Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis

Exploring the Surface: Sampling of Potential Skin Cancer Biomarkers Kynurenine and Tryptophan, Studied on 3D Melanocyte and Melanoma Models

Early detection of cancer via biomarkers is vital for improving patient survival rates. In the case of skin cancers, low-molecular-weight biomarkers can penetrate the skin barrier, enabling non-invasive sampling at an early stage. This study focuses on detecting tryptophan (Trp) and kynurenine (Kyn) on the surface of reconstructed 3D melanoma and melanocyte models. This is examined in connection with IDO-1 and IL-6 expression in response to IFN-γ or UVB stimulation, both crucial factors of the melanoma tumor microenvironment (TME). Using a polystyrene scaffold, full-thickness human skin equivalents containing fibroblasts, keratinocytes, and melanocytes or melanoma cells were developed. The samples were stimulated with IFN-γ or UVB, and Trp and Kyn secretion was measured using HPLC-PDA and HPLC-MS. The expression of IDO-1 and IL-6 was measured using RT-qPCR. Increased Trp catabolism to Kyn was observed in IFN-γ-stimulated melanoma and melanocyte models, along with higher IDO-1 expression. UVB exposure led to significant changes in Kyn levels but only in the melanoma model. This study demonstrates the potential of skin surface Trp and Kyn monitoring to capture TME metabolic changes. It also lays the groundwork for future in vivo studies, aiding in understanding and monitoring skin cancer progression.

ABCD Classification of Skin Types

The classification of an individual's skin type is important because it helps dermatologists and aesthetic clinicians select the most effective methods for treating the skin. To select suitable cosmetic products and procedures that help ensure healthy and beautiful skin, clinicians must consider the patient's skin type. An individualized classification of skin types allows the clinician to develop personalized skin care recommendations that consider an individual's genetic and physiological characteristics. In the fields of dermatology and aesthetics, the classification of skin types is the basis for scientific research. Skin type classification systems are additionally important for both dermatologists and aesthetic practitioners because they influence the selection of cosmetic products, procedures, and treatments. The purpose of this paper was to provide a detailed review and evaluation of the relevance, suitability, and benefits of the new ABCD Classification of Skin Types, to compare it with other systems for classifying skin types, and to identify parameters for its improvement.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

BACKGROUND

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.

OBJECTIVES

To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.

METHODS

In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.

RESULTS

We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

CONCLUSIONS

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

Photoacoustic imaging for cutaneous melanoma assessment: a comprehensive review

Significance

Cutaneous melanoma (CM) has a high morbidity and mortality rate, but it can be cured if the primary lesion is detected and treated at an early stage. Imaging techniques such as photoacoustic (PA) imaging (PAI) have been studied and implemented to aid in the detection and diagnosis of CM.

Aim

Provide an overview of different PAI systems and applications for the study of CM, including the determination of tumor depth/thickness, cancer-related angiogenesis, metastases to lymph nodes, circulating tumor cells (CTCs), virtual histology, and studies using exogenous contrast agents.

Approach

A systematic review and classification of different PAI configurations was conducted based on their specific applications for melanoma detection. This review encompasses animal and preclinical studies, offering insights into the future potential of PAI in melanoma diagnosis in the clinic.

Results

PAI holds great clinical potential as a noninvasive technique for melanoma detection and disease management. PA microscopy has predominantly been used to image and study angiogenesis surrounding tumors and provide information on tumor characteristics. Additionally, PA tomography, with its increased penetration depth, has demonstrated its ability to assess melanoma thickness. Both modalities have shown promise in detecting metastases to lymph nodes and CTCs, and an all-optical implementation has been developed to perform virtual histology analyses. Animal and human studies have successfully shown the capability of PAI to detect, visualize, classify, and stage CM.

Conclusions

PAI is a promising technique for assessing the status of the skin without a surgical procedure. The capability of the modality to image microvasculature, visualize tumor boundaries, detect metastases in lymph nodes, perform fast and label-free histology, and identify CTCs could aid in the early diagnosis and classification of CM, including determination of metastatic status. In addition, it could be useful for monitoring treatment efficacy noninvasively.

The role of in vivo reflectance confocal microscopy for the management of acne: A systematic review

Acne diagnosis, severity assessment and treatment follow-up rely primarily on clinical examination. In vivo reflectance confocal microscopy (RCM) provides non-invasively, real-time images of skin lesions with a level of detail close to histopathology. This systematic literature review aims to provide an overview of RCM utility in acne and a summary of specific features with clinical application that may increase objectivity in evaluating this condition. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting our results. We systematically searched three databases: PubMed, Clarivate and Google Scholar (January 2022). All included studies used RCM to investigate acne in human patients and reported the investigated skin area and type (acne lesions or clinically uninvolved skin), the substance used in the case of treatment. Our search identified 2184 records in the three databases investigated. After duplicate removal, 1608 records were screened, 35 were selected for full-text assessment, and 14 were included in this review. We used the QUADAS-2 tool to evaluate the risk of bias and applicability concerns. RCM was selected as the index test and clinical examination as the reference standard. The total number of patients from all studies was 291, with 216 acne patients and 60 healthy participants aged between 13 and 45 years. The 14 considered studies analysed 456 follicles from healthy participants, 1445 follicles from uninvolved skin in acne patients and 1472 acne lesions. Consistent RCM findings concerning follicles of acne patients reported across studies were increased follicular infundibulum size, thick, bright border, intrafollicular content and inflammation. Our analysis indicates that RCM is a promising tool for acne evaluation. Nevertheless, standardization, a unified terminology, consistent research methods and unitary reporting of RCM findings are necessary. PROSPERO registration number CRD42021266547.

Photodynamic Therapy is an Effective Treatment of Facial Pigmented Actinic Keratosis

BACKGROUND

Actinic keratosis (AK), or solar keratosis, is a precancerous condition of the skin, mainly caused by excessive and chronic exposure to ultraviolet radiation. Pigmented AK (pAK) is a rare variant of AK. Photodynamic therapy (PDT) is widely used to treat the classical variant of AK, but very limited data are available on the use of PDT in patients with pAK. The objective of this study was to assess the usefulness of PDT in the treatment of pAK.

METHODS

The study included 16 patients with 20 pAK lesions treated with PDT. All skin lesions were clinically and dermatoscopically assessed for typical features characteristic of pAK. Reflectance confocal microscopy (RCM) was also used to assess keratinocyte atypia, confirm pAK diagnosis, and rule out other disease entities.

RESULTS

After three PDT sessions, the complete resolution of all clinical features of pAK was observed in 80% of the studied lesions. Dermatoscopically, 65% of the lesions achieved 100% response and no cellular atypia was seen in the follow-up RCM images of 85% of lesions.

CONCLUSIONS

Photodynamic therapy is an effective treatment modality for pAK in fair-skinned individuals.

Clinical Utility of an AI-powered, Handheld Elastic Scattering Spectroscopy Device on the Diagnosis and Management of Skin Cancer by Primary Care Physicians

BACKGROUND

Patients with lesions suspicious for skin cancer often present to primary care physicians (PCPs), who may have limited training in skin cancer diagnosis.

OBJECTIVE

To measure the impact of an adjunctive handheld device for PCPs that employs elastic scattering spectroscopy (ESS) on the diagnosis and management of skin cancer.

METHODS

Fifty-seven PCPs evaluated 50 clinical images of skin lesions (25 malignant and 25 benign), first without and then with knowledge of the handheld ESS device output, and in each case indicated if a lesion was likely to be benign or malignant.

RESULTS

The diagnostic sensitivity of the PCPs with and without the use of the ESS device was 88% (95% CI, 84%-92%) and 67% (95% CI, 62%-72%), respectively (P < .0001). In contrast, no significant difference was observed in the diagnostic specificity. The management sensitivity of the physicians with and without the use of the ESS device was 94% (95% CI, 91%-96%) and 81% (95% CI, 77%-85%), respectively (P = .0009). Similarly, no significant difference was observed in the management specificity.

CONCLUSION

The use of the ESS device may have the potential to help improve skin cancer diagnosis and confidence in management decision-making in a primary care setting.

A Decision Support System for Melanoma Diagnosis from Dermoscopic Images

Innovative technologies in dermatology allow for the early screening of skin cancer, which results in a reduction in the mortality rate and surgical treatments. The diagnosis of melanoma is complex not only because of the number of different lesions but because of the high similarity amongst skin lesions of different nature; hence, human vision and physician experience still play a major role. The adoption of automatic systems would aid clinical assessment and make the diagnosis reproducible by eliminating inter- and intra-observer variabilities. In our paper, we describe a computer-aided system for the early diagnosis of melanoma in dermoscopic images. A soft pre-processing phase is performed so as to avoid the loss of details both in texture, colors, and contours, and color-based image segmentation is later carried out using k-means. Features linked to both geometric properties and color characteristics are used to analyze skin lesions through a support vector machine classifier. The PH2 public database is used for the assessment of the procedure’s sensitivity, specificity, and accuracy. A statistical approach is carried out to establish the impact of image quality on performance. The obtained results show remarkable achievements, so our computer-aided approach should be suitable as a Decision Support System for melanoma detection.

Characterizing Malignant Melanoma Clinically Resembling Seborrheic Keratosis Using Deep Knowledge Transfer

Simple Summary

Malignant melanomas (MMs) with aypical clinical presentation constitute a diagnostic pitfall, and false negatives carry the risk of a diagnostic delay and improper disease management. Among the most common, challenging presentation forms of MMs are those that clinically resemble seborrheic keratosis (SK). On the other hand, SK may mimic melanoma, producing ‘false positive overdiagnosis’ and leading to needless excisions. The evolving efficiency of deep learning algorithms in image recognition and the availability of large image databases have accelerated the development of advanced computer-aided systems for melanoma detection. In the present study, we used image data from the International Skin Image Collaboration archive to explore the capacity of deep knowledge transfer in the challenging diagnostic task of the atypical skin tumors of MM and SK.

Abstract

Malignant melanomas resembling seborrheic keratosis (SK-like MMs) are atypical, challenging to diagnose melanoma cases that carry the risk of delayed diagnosis and inadequate treatment. On the other hand, SK may mimic melanoma, producing a ‘false positive’ with unnecessary lesion excisions. The present study proposes a computer-based approach using dermoscopy images for the characterization of SΚ-like MMs. Dermoscopic images were retrieved from the International Skin Imaging Collaboration archive. Exploiting image embeddings from pretrained convolutional network VGG16, we trained a support vector machine (SVM) classification model on a data set of 667 images. SVM optimal hyperparameter selection was carried out using the Bayesian optimization method. The classifier was tested on an independent data set of 311 images with atypical appearance: MMs had an absence of pigmented network and had an existence of milia-like cysts. SK lacked milia-like cysts and had a pigmented network. Atypical MMs were characterized with a sensitivity and specificity of 78.6% and 84.5%, respectively. The advent of deep learning in image recognition has attracted the interest of computer science towards improved skin lesion diagnosis. Open-source, public access archives of skin images empower further the implementation and validation of computer-based systems that might contribute significantly to complex clinical diagnostic problems such as the characterization of SK-like MMs.