Diagnostic accuracy of dermoscopy

Dermoscopy of early melanomas: variation according to the anatomic site

To date, is yet to be elucidated whether the body location of cutaneous melanoma can significantly affect an early dermoscopic diagnosis and, consequently, if it can be regarded as a prognostic factor. To investigate the dermoscopic appearance of early melanomas (EMs) at different body sites; to test the ability of dermoscopists in recognizing specific dermoscopic features in EMs. A pool of 106 experienced dermoscopists evaluated the presence of 10 dermoscopic features assumed as suggestive of malignancy among 268 images of EMs with ambiguous appearance located at 16 body sites. According to 720 evaluations, EMs of the "upper extremities" showed a prevalence of early atypical lentiginous features. EMs of the "anterior trunk" exhibited the lower rate of recognition for all features. EMs of the "rear trunk" can be regarded as an intermediate area, showing high recognition rates of regression-related and chronic-traumatism-related features.

Training general practitioners in melanoma diagnosis: a scoping review of the literature

BACKGROUND

General practitioners (GPs) play a key role in early melanoma detection. To help GPs deal with suspicious skin lesions, melanoma diagnostic training programmes have been developed. However, it is unclear whether these programmes guarantee the acquisition of skills that will be applied by GPs in their daily clinical practice and maintained over time.

OBJECTIVES

This scoping review aimed to examine and compare educational programmes designed to train GPs in melanoma diagnosis using clinical (naked eye) examination alone or dermoscopy±clinical examination, and sought to inform on the long-term sustainability of the GPs' acquired skills.

ELIGIBILITY CRITERIA

Studies eligible for inclusion evaluated educational programmes for teaching diagnosis of melanoma to GPs. MEDLINE, EMBASE and Cochrane databases were searched for relevant articles from 1995 to May 2020.

RESULTS

Forty-five relevant articles were found assessing 31 educational programmes. Most programmes that improved the diagnostic accuracy and long-term performances of the GPs, that is, increase in confidence, decrease in dermatologist referral for benign skin lesions and improvement in the benign/malignant ratio of excised skin lesions, trained the GPs in clinical diagnosis, followed by dermoscopy. To maintain long-term performances, these programmes provided refresher training material.

CONCLUSION

This review shows that studies generally report positive outcomes from the training of GPs in melanoma diagnosis. However, refresher training material seemed necessary to maintain the acquired skills. The optimal form and ideal frequency for these updates have yet to be defined.

SEOM clinical guideline for the management of cutaneous melanoma (2020)

Melanoma affects about 6000 patients a year in Spain. A group of medical oncologists from Spanish Society of Medical Oncology (SEOM) and Spanish Multidisciplinary Melanoma Group (GEM) has designed these guidelines to homogenize the management of these patients. The diagnosis must be histological and determination of BRAF status has to be performed in patients with stage ≥ III. Stage I–III resectable melanomas will be treated surgically. In patients with stage III melanoma, adjuvant treatment with immunotherapy or targeted therapy is also recommended. Patients with unresectable or metastatic melanoma will receive treatment with immunotherapy or targeted therapy, the optimal sequence of these treatments remains unclear. Brain metastases require a separate consideration, since, in addition to systemic treatment, they may require local treatment. Patients must be followed up closely to receive or change treatment as soon as their previous clinical condition changes, since multiple therapeutic options are available.

Clinical performance of a novel hyperspectral imaging device for cutaneous melanoma and pigmented skin lesions in Caucasian skin

BACKGROUND

The quest for diagnostic tools for the detection of cutaneous malignant melanoma (cMM) is ongoing. A challenge in cMM care is not overlooking cMM at an early stage, while simultaneously avoiding unnecessary biopsies or excisions of benign pigmented skin lesions (PSLs). A novel hyperspectral imaging (HSI) device is shown to have potential for differentiating equivocal PSLs in Asian skin types. Our objective was to assess the accuracy of the HSI device in distinguishing between cMM and benign PSLs in patients with Caucasian skin types.

METHODS

Patients with Caucasian skin types (Fitzpatrick I-II), enrolled for excisional biopsies of PSLs were included and examined using the HSI device. The discrimination index (DI) was used to demonstrate the sensitivity (SE) and specificity (SP) in comparison with the re-evaluated histopathology diagnoses.

RESULTS

In 186 patients, 202 pigmented skin lesions were included. The sensitivity to detect cMM was 96.7% (87/90), and the specificity for benign lesions was 42.1% (45/107). The AUC was 0.800 (95% confidence interval (CI): 0.740-0.861).

CONCLUSIONS

Our novel HSI device showed a high sensitivity in detecting malignant lesions in patients with Caucasian skin types. Compared with analogous technologies, as multispectral imaging or electrical impedance spectroscopy, our device showed similar or better accuracy in differentiating cMM from benign PSLs. Therefore, it might be a useful clinical tool in skin types I-IV and where further triage of pigmented skin lesions is important.

Reproduction of patterns in melanocytic proliferations by agent-based simulation and geometric modeling

Spatio-temporal patterns of melanocytic proliferations observed in vivo are important for diagnosis but the mechanisms that produce them are poorly understood. Here we present an agent-based model for simulating the emergence of the main biologic patterns found in melanocytic proliferations. Our model portrays the extracellular matrix of the dermo-epidermal junction as a two-dimensional manifold and we simulate cellular migration in terms of geometric translations driven by adhesive, repulsive and random forces. Abstracted cellular functions and melanocyte-matrix interactions are modeled as stochastic events. For identification and validation we use visual renderings of simulated cell populations in a horizontal perspective that reproduce growth patterns observed in vivo by sequential dermatoscopy and corresponding vertical views that reproduce the arrangement of melanocytes observed in histopathologic sections. Our results show that a balanced interplay of proliferation and migration produces the typical reticular pattern of nevi, whereas the globular pattern involves additional cellular mechanisms. We further demonstrate that slight variations in the three basic cellular properties proliferation, migration, and adhesion are sufficient to produce a large variety of morphological appearances of nevi. We anticipate our model to be a starting point for the reproduction of more complex scenarios that will help to establish functional connections between abstracted microscopic behavior and macroscopic patterns in all types of melanocytic proliferations including melanoma.

Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images

As an analytic tool in medicine, deep learning has gained great attention and opened new ways for disease diagnosis. Recent studies validate the effectiveness of deep learning algorithms for binary classification of skin lesions (i.e., melanomas and nevi classes) with dermoscopic images. Nonetheless, those binary classification methods cannot be applied to the general clinical situation of skin cancer screening in which multi-class classification must be taken into account. The main objective of this research is to develop, implement, and calibrate an advanced deep learning model in the context of automated multi-class classification of skin lesions. The proposed Deep Convolutional Neural Network (DCNN) model is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficacy and performance. Dermoscopic images are acquired from the International Skin Imaging Collaboration databases (ISIC-17, ISIC-18, and ISIC-19) for experiments. The experimental results of the proposed DCNN approach are presented in terms of precision, sensitivity, specificity, and other metrics. Specifically, it attains 94 % precision, 93 % sensitivity, and 91 % specificity in ISIC-17. It is demonstrated by the experimental results that this proposed DCNN approach outperforms state-of-the-art algorithms, exhibiting 0.964 area under the receiver operating characteristics (AUROC) in ISIC-17 for the classification of skin lesions and can be used to assist dermatologists in classifying skin lesions. As a result, this proposed approach provides a novel and feasible way for automating and expediting the skin lesion classification task as well as saving effort, time, and human life.

Skin Cancer and Dermoscopy Training for Primary Care Physicians: A Pilot Study

INTRODUCTION

The primary objective of this study was to determine the diagnostic accuracy and effect of an educational training on skin cancer course and dermoscopy use among primary care physicians in rural areas of Colombia. The secondary objective was to assess the diagnostic accuracy of skin cancer diagnosis and detection rate after 3 months of the initial training.

METHODS

Twenty-one primary care physicians from 6 rural areas of Colombia participated in a 2-day skin cancer and dermoscopy training, followed by a day-long hands-on session on dermoscopy at a free skin cancer screening event. Pre- and post-tests were performed using clinical and dermoscopic images to evaluate the user's ability to diagnose and differentiate benign and malignant neoplasms. In addition, participants' levels of confidence were assessed.

RESULTS

After the training, the sensitivity and specificity of characterizing skin lesions as benign or malignant or providing a specific diagnosis (ie, angioma, seborrheic keratosis, basal cell carcinoma, etc.) increased by 23.6% (62.9% to 86.5%; P < 0.0001) and 21% (54.7% to 75.7%; P < 0.0017), respectively. In addition, levels of confidence when diagnosing skin lesions changed from extremely low or low, to high or extremely high by 20.7% (38.3% to 59%) using dermoscopic images (odds ratio (OR) 3.22; 95% confidence interval (CI): 2.67-3.86; P < 0.0001). The secondary objective was not achieved due to loss of follow-up of the majority of participants.

CONCLUSION

Providers serving populations with limited healthcare access may benefit from education in diagnosing and differentiating skin cancer with the use of dermoscopy, which may ultimately improve patient care and reduce healthcare costs.

Data Augmentation Using Adversarial Image-to-Image Translation for the Segmentation of Mobile-Acquired Dermatological Images

Dermoscopic images allow the detailed examination of subsurface characteristics of the skin, which led to creating several substantial databases of diverse skin lesions. However, the dermoscope is not an easily accessible tool in some regions. A less expensive alternative could be acquiring medium resolution clinical macroscopic images of skin lesions. However, the limited volume of macroscopic images available, especially mobile-acquired, hinders developing a clinical mobile-based deep learning approach. In this work, we present a technique to efficiently utilize the sizable number of dermoscopic images to improve the segmentation capacity of macroscopic skin lesion images. A Cycle-Consistent Adversarial Network is used to translate the image between the two distinct domains created by the different image acquisition devices. A visual inspection was performed on several databases for qualitative evaluation of the results, based on the disappearance and appearance of intrinsic dermoscopic and macroscopic features. Moreover, the Fréchet Inception Distance was used as a quantitative metric. The quantitative segmentation results are demonstrated on the available macroscopic segmentation databases, SMARTSKINS and Dermofit Image Library, yielding test set thresholded Jaccard Index of 85.13% and 74.30%. These results establish a new state-of-the-art performance in the SMARTSKINS database.

Morphologic Features of Cutaneous T-Cell Lymphomas Using Dermoscopy and High Frequency Ultrasound

The diagnosis of cutaneous T-cell lymphomas (CTCL) is frequently delayed by a median of three years and requires the clinical evaluation of an experienced dermatologist and a confirmatory skin biopsy. Dermoscopy and high-frequency ultrasound (HFUS) represent two non-invasive diagnostic tools. While dermoscopy is inexpensive and widely used for the diagnosis of melanoma and non-melanoma skin cancers, HFUS of skin lymphomas represents a novel diagnostic approach that is not yet implemented in the routine dermatologic practice. The aim of our study was to prospectively assess skin lesions of patients with either CTCL patches or plaques with dermoscopy and HFUS and to compare the findings with atopic dermatitis (AD) and psoriasis. Thirteen patients with an established diagnosis of CTCL, psoriasis, or AD were studied: Dermoscopy features including spermatozoa-like structures and the presence of white scales could assist in differentiating between early-stage CTCL and AD. HFUS measurements of the skin thickness indicated increased epidermal-, thickness in CTCL, and psoriasis compared with AD. Our results support the use of dermoscopy as a useful tool to diagnose CTCL. HFUS could augment the dermatologic assessment, but further studies will be needed to define standardized parameters.

Lupus Vulgaris in Darker Skin: Dermoscopic and Histopathologic Incongruity

Introduction

Lupus Vulgaris (LV) is the chronic, progressive, tissue destructive form of cutaneous tuberculosis. LV should be diagnosed and treated to prevent scaring and deformities. Histopathology is the gold standard for the diagnosis. Dermoscopy is helpful tool in diagnosing different dermatological condition. Here, dermoscopic and histopathogical correlation in LV was attempted.

Materials and Methods

It was a cross sectional, observational study done from February 2019 to October 2019. Nineteen patients of LV were included. Dermlite 4 with attached smart phone (iphone) was employed. LV lesions were subjected to skin biopsy to confirm the diagnosis.

Results

Study enrolled 19 patients, with 8males, 5 female and 6 children. Dermoscopy showed yellowish-white globules, white structureless areas and white scales were noted in 19 (100%) patients. Telangiectasias were seen in 16 (84.21%) patients as long linear, branching and short linear vessels. Pinkish-red background was noted in all patients (100% n=19). Newer observations included white shiny streaks, white rosettes and bluish hue. Age, sex, duration of lesions had no influence in the dermoscopic patterns. Discrepancy in dermoscopic-histopathologic correlation was noted. Facial lesions showed increased frequency of follicular plugs, patulous follicles and white rosettes.

Conclusion

Dermoscopy is widely gaining importance in the realm of dermatology. In this study, dermoscopy demonstrated characteristic patterns in LV. Thus, dermoscopy a non-invasive procedure can be used as diagnostic tool in many infective dermatoses.

Skin Lesion Classification Using Densely Connected Convolutional Networks with Attention Residual Learning

Skin lesion classification is an effective approach aided by computer vision for the diagnosis of skin cancer. Though deep learning models presented advantages over traditional methods and brought tremendous breakthroughs, a precise diagnosis is still challenging because of the intra-class variation and inter-class similarity caused by the diversity of imaging methods and clinicopathology. In this paper, we propose a densely connected convolutional network with an attention and residual learning (ARDT-DenseNet) method for skin lesion classification. Each ARDT block consists of dense blocks, transition blocks and attention and residual modules. Compared to a residual network with the same number of convolutional layers, the size of the parameters of the densely connected network proposed in this paper has been reduced by half, while the accuracy of skin lesion classification is preserved. Our improved densely connected network adds an attention mechanism and residual learning after each dense block and transition block without introducing additional parameters. We evaluate the ARDT-DenseNet model with the ISIC 2016 and ISIC 2017 datasets. Our method achieves an ACC of 85.7% and an AUC of 83.7% in skin lesion classification with ISIC 2016 and an average AUC of 91.8% in skin lesion classification with ISIC 2017. The experimental results show that the method proposed in this paper has achieved a significant improvement in skin lesion classification, which is superior to that of the state-of-the-art method.

Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions

This study evaluated whether deep learning frameworks trained in large datasets can help non-dermatologist physicians improve their accuracy in categorizing the seven most common pigmented skin lesions. Open-source skin images were downloaded from the International Skin Imaging Collaboration (ISIC) archive. Different deep neural networks (DNNs) (n = 8) were trained based on a random dataset constituted of 8015 images. A test set of 2003 images was used to assess the classifiers' performance at low (300 × 224 RGB) and high (600 × 450 RGB) image resolution and aggregated data (age, sex and lesion localization). We also organized two different contests to compare the DNN performance to that of general practitioners by means of unassisted image observation. Both at low and high image resolution, the DNN framework differentiated dermatological images with appreciable performance. In all cases, the accuracy was improved when adding clinical data to the framework. Finally, the least accurate DNN outperformed general practitioners. The physician's accuracy was statistically improved when allowed to use the output of this algorithmic framework as guidance. DNNs are proven to be high performers as skin lesion classifiers and can improve general practitioner diagnosis accuracy in a routine clinical scenario.

Improving the prevention and diagnosis of melanoma on a national scale: A comparative study of performance in the United Kingdom and Australia

We undertook this study in light of an uncontrolled rise of melanoma incidence and mortality rates in the United Kingdom (UK). We aim to assess the effectiveness of prevention and early melanoma diagnosis in the UK's National Health Service (NHS) in comparison to the Australian system that has limited the melanoma rise. We compare the prevention campaigns against skin cancer and the stage at which melanoma is diagnosed. We analyse key drivers of early diagnosis. Overall, Australia has performed better than the UK and provides an example for the UK's NHS for better preventing melanoma and diagnosing it. Technologies under development, such as tele-dermatology and artificial intelligence applications, could aid in making melanoma early diagnosis easier, more cost-efficient, and lessen the burden on health systems. Diagnoses also provide the data to help public health officials target prevention programs.

Technical Note: Noninvasive mid-IR fiber-optic evanescent wave spectroscopy (FEWS) for early detection of skin cancers

PURPOSE

Melanoma is the most lethal of the three primary skin cancers, including also basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), which are less lethal. The accepted diagnosis process involves manually observing a suspicious lesion through a Dermascope (i.e., a magnifying glass), followed by a biopsy. This process relies on the skill and the experience of a dermatologist. However, to the best of our knowledge, there is no accepted automatic, noninvasive, and rapid method for the early detection of the three types of skin cancer, distinguishing between them and noncancerous lesions, and identifying each of them. It is our aim to develop such a system.

METHODS

We developed a fiber-optic evanescent wave spectroscopy (FEWS) system based on middle infrared (mid-IR) transmitting AgClBr fibers and a Fourier-transform infrared spectrometer (FTIR). We used the system to perform mid-IR spectral measurements on suspicious lesions in 90 patients, before biopsy, in situ, and in real time. The lesions were then biopsied and sent for pathology. The spectra were analyzed and the differences between pathological and healthy tissues were found and correlated.

RESULTS

Five of the lesions measured were identified as melanomas, seven as BCC, and three as SCC. Using mathematical analyses of the spectra of these lesions we were able to tell that all were skin cancers and we found specific and easily identifiable differences between them.

CONCLUSIONS

This FEWS method lends itself to rapid, automatic and noninvasive early detection and characterization of skin cancers. It will be easily implemented in community clinics and has the potential to greatly simplify the diagnosis process.

A GAN-based image synthesis method for skin lesion classification

BACKGROUND AND OBJECTIVE

There are many types of skin cancer, and melanoma is the most lethal one. Dermoscopy is an important imaging technique to screen melanoma and other skin lesions. However, Skin lesion classification based on computer-aided diagnostic techniques is a challenging task owing to the scarcity of labeled data and class-imbalanced dataset. It is necessary to apply data augmentation technique based on generative adversarial networks (GANs) to skin lesion classification for helping dermatologists in more accurate diagnostic decisions.

METHODS

A whole process of using GAN-based data augmentation technology to improve the skin lesion classification performance has been established in this article. First of all, the skin lesion style-based GANs is proposed according to the basic architecture of style-based GANs. The proposed model modifies the structure of style control and noise input in the original generator, adjusts both the generator and discriminator to efficiently synthesize high-quality skin lesion images. As for image classification, the classifier is constructed on the pretrained deep neural network using transfer learning method. The synthetic images from the proposed skin lesion style-based GANs are finally added to the training set to help train the classifier for better classification performance.

RESULTS

The proposed skin lesion style-based GAN has been evaluated by Inception Score (IS), Fréchet Inception Distance (FID), Precision and Recall, and is superior to other compared GAN models in these quantitative evaluation metrics. By adding the synthesized images to the training set, the main classification indicators like accuracy, sensitivity, specificity, average precision and balanced multiclass accuracy are 95.2%, 83.2%, 74.3%, 96.6% and 83.1% on the dataset of International Skin Imaging Collaboration (ISIC) 2018 Challenge, which have been improved by 1.6%, 24.4%, 3.6%, 23.2% and 5.6% respectively compared to the CNN model.

CONCLUSIONS

The proposed skin lesion style-based GANs can generate high-quality skin lesion images efficiently, leading to the performance improvement of the classification model. This work provides a valuable reference for medical image analysis based on deep learning.

Dermatoscopic Features of Combined nevus – a Case Report

Combined nevi (CN), a rare nevus type represent a category of so-called compound tumors. Determined by the presence of two or more different nevus in one biopsy specimen, CN commonly show variable clinical and dermatoscopic features. Therefore, CN could be a diagnostic challenge. We present a 7-year-old Caucasian girl with a pigmented lesion on the arm of no specified duration. Clinical examination showed sharply demarcated pigmented papule. Dermatoscopy revealed a nonchaotic lesion with structureless well defined, minimally eccentric blue area, structureless brown area and brown clods in a symmetric fashion, no vessels and no other clues for melanoma. Histopathology showed a compound common melanocytic nevus, blue nevus in the centre of the lesion with no signs of atypia. Up to now, only 25 cases of CN with dermatoscopic description have been published, withno precise dermatoscopic features established yet. Therefore, studies with larger number of cases are needed for the final conclusions.

Profile of the use of dermoscopy among dermatologists in Brazil (2018)

BACKGROUND

Dermoscopy increases the diagnostic accuracy in dermatology. The aspects related to training, usage profile, or perceptions of usefulness of dermoscopy among dermatologists in Brazil have not been described.

OBJECTIVES

To evaluate the profile of the use of dermoscopy and the perception of the impact of the technique on clinical practice.

METHODS

The Brazilian Society of Dermatology invited all members to complete an online form with 20 items regarding demographic data, dermatological assistance, use of dermoscopy, and perceptions of the impact of the technique on clinical practice. The proportions between the categories were compared by analysis of residuals in contingency tables, and p-values < 0.01 were considered significant.

RESULTS

The answers from 815 associates (9.1% of those invited to participate) were assessed, 84% of whom were female, and 71% of whom were younger than 50 years of age. The use of dermoscopy was reported in the daily practice of 98% of dermatologists: 88% reported using it more than once a day. Polarized light dermoscopy was the most used method (83%) and pattern analysis was the most used algorithm (63%). The diagnosis and follow-up of melanocytic lesions was identified as the main use of the technique, while the benefit for the diagnosis of inflammatory lesions was acknowledged by less than half of the sample (42%).

STUDY LIMITATIONS

This was a non-randomized study.

CONCLUSION

Dermoscopy is incorporated into the clinical practice of almost all Brazilian dermatologists, and it is recognized for increasing diagnostic certainty in different contexts, especially for pigmented lesions.

Exploration of Concerns about the Evidence-Based Guideline Approach in Conservation Management: Hints from Medical Practice

The importance of using evidence in decision-making is frequently highlighted in policy reports and scientific papers. However, subjective judgments of the reliability of environmental evidence vary widely, and large-scale systematic searches for evidence are only common for climate-related topics. In the medical field, evidence-based guidelines are routinely used to guide treatments. In the management of multiple-use landscapes similar guidelines could substantially narrow the science-practice gap but are largely absent. The challenges potential guidelines face are therefore unknown. For the case of forest conservation, we conducted 14 semistructured interviews with mainly forest practitioners and presented them an example medical guideline together with evidence-based statements on forest conservation (hereinafter: statement paper). We identified 28 concerns related to potential evidence-based guidelines in forest conservation. The interviews yielded approximately three major findings. First, recommendations on forest conservation are better accepted if they include clear instructions and are formulated for a specific context. Fragmentary conservation evidence complicates the formulation of specific recommendations. Second, the level of evidence framework, which indicates the strength of the available evidence, is perceived as too complex. Third, neglecting forest multifunctionality in a potential guideline hampers its application but, if addressed, potentially weakens its ecological relevance. We show that major concerns about potential evidence-based conservation guidelines are similar to the challenges experienced by medical guidelines. We also identify concerns unique to forestry.

Fast fully automatic skin lesions segmentation probabilistic with Parzen window

Cutaneous melanoma accounts for over 90% of all melanoma, causing up to 55,500 annual deaths. However, it is a potentially curable type of cancer. Since melanoma is potentially curable, the disease's mortality rate is directly linked to late detection. This work proposes an approach that presents the balance between time and efficiency. This paper proposes the method of fast and automatic segmentation of skin lesions using probabilistic characteristics with the Parzen window (SPPW). The results obtained by the method were based on PH2 and ISIC datasets. The SPPW approach reached the following averages between the two datasets Specificity of 98.55%, Accuracy of 95.48%, Dice of 91.12%, Sensitivity of 88.45%, Mattheus of 87.86%, and Jaccard Index of 84.90%. The highlights of the proposed method are its short average segmentation time per image, and its metrics values, which are often higher than the ones obtained by other methods. Therefore, the SPPW method of segmentation is a quick, viable, and easily accessible option to aid in the diagnosis of diseased skin.

The evolving field of Dermato-oncology and the role of dermatologists: Position Paper of the EADO, EADV and Task Forces, EDF, IDS, EBDV-UEMS and EORTC Cutaneous Lymphoma Task Force

BACKGROUND

The incidence of skin cancers has been increasing steadily over the last decades. Although there have been significant breakthroughs in the management of skin cancers with the introduction of novel diagnostic tools and innovative therapies, skin cancer mortality, morbidity and costs heavily burden the society.

OBJECTIVE

Members of the European Association of Dermato-Oncology, European Academy of Dermatology and Venereology, International Dermoscopy Society, European Dermatology Forum, European Board of Dermatovenereology of the European Union of Medical Specialists and EORTC Cutaneous Lymphoma Task Force have joined this effort to emphasize the fundamental role that the specialist in Dermatology-Venereology has in the diagnosis and management of different types of skin cancer. We review the role of dermatologists in the prevention, diagnosis, treatment and follow-up of patients with melanoma, non-melanoma skin cancers and cutaneous lymphomas, and discuss approaches to optimize their involvement in effectively addressing the current needs and priorities of dermato-oncology.

DISCUSSION

Dermatologists play a crucial role in virtually all aspects of