-based "You Only Look Once" neural network model achieved a high sensitivity and specificity in the classification of melanomas and melanocytic nevi with an accuracy similar to that of skilled dermatologists. However, a bigger dataset is required in order to increase the algorithm's sensitivity for seborrheic keratoses.

Dissection of medical AI reasoning processes via physician and generative-AI collaboration

Despite the proliferation and clinical deployment of artificial intelligence (AI)-based medical software devices, most remain black boxes that are uninterpretable to key stakeholders including patients, physicians, and even the developers of the devices. Here, we present a general model auditing framework that combines insights from medical experts with a highly expressive form of explainable AI that leverages generative models, to understand the reasoning processes of AI devices. We then apply this framework to generate the first thorough, medically interpretable picture of the reasoning processes of machine-learning-based medical image AI. In our synergistic framework, a generative model first renders "counterfactual" medical images, which in essence visually represent the reasoning process of a medical AI device, and then physicians translate these counterfactual images to medically meaningful features. As our use case, we audit five high-profile AI devices in dermatology, an area of particular interest since dermatology AI devices are beginning to achieve deployment globally. We reveal how dermatology AI devices rely both on features used by human dermatologists, such as lesional pigmentation patterns, as well as multiple, previously unreported, potentially undesirable features, such as background skin texture and image color balance. Our study also sets a precedent for the rigorous application of explainable AI to understand AI in any specialized domain and provides a means for practitioners, clinicians, and regulators to uncloak AI's powerful but previously enigmatic reasoning processes in a medically understandable way.

Evaluation of dynamic dermoscopic features of melanoma and benign naevi by sequential digital dermoscopic imaging and total body photography in a high-risk Australian cohort

BACKGROUND/OBJECTIVES

Sequential digital dermoscopic imaging (SDDI) and total body photography (TBP) are recommended as a two-step surveillance method for individuals at high-risk of developing cutaneous melanoma. Dermoscopic features specific to melanoma have been well described, however, dynamic changes on serial imaging are less understood. This study aims to identify and compare dermoscopic features in developing melanomas and benign naevi that underwent SDDI and TBP to understand which dermoscopic features may be associated with a malignant change.

METHOD

Histopathology reports from a private specialist dermatology clinic from January 2007 to December 2019 were reviewed. Histopathologically confirmed melanoma and benign naevi that underwent SDDI and TBP with a minimum follow-up interval of 3 months were included.

RESULTS

Eighty-nine melanomas (38.2% invasive, median Breslow thickness 0.35 mm, range: 0.2-1.45 mm) and 48 benign naevi were evaluated by three experienced dermatologists for dermoscopic changes. Features most strongly associated with melanoma included the development of neovascularisation, asymmetry and growth in pigment network, additional colours, shiny white structures, regression, structureless areas and change to a multi-component pattern. The presence of atypical vessels (p = 0.02) and shiny white structures (p = 0.02) were significantly associated with invasive melanoma.

CONCLUSION

Evaluation for certain evolving dermoscopic features in melanocytic lesions monitored by SDDI and TBP is efficient in assisting clinical decision making. SDDI with TBP is an effective tool for early detection of melanoma.

Racial Differences in Perceived Risk and Sunscreen Usage

Background Although White individuals have higher incidence of melanoma, clinical outcomes are worse among patients with skin of color. This disparity arises from delayed diagnoses and treatment that are largely due to clinical and sociodemographic factors. Investigating this discrepancy is crucial to decrease melanoma-related mortality rates in minority communities. A survey was used to investigate the presence of racial disparities in perceived sun exposure risks and behaviors. Methods A survey consisting of 16 questions was deployed via social media to assess skin health knowledge. Over 350 responses were recorded, and the extracted data were analyzed using statistical software. Results Of the respondents, White patients were significantly more likely to have higher perceived risk of developing skin cancer, highest levels of sunscreen usage, and higher reported frequency of skin checks performed by primary care providers (PCPs). There was no difference between racial groups in the amount of education provided by PCPs related to sun exposure risks. Conclusion The survey findings suggest inadequate dermatologic health literacy as a result of other factors such as public health and sunscreen product marketing rather than as a consequence of inadequate dermatologic education provided in healthcare settings. Factors such as racial stereotypes in communities, implicit biases in marketing companies, and public health campaigns should be considered. Further studies should be conducted to determine these biases and improve education in communities of color.

