The Value of Total Body Photography for the Early Detection of Melanoma: A Systematic Review

Comparison of Extended Skin Cancer Screening Using a Three-Step Advanced Imaging Programme vs. Standard-of-Care Examination in a High-Risk Melanoma Patient Cohort

Modern diagnostic procedures, such as three-dimensional total body photography (3D-TBP), digital dermoscopy (DD), and reflectance confocal microscopy (RCM), can improve melanoma diagnosis, particularly in high-risk patients. This study assessed the benefits of combining these advanced imaging techniques in a three-step programme in managing high-risk patients. This study included 410 high-risk melanoma patients who underwent a specialised imaging consultation in addition to their regular skin examinations in outpatient care. At each visit, the patients underwent a 3D-TBP, a DD for suspicious findings, and an RCM for unclear DD findings. The histological findings of excisions initiated based on imaging consultation and outpatient care were compared. Imaging consultation detected sixteen confirmed melanomas (eight invasive and eight in situ) in 39 excised pigmented lesions. Outpatient care examination detected seven confirmed melanomas (one invasive and six in situ) in 163 excised melanocytic lesions. The number needed to excise (NNE) in the imaging consultation was significantly lower than that in the outpatient care (2.4 vs. 23.3). The NNE was 2.6 for DD and 2.3 for RCM. DD, 3D-TBP, or RCM detected melanomas that were not detected by the other imaging methods. The three-step imaging programme improves melanoma detection and reduces the number of unnecessary excisions in high-risk patients.

A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography

Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.

Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance

IMPORTANCE

Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis.

DESIGN AND PATIENTS

In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified "new" and "changed" lesions during follow-up examinations.

RESULTS

At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites).

CONCLUSIONS

ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings.

Artificial intelligence in the detection of skin cancer: State of the art

The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. To detect and recognize neoplastic lesions, such systems use various artificial intelligence (AI) algorithms. This area of computer science applications has recently undergone dynamic development, abounding in several solutions that are effective tools supporting diagnosticians in many medical specialties. In this contribution, a number of applications of different classes of AI algorithms for the detection of this skin melanoma are presented and evaluated. Both classic systems based on the analysis of dermatoscopic images as well as total body systems, enabling the analysis of the patient's whole body to detect moles and pathologic changes, are discussed. These increasingly popular applications that allow the analysis of lesion images using smartphones are also described. The quantitative evaluation of the discussed systems with particular emphasis on the method of validation of the implemented algorithms is presented. The advantages and limitations of AI in the analysis of lesion images are also discussed, and problems requiring a solution for more effective use of AI in dermatology are identified.

Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review

BACKGROUND

Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM.

METHODS

A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases.

RESULTS

A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%.

CONCLUSION

Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes.

Advancements in acne detection: application of the CenterNet network in smart dermatology

Introduction

Acne detection is critical in dermatology, focusing on quality control of acne imagery, precise segmentation, and grading. Traditional research has been limited, typically concentrating on singular aspects of acne detection.

Methods

We propose a multi-task acne detection method, employing a CenterNet-based training paradigm to develop an advanced detection system. This system collects acne images via smartphones and features multi-task capabilities for detecting image quality and identifying various acne types. It differentiates between noninflammatory acne, papules, pustules, nodules, and provides detailed delineation for cysts and post-acne scars.

Results

The implementation of this multi-task learning-based framework in clinical diagnostics demonstrated an 83% accuracy in lesion categorization, surpassing ResNet18 models by 12%. Furthermore, it achieved a 76% precision in lesion stratification, outperforming dermatologists by 16%.

Discussion

Our framework represents a advancement in acne detection, offering a comprehensive tool for classification, localization, counting, and precise segmentation. It not only enhances the accuracy of remote acne lesion identification by doctors but also clarifies grading logic and criteria, facilitating easier grading judgments.

A survey on deep learning for skin lesion segmentation

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online³.

Melanoma diagnosis at a specialist dermatology practice without the use of photographic surveillance

BACKGROUND/OBJECTIVE

Photographic aides are increasingly used in melanoma surveillance. We report melanoma characteristics detected using traditional surveillance without photographic technologies.

METHODS

Retrospective study of melanomas diagnosed by three dermatologists at a private dermatology practice over 7 years. Patients underwent full skin examinations with dermoscopy and suspect lesions were excised or biopsied. Total body photography (TBP) and serial digital dermoscopic imaging (SDDI) were not used. Patient demographics, melanoma subtype and thickness, location, biopsy technique and keratinocyte cancers diagnosed at the same visit were recorded. Ratio of in situ to invasive melanomas was calculated. Melanoma risk factors were recorded for 69 randomly-selected patients.

RESULTS

492 patients were diagnosed with 615 melanomas during 579 visits. 505 (82%) were in situ (in situ to invasive ratio of 4.6:1). Of the invasive melanomas, 85.5% had a Breslow thickness <0.8 mm, 10 (9.1%) 0.8-1 mm and 6 (5.5%) >1 mm. 43.3% of in situ melanomas were lentiginous or lentigo maligna and 41.6% were superficial spreading melanomas (SSM). Of invasive melanomas, 24.3% were lentigo maligna melanoma and 59.5% were SSM. 48.4% of melanomas were diagnosed by shave procedures. Where risk factors were known, 25% were very-high-risk and 43% had a history of melanoma. Keratinocyte carcinoma was diagnosed by biopsy at 26.1% of visits. Studies using TBP and/or SDDI report in situ to invasive ratios of 0.59:1 to 2.17:1.

CONCLUSION

Tradiational melanoma surveillance with immediate biopsy of suspect lesions results in high in situ to invasive ratios. Studies using photographic surveillance show lower ratios of in situ to invasive disease.

Consumer views of melanoma early detection using 3D total-body photography: cross-sectional survey

BACKGROUND

Three-dimensional (3D) total-body photography is a recent advance in melanoma early detection that can aid in monitoring and identifying new and changing skin lesions over time.

METHODS

A cross-sectional survey of adults living in metropolitan and rural areas of Australia was conducted to assess perceptions of 3D total-body photography. Participants completed a survey detailing their previous use of skin cancer photography, personal skin checking history, perceptions of 3D total-body photography, and willingness to pay. Participants were also asked to describe the potential barriers and facilitators of 3D total-body photography in their own words.

RESULTS

A total of 1056 participants completed the survey, with 739 (70%) from metropolitan areas of Australia and 317 (30%) from rural areas. Most participants (95%, n = 1004/1056) indicated they would consider using 3D total-body photography if it became commercially available at their regular medical practice. Most participants indicated 3D total-body photography would be effective to identify suspicious skin spots (94%, n = 995/1056), monitor lesion changes (94%, n = 997/1056), and reduce skin cancer related anxiety (90%, n = 950/1055). In open-ended feedback, participants (87%, n = 918/1056) identified perceived benefits, including more comprehensive screenings, earlier detection, and less human error. Participants (84%, n = 889/1056) also identified potential barriers to 3D total-body photography, including cost, accessibility and availability, trust in the technology, and digital security concerns.

CONCLUSIONS

Participant feedback indicated a high level of acceptance and confidence in the technology. To facilitate clinical translation, addressing consumer-identified barriers to 3D total-body photography will be vital.

Development and Narrow Validation of Computer Vision Approach to Facilitate Assessment of Change in Pigmented Cutaneous Lesions

The documentation of the change in the number and appearance of pigmented cutaneous lesions over time is critical to the early detection of skin cancers and may provide preliminary signals of efficacy in early-phase therapeutic prevention trials for melanoma. Despite substantial progress in computer-aided diagnosis of melanoma, automated methods to assess the evolution of lesions are relatively undeveloped. This report describes the development and narrow validation of mathematical algorithms to register nevi between sequential digital photographs of large areas of skin and to align images for improved detection and quantification of changes. Serial posterior truncal photographs from a pre-existing database were processed and analyzed by the software, and the results were evaluated by a panel of clinicians using a separate Extensible Markup Language‒based application. The software had a high sensitivity for the detection of cutaneous lesions as small as 2 mm. The software registered lesions accurately, with occasional errors at the edges of the images. In one pilot study with 17 patients, the use of the software enabled clinicians to identify new and/or enlarged lesions in 3‒11 additional patients versus the unregistered images. Automated quantification of size change performed similarly to that of human raters. These results support the further development and broader validation of this technique.

Silva C, Horsham C, Jayasinghe D, Sanjida S, Schaider H, Aitken J, Sturm RA, Prow T, Menzies SW, Janda M. Skin cancer excisions and histopathology outcomes when following a contemporary population‐based cohort longitudinally with 3D total‐body photography

Background

Skin cancer represents a significant health burden across the globe and early detection is critical to improve health outcomes. Three-dimensional (3D) total-body photography is a new and emerging technology which can support clinicians when they monitor people's skin over time.

Objectives

The aim of this study was to improve our understanding of the epidemiology and natural history of melanocytic naevi in adults, and their relationship with melanoma and other skin cancers.

Methods

Mind Your Moles was a 3-year prospective, population-based cohort study which ran from December 2016 to February 2020. Participants visited the Princess Alexandra Hospital every 6 months for 3 years to undergo both a clinical skin examination and 3D total-body photography.

Results

A total of 1213 skin screening imaging sessions were completed. Fifty-six percent of participants (n = 108/193) received a referral to their own doctor for 250 lesions of concern, 101/108 (94%) for an excision/biopsy. Of those, 86 people (85%) visited their doctor and received an excision/biopsy for 138 lesions. Histopathology of these lesions found 39 non-melanoma skin cancers (across 32 participants) and six in situ melanomas (across four participants).

Conclusions

3D total-body imaging results in diagnosis of a high number of keratinocyte cancers (KCs) and their precursors in the general population.

[The Rise of Artificial Intelligence - High Prediction Accuracy in Early Detection of Pigmented Melanoma]

The incidence of malignant melanoma is increasing worldwide. If detected early, melanoma is highly treatable, so early detection is vital.Skin cancer early detection has improved significantly in recent decades, for example by the introduction of screening in 2008 and dermoscopy. Nevertheless, in particular visual detection of early melanomas remains challenging because they show many morphological overlaps with nevi. Hence, there continues to be a high medical need to further develop methods for early skin cancer detection in order to be able to reliably diagnosemelanomas at a very early stage.Routine diagnostics for melanoma detection include visual whole body inspection, often supplemented by dermoscopy, which can significantly increase the diagnostic accuracy of experienced dermatologists. A procedure that is additionally offered in some practices and clinics is wholebody photography combined with digital dermoscopy for the early detection of malignant melanoma, especially for monitoring high-risk patients.In recent decades, numerous noninvasive adjunctive diagnostic techniques were developed for the examination of suspicious pigmented moles, that may have the potential to allow improved and, in some cases, automated evaluation of these lesions. First, confocal laser microscopy should be mentioned here, as well as electrical impedance spectroscopy, multiphoton laser tomography, multispectral analysis, Raman spectroscopy or optical coherence tomography. These diagnostic techniques usually focus on high sensitivity to avoid malignant melanoma being overlooked. However, this usually implies lower specificity, which may lead to unnecessary excision of benign lesions in screening. Also, some of the procedures are time-consuming and costly, which also limits their applicability in skin cancer screening. In the near future, the use of artificial intelligence might change skin cancer diagnostics in many ways. The most promising approach may be the analysis of routine macroscopic and dermoscopic images by artificial intelligence.For the classification of pigmented skin lesions based on macroscopic and dermoscopic images, artificial intelligence, especially in form of neural networks, has achieved comparable diagnostic accuracies to dermatologists under experimental conditions in numerous studies. In particular, it achieved high accuracies in the binary melanoma/nevus classification task, but it also performed comparably well to dermatologists in multiclass differentiation of various skin diseases. However, proof of the basic applicability and utility of such systems in clinical practice is still pending. Prerequisites that remain to be established to enable translation of such diagnostic systems into dermatological routine are means that allow users to comprehend the system's decisions as well as a uniformly high performance of the algorithms on image data from other hospitals and practices.At present, hints are accumulating that computer-aided diagnosis systems could provide their greatest benefit as assistance systems, since studies indicate that a combination of human and machine achieves the best results. Diagnostic systems based on artificial intelligence are capable of detecting morphological characteristics quickly, quantitatively, objectively and reproducibly, and could thus provide a more objective analytical basis - in addition to medical experience.

Diagnostics Using Non-Invasive Technologies in Dermatological Oncology

The growing incidence of skin cancer, with its associated mortality and morbidity, has in recent years led to the developing of new non-invasive technologies, which allow an earlier and more accurate diagnosis. Some of these, such as digital photography, 2D and 3D total-body photography and dermoscopy are now widely used and others, such as reflectance confocal microscopy and optical coherence tomography, are limited to a few academic and referral skin cancer centers because of their cost or the long training period required. Health care professionals involved in the treatment of patients with skin cancer need to know the implications and benefits of new non-invasive technologies for dermatological oncology. In this article we review the characteristics and usability of the main diagnostic imaging methods available today.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

The Experience of 3D Total-Body Photography to Monitor Nevi: Results From an Australian General Population-Based Cohort Study

BACKGROUND

Digital 3D total-body photography of the skin surface is an emerging imaging modality that can facilitate the identification of new and changing nevi.

OBJECTIVE

We aimed to describe the experiences of study participants drawn from the general population who were provided 3D total-body photography and dermoscopy for the monitoring of nevi.

METHODS

A population-based prospective study of adults aged 20-70 years from South East Queensland, Australia was conducted. Participants underwent 3D total-body photography and dermoscopy every 6 months over a 3-year period. Participants were asked to provide closed and open-ended feedback on their 3D total-body photography and dermoscopy experience (eg, comfort, trust, intended future use, and willingness to pay) at the halfway study time point (18 months) and final study time point (36 months). We assessed changes in participants' reported experience of 3D total-body photography, and patient characteristics associated with patient experience at the end of the study (36 months) were analyzed.

RESULTS

A total of 149 participants completed the surveys at both the 18- and 36-month time points (median age 55, range 23-70 years; n=94, 63.1% were male). At the 18-month time point, most participants (n=103, 69.1%) stated they completely trusted 3D total-body imaging for the diagnosis and monitoring of their nevi, and this did not change at the 36-month (n=104, 69.8%) time point. The majority of participants reported that they were very comfortable or comfortable with the technology at both the 18- (n=138, 92.6%) and 36-month (n=140, 94%) time points, respectively; albeit, the number of participants reporting that they were very comfortable reduced significantly between the 18- and 36-month time points, from 71.1% (n=106) to 61.1% (n=91; P=.01). Almost all participants (n=140, 94%) would consider using this technology if it were to become commercially available, and this did not change during the two study time points. Half of the participants (n=74) cited barriers to participating in 3D total-body photography, including trust in the ability of this technology to detect and monitor suspicious lesions, digital privacy, cost, and travel requirements.

CONCLUSIONS

The majority of participants expressed positive attitudes toward 3D total-body photography for the monitoring of their moles. Half of the participants identified potential barriers to uptake.

Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome

Weltweit steigt die Inzidenz des malignen Melanoms an. Bei frühzeitiger Erkennung ist das Melanom gut behandelbar, eine Früherkennung ist also lebenswichtig.

Die Hautkrebs-Früherkennung hat sich in den letzten Jahrzehnten bspw. durch die Einführung des Screenings im Jahr 2008 und die Dermatoskopie deutlich verbessert. Dennoch bleibt die visuelle Erkennung insbesondere von frühen Melanomen eine Herausforderung, weil diese viele morphologische Überlappungen mit Nävi zeigen. Daher ist der medizinische Bedarf weiterhin hoch, die Methoden zur Hautkrebsfrüherkennung gezielt weiterzuentwickeln, um Melanome bereits in einem sehr frühen Stadium sicher diagnostizieren zu können.

Die Routinediagnostik zur Hautkrebs-Früherkennung umfasst die visuelle Ganzkörperinspektion, oft ergänzt durch die Dermatoskopie, durch die sich die diagnostische Treffsicherheit erfahrener Hautärzte deutlich erhöhen lässt. Ein Verfahren, was in einigen Praxen und Kliniken zusätzlich angeboten wird, ist die kombinierte Ganzkörperfotografie mit der digitalen Dermatoskopie für die Früherkennung maligner Melanome, insbesondere für das Monitoring von Hochrisiko-Patienten.

In den letzten Jahrzenten wurden zahlreiche nicht invasive zusatzdiagnostische Verfahren zur Beurteilung verdächtiger Pigmentmale entwickelt, die das Potenzial haben könnten, eine verbesserte und z. T. automatisierte Bewertung dieser Läsionen zu ermöglichen. In erster Linie ist hier die konfokale Lasermikroskopie zu nennen, ebenso die elektrische Impedanzspektroskopie, die Multiphotonen-Lasertomografie, die Multispektralanalyse, die Raman-Spektroskopie oder die optische Kohärenztomografie. Diese diagnostischen Verfahren fokussieren i. d. R. auf hohe Sensitivität, um zu vermeiden, ein malignes Melanom zu übersehen. Dies bedingt allerdings üblicherweise eine geringere Spezifität, was im Screening zu unnötigen Exzisionen vieler gutartiger Läsionen führen kann. Auch sind einige der Verfahren zeitaufwendig und kostenintensiv, was die Anwendbarkeit im Screening ebenfalls einschränkt.

In naher Zukunft wird insbesondere die Nutzung von künstlicher Intelligenz die Diagnosefindung in vielfältiger Weise verändern. Vielversprechend ist v. a. die Analyse der makroskopischen und dermatoskopischen Routine-Bilder durch künstliche Intelligenz. Für die Klassifizierung von pigmentierten Hautläsionen anhand makroskopischer und dermatoskopischer Bilder erzielte die künstliche Intelligenz v. a. in Form neuronaler Netze unter experimentellen Bedingungen in zahlreichen Studien bereits eine vergleichbare diagnostische Genauigkeit wie Dermatologen. Insbesondere bei der binären Klassifikationsaufgabe Melanom/Nävus erreichte sie hohe Genauigkeiten, doch auch in der Multiklassen-Differenzierung von verschiedenen Hauterkrankungen zeigt sie sich vergleichbar gut wie Dermatologen. Der Nachweis der grundsätzlichen Anwendbarkeit und des Nutzens solcher Systeme in der klinischen Praxis steht jedoch noch aus. Noch zu schaffende Grundvoraussetzungen für die Translation solcher Diagnosesysteme in die dermatologischen Routine sind Möglichkeiten für die Nutzer, die Entscheidungen des Systems nachzuvollziehen, sowie eine gleichbleibend gute Leistung der Algorithmen auf Bilddaten aus fremden Kliniken und Praxen.

Derzeit zeichnet sich ab, dass computergestützte Diagnosesysteme als Assistenzsysteme den größten Nutzen bringen könnten, denn Studien deuten darauf hin, dass eine Kombination von Mensch und Maschine die besten Ergebnisse erzielt. Diagnosesysteme basierend auf künstlicher Intelligenz sind in der Lage, Merkmale schnell, quantitativ, objektiv und reproduzierbar zu erfassen, und könnten somit die Medizin auf eine mathematische Grundlage stellen – zusätzlich zur ärztlichen Erfahrung.

Melanoma: How and When to Consider Clinical Diagnostic Technologies

In response to rising rates of melanoma worldwide, novel non-invasive melanoma detection techniques are emerging to facilitate the early detection of melanoma and decrease unnecessary biopsies of benign pigmented lesions. Because they often report similar study findings, it may be difficult to determine how best to incorporate these technologies into clinical practice based on their supporting studies alone. As an expansion of the recent article by Fried et al.¹, which reviewed the clinical data supporting these non-invasive melanoma detection techniques, the first article in this continuing medical education series provides practical advice on how and when to use various non-invasive melanoma detection techniques into clinical practice.

Etiologies of Melanoma Development and Prevention Measures: A Review of the Current Evidence

Simple Summary

Melanoma constitutes a major public health risk, with the rates of diagnosis increasing on a yearly basis. Monitoring for risk factors and preventing dangerous behaviors that increase melanoma risk, such as tanning, are important measures for melanoma prevention. Additionally, assessing the effectiveness of various methods to prevent sun exposure and sunburns—which can lead to melanoma—is important to help identify ways to reduce the development of melanoma. We summarize the recent evidence regarding the heritable and behavioral risks underlying melanoma, as well as the current methods used to reduce the risk of developing melanoma and to improve the diagnosis of this disease.

Abstract

(1) Melanoma is the most aggressive dermatologic malignancy, with an estimated 106,110 new cases to be diagnosed in 2021. The annual incidence rates continue to climb, which underscores the critical importance of improving the methods to prevent this disease. The interventions to assist with melanoma prevention vary and typically include measures such as UV avoidance and the use of protective clothing, sunscreen, and other chemopreventive agents. However, the evidence is mixed surrounding the use of these and other interventions. This review discusses the heritable etiologies underlying melanoma development before delving into the data surrounding the preventive methods highlighted above. (2) A comprehensive literature review was performed to identify the clinical trials, observational studies, and meta-analyses pertinent to melanoma prevention and incidence. Online resources were queried to identify epidemiologic and clinical trial information. (3) Evidence exists to support population-wide screening programs, the proper use of sunscreen, and community-targeted measures in the prevention of melanoma. Clinical evidence for the majority of the proposed preventive chemotherapeutics is presently minimal but continues to evolve. (4) Further study of these chemotherapeutics, as well as improvement of techniques in artificial intelligence and imaging techniques for melanoma screening, is warranted for continued improvement of melanoma prevention.