A Survey on Computer-Aided Intelligent Methods to Identify and Classify Skin Cancer

Melanoma is one of the skin cancer types that is more dangerous to human society. It easily spreads to other parts of the human body. An early diagnosis is necessary for a higher survival rate. Computer-aided diagnosis (CAD) is suitable for providing precise findings before the critical stage. The computer-aided diagnostic process includes preprocessing, segmentation, feature extraction, and classification. This study discusses the advantages and disadvantages of various computer-aided algorithms. It also discusses the current approaches, problems, and various types of datasets for skin images. Information about possible future works is also highlighted in this paper. The inferences derived from this survey will be useful for researchers carrying out research in skin cancer image analysis.

Mueller Matrix Microscopy for In Vivo Scar Tissue Diagnostics and Treatment Evaluation

Scars usually do not show strong contrast under standard skin examination relying on dermoscopes. They usually develop after skin injury when the body repairs the damaged tissue. In general, scars cause multiple types of distress such as movement restrictions, pain, itchiness and the psychological impact of the associated cosmetic disfigurement with no universally successful treatment option available at the moment. Scar treatment has significant economic impact as well. Mueller matrix polarimetry with integrated autofocus and automatic data registration can potentially improve scar assessment by the dermatologist and help to make the evaluation of the treatment outcome objective. Polarimetry can provide new physical parameters for an objective treatment evaluation. We show that Mueller matrix polarimetry can enable strong contrast for in vivo scar imaging. Additionally, our results indicate that the polarization stain images obtained form there could be a useful tool for dermatology. Furthermore, we demonstrate that polarimetry can be used to monitor wound healing, which may help prevent scarring altogether.

SIL-Net: A Semi-Isotropic L-shaped network for dermoscopic image segmentation

BACKGROUND

Dermoscopic image segmentation using deep learning algorithms is a critical technology for skin cancer detection and therapy. Specifically, this technology is a spatially equivariant task and relies heavily on Convolutional Neural Networks (CNNs), which lost more effective features during cascading down-sampling or up-sampling. Recently, vision isotropic architecture has emerged to eliminate cascade procedures in CNNs as well as demonstrates superior performance. Nevertheless, it cannot be used for the segmentation task directly. Based on these discoveries, this research intends to explore an efficient architecture which not only preserves the advantages of the isotropic architecture but is also suitable for clinical dermoscopic diagnosis.

METHODS

In this work, we introduce a novel Semi-Isotropic L-shaped network (SIL-Net) for dermoscopic image segmentation. First, we propose a Patch Embedding Weak Correlation (PEWC) module to address the issue of no interaction between adjacent patches during the standard Patch Embedding process. Second, a plug-and-play and zero-parameter Residual Spatial Mirror Information (RSMI) path is proposed to supplement effective features during up-sampling and optimize the lesion boundaries. Third, to further reconstruct deep features and get refined lesion regions, a Depth Separable Transpose Convolution (DSTC) based up-sampling module is designed.

RESULTS

The proposed architecture obtains state-of-the-art performance on dermoscopy benchmark datasets ISIC-2017, ISIC-2018 and PH2. Respectively, the Dice coefficient (DICE) of above datasets achieves 89.63%, 93.47%, and 95.11%, where the Mean Intersection over Union (MIoU) are 82.02%, 88.21%, and 90.81%. Furthermore, the robustness and generalizability of our method has been demonstrated through additional experiments on standard intestinal polyp datasets (CVC-ClinicDB and Kvasir-SEG).

CONCLUSION

Our findings demonstrate that SIL-Net not only has great potential for precise segmentation of the lesion region but also exhibits stronger generalizability and robustness, indicating that it meets the requirements for clinical diagnosis. Notably, our method shows state-of-the-art performance on all five datasets, which highlights the effectiveness of the semi-isotropic design mechanism.

Diagnostic accuracy of dermoscopy for onychomycosis: A systematic review

Background

Dermoscopy is a non-invasive adjuvant diagnostic tool that allows clinicians to visualize microscopic features of cutaneous disorders. Recent studies have demonstrated that dermoscopy can be used to diagnose onychomycosis. We performed this systematic review to identify the characteristic dermoscopic features of onychomycosis and understand their diagnostic utility.

Methods

We searched the Medline, Embase, Scopus, and Cochrane databases from conception until May 2021. Studies on the dermoscopic features of onychomycosis were screened. The exclusion criteria were as follows: fewer than 5 cases of onychomycosis, review articles, and studies including onychomycosis cases that were not mycologically verified. Studies on fungal melanonychia were analyzed separately. We adhered to the MOOSE guidelines. Independent data extraction was performed. Data were pooled using a random effects model to account for study heterogeneity. The primary outcome was the diagnostic accuracy of the dermoscopic features of onychomycosis. This was determined by pooling the sensitivity and specificity values of the dermoscopic features identified during the systematic review using the DerSimonian-Laird method. Meta-DiSc version 1.4 and Review Manager 5.4.1 were used to calculate these values.

Results

We analyzed 19 articles on 1693 cases of onychomycosis and 5 articles on 148 cases of fungal melanonychia. Commonly reported dermoscopic features of onychomycosis were spikes or spiked pattern (509, 30.1%), jagged or spiked edges or jagged edge with spikes (188, 11.1%), jagged proximal edge (175, 10.3%), subungual hyperkeratosis (131, 7.7%), ruins appearance, aspect or pattern (573, 33.8%), and longitudinal striae (929, 54.9%). Commonly reported features of fungal melanonychia included multicolor (101, 68.2%), non-longitudinal homogenous pigmentation (75, 50.7%) and longitudinal white or yellow streaks (52, 31.5%).

Conclusion

This study highlights the commonly identified dermoscopic features of onychomycosis. Recognizing such characteristic dermoscopic features of onychomycosis can assist clinicians diagnose onychomycosis by the bedside.

Registration of polarimetric images for in vivo skin diagnostics

SIGNIFICANCE

Mueller matrix (MM) polarimetry is a promising tool for the detection of skin cancer. Polarimetric in vivo measurements often suffer from misalignment of the polarimetric images due to motion, which can lead to false results.

AIM

We aim to provide an easy-to-implement polarimetric image data registration method to ensure proper image alignment.

APPROACH

A feature-based image registration is implemented for an MM polarimeter for phantom and in vivo human skin measurements.

RESULTS

We show that the keypoint-based registration of polarimetric images is necessary for in vivo skin polarimetry to ensure reliable results. Further, we deliver an efficient semiautomated method for the registration of polarimetric images.

CONCLUSIONS

Image registration for in vivo polarimetry of human skin is required for improved diagnostics and can be efficiently enhanced with a keypoint-based approach.

Fractal Dimension Analysis of Melanocytic Nevi and Melanomas in Normal and Polarized Light-A Preliminary Report

Clinical diagnosis of pigmented lesions can be a challenge in everyday practice. Benign and dysplastic nevi and melanomas may have similar clinical presentations, but completely different prognoses. Fractal dimensions of shape and texture can describe the complexity of the pigmented lesion structure. This study aims to apply fractal dimension analysis to differentiate melanomas, dysplastic nevi, and benign nevi in polarized and non-polarized light. A total of 87 Eighty-four patients with 97 lesions were included in this study. All examined lesions were photographed under polarized and non-polarized light, surgically removed, and examined by a histopathologist to establish the correct diagnosis. The obtained images were then processed and analyzed. Area, perimeter, and fractal dimensions of shape and texture were calculated for all the lesions under polarized and non-polarized light. The fractal dimension of shape in polarized light enables differentiating melanomas, dysplastic nevi, and benign nevi. It also makes it possible to distinguish melanomas from benign and dysplastic nevi under non-polarized light. The fractal dimension of texture allows distinguishing melanomas from benign and dysplastic nevi under polarized light. All examined parameters of shape and texture can be used for developing an automatic computer-aided diagnosis system. Polarized light is superior to non-polarized light for imaging texture details.

Lesion- and Patient-Related Variables May Provide Additional Clues during Dermoscopic Assessment of Blue Nevi—A Retrospective Cohort Study

Simple Summary

Blue nevi (BN) are dermal dendritic melanocytic proliferations which may be congenital or acquired. Due to wide clinical and dermoscopic presentation, their diagnosis may sometimes be difficult, especially if the history of lesion occurrence is unknown. Little is known about the correlation between lesion- and patient-related variables and dermoscopic features of blue nevi. The aim of the study was to analyze dermoscopic features of blue nevi, with particular regard to structures whose prevalence has not been previously reported, and to investigate the possible influence of selected clinical variables on dermoscopic presentation. Our findings provide new insights into the dermoscopic structures observed in blue nevi and their variability according to patient’s phototype and lesion size/localization.

Abstract

Background: Little is known about the correlation between lesion- and patient-related variables and the dermoscopic features of blue nevi. The aim of the study was dermoscopic analysis of blue nevi in association with patient- and lesion-related variables, with a special interest in structures whose prevalence has not been previously reported. Methods: This was a double-center, retrospective study, which included the analysis of histopathologically confirmed blue nevi (n = 93). Results: There was no difference in the frequency of the observed dermoscopic features according to patients’ gender and age. Pink structureless areas were more common in patients with I/II Fitzpatrick skin phototypes as well as in the patients with photodamaged skin, while blue prominent skin markings over brownish/blue-gray background occurred exclusively in patients with phototype III. Structures of previously unreported prevalence in blue nevi were skin-colored circles (present in 32.3%), gray circles (2.2%), follicular ostia with no pigmentation (18.4%; present exclusively on the face), blue skin markings over brownish background (present in 18.2%; detected only on the limbs) and dark brown polygons (one lesion located on the lower extremity). Conclusion: Dermoscopic presentation of blue nevi may vary according to the patient’s phototype and lesion size/localization rather than gender and age.

Circulating microRNA biomarkers in melanoma and non-melanoma skin cancer

INTRODUCTION

Skin cancer is the most common type of cancer and is classified in melanoma and non-melanoma cancers, which include basal cell, squamous cell and Merkel cell carcinoma. Specific microRNAs are dysregulated in each skin cancer type. MicroRNAs act as oncogene or tumor suppressor gene regulators and are actively released from tumor cells in the circulation. Cell-free microRNAs serve many, and possibly yet unexplored, functional roles, but their presence and abundance in the blood has been investigated as disease biomarker. Indeed, specific microRNAs can be isolated and quantified in the blood, usually in serum or plasma fractions, where they are uncommonly stable. MicroRNA levels reflect underlying conditions and have been associated with skin cancer presence, stage, evolution, or therapy efficacy.

AREAS COVERED

In this review, we summarize the state of the art on circulating microRNAs detectable in skin cancer patients including all the studies that performed microRNA identification and quantification in the circulation using appropriate sample size and statistics and providing detailed methodology, with a specific focus on diagnostic and prognostic biomarkers.

EXPERT OPINION

Circulating microRNAs display a relevant biomarker potential. We expect the development of methodological guidelines and standardized protocols for circulating miRNA quantification in clinical settings.

Sub-macroscopic skin presentation of acromegaly and effect of pituitary tumor surgery: A study using dermatoscopy and ultra-high-frequency ultrasound

OBJECTIVE

Excessive growth hormone and insulin-like growth factor 1 contribute to cutaneous changes in acromegaly. We investigated the sub-macroscopic skin manifestation of acromegaly patients and explored its reversibility upon hormone reduction after pituitary adenoma surgery.

DESIGN

Prospectively cohort study.

METHODS

We enrolled 26 patients with acromegaly and 26 patients with non-functioning pituitary adenomas undergoing pituitary adenomectomy at Peking Union Medical College Hospital from July 2021 to March 2022. Skin presentations were evaluated by dermatoscopy and ultra-high-frequency ultrasound before and after surgery.

RESULTS

Skin thickening, follicular plugs, perifollicular pigmentations, perifollicular orange haloes, red structureless areas, increased hair shafts, honeycomb-like pigmentations, widened dermatoglyphics, dilated appendage openings, excessive seborrhea, hyperhidrosis, enlarged pores, and acne-like lesions were commonly occurring in acromegaly patients, and their incidences were higher than the controls (P<0.05). At 3-month follow-up after surgery, the thickness of skin reduced (4.0 ± 0.4 to 3.7 ± 0.4, P=0.007), the incidences of hyperhidrosis (92.3% to 69.2%, P=0.035) and acne-like lesions (53.8% to 26.9%, P=0.048) declined, and the severity of multiple cutaneous lesions improved. Patients with surgical endocrine remission (53.8%) had greater declines in the thickness of skin than those without remission. Patients with improvement of >1 skin lesions were younger (P=0.028) and had higher baseline GH levels (P=0.021) than those with improvement of ≤1 skin lesion.

CONCLUSIONS

Dermatoscopy and ultra-high-frequency ultrasound provided augmented visual examination of the cutaneous changes in acromegaly. Some of the skin lesions could improve or reverse after pituitary surgery. Baseline GH levels, age, and endocrine remission were correlated with skin improvement at 3-month follow-up.

Dermoscopy of Small Diameter Melanomas with the Diagnostic Feasibility of Selected Algorithms-A Clinical Retrospective Multicenter Study

Simple Summary

The early detection of melanoma determines the recovery of the patient. Dermoscopy, which is one of the diagnostic tools for pigmented lesions, is characterized by high sensitivity and specificity, giving the clinician the possibility to detect the presence of abnormal structures before their clinical presentation. There are a small number of dermoscopic analyses of pigmented lesions of less than 6 mm in diameter in the published literature. The authors attempted to identify characteristic dermoscopic structures typical for melanomas of less than 5 mm in diameter in comparison with a group of melanomas exceeding this dimension at an identical clinical stage. It was found that dermoscopy in the secondary prevention of micromelanomas (appearing mainly as brown lesions) revealed the presence of dotted or polymorphous vessels, with architectural disorder in half of cases. Moreover, spitzoid, multicomponent asymmetric or nonmelanoma-specific patterns prevailed. Knowledge of these dermoscopic features brings the clinician closer to an early diagnosis of melanoma with a diameter of 5 mm or less.

Abstract

Objective: The aim of the study was to verify two hypotheses. The first concerned the possibility of diagnostic dermoscopic differentiation between cutaneous melanomas of the histopathological category in situ (pTis) and thin melanomas (pT1a) in terms of their diameter. The second assessed the diagnostic feasibility of two dermoscopic algorithms aiming to detect ≤ 5.0 mm-sized melanomas histopathologically confirmed as pTis and pT1a. Methods: Dermoscopic images of consecutive cases of histopathologically confirmed melanomas were evaluated by three independent investigators for the presence of the predefined criteria. The melanomas were subdivided according to their diameter into small melanomas, so-called micromelanomas (microM)—sized ≤ 5.0 mm and >5.0 mm, according to published definitions of small melanocytic lesions. The Triage Amalgamated Dermoscopic Algorithm (TADA) and the revisited 7-point checklist of dermoscopy (7-point) algorithm were chosen for the diagnostic feasibility. Odds ratios and corresponding 95% confidence limits (CL) were calculated using the logistic regression adjusted for age for the melanoma-specific dermoscopic structures, the dermoscopic patterns and the diagnostic feasibility of the 7-point checklist and TADA algorithms. The p-values of the results were corrected using the Bonferroni method. Results: In total, 106 patients with 109 melanomas, 50 sized ≤ 5.0 mm and 59 exceeding the diameter of 5.0 mm, were retrospectively analyzed. The prevalent general pattern of microM was the spitzoid one (48% vs. 11.86%, p = 0.0013). Furthermore, 40% of microM vs. 6.78% melanomas sized > 5.0 mm (p = 0.0023) did not present melanoma-specific patterns. The asymmetric multicomponent pattern was present in 64.41% melanomas sized > 5.0 mm and in 26.00% microM (p = 0.0034). The asymmetry of structures or colors was detected in 56% microM vs. 89.83% (p = 0.0020) and 56% microM and 94.92% (p = 0.000034) melanoma sized > 5.0 mm, respectively. The differences in frequency of the detected dermoscopic structures specific to melanomas revealed that microM are almost deprived of negative networks (p = 0.04), shiny white structures (p = 0.0027) and regression features (p = 0.00003). Neither prominent skin markings nor angulated lines were found in the entire study group. Out of the vascular structures, microM presented only dotted (32%) or polymorphous (28%) vessels, although more rarely than melanomas sized > 5.0 mm (66.1% p = 0.017 and 49% p > 0.05, respectively). The diagnostic feasibility revealed a score ≥ 3 of the 7-point algorithm (indicative for malignancy) in 60% microM and 98.31% melanomas sized > 5.0 mm (p = 0.000006). The TADA algorithm revealed melanoma-specific patterns in 64% microM and 96.61% > 5.0 mm-sized melanomas (p = 0.00006) and melanoma-specific structures in 72% and 91.53% (p > 0.05), respectively. Conclusion: In the dermoscopy, 40% of micromelanomas histopathologically staged as pTis and pT1a did not reveal melanoma-specific patterns. Among the general melanocytic patterns, the spitzoid one was the most frequently found in melanomas sized ≤ 5.0 mm. The 7-point checklist and TADA dermoscopic algorithms were helpful in the identification of the majority of melanomas sized ≤ 5.0 mm.

Clinical-dermoscopic similarities between atypical nevi and early stage melanoma

Atypical (Clark) nevi are benign tumors that may be considered precursors of melanoma. Many studies acknowledge a linear progression from typical to atypical nevi that eventually transform into melanoma. It is often challenging to differentiate a Clark nevus from melanoma, especially in its early stages, due to their clinical, dermoscopic, and histological resemblance. Dermoscopy is a powerful tool in early melanoma diagnosis, but it is a subjective method of examination. Therefore, the use of dermoscopic algorithms and checklists can overcome this issue. In the case of a difficult diagnosis, since both dermoscopy and histopathological exam are subjective methods of examination, modern molecular biology techniques can be used to distinguish between benign and malignant tumors. This study aimed to test the accuracy of specific clinical and dermoscopic criteria in order to distinguish between benign and malignant tumors, with a secondary objective to provide an overview of the clinical and dermoscopic features of atypical nevi and melanoma. In the present study, dermoscopic algorithms did not necessarily help distinguish benign and malignant tumors but demonstrated that nevi and melanoma have similar characteristics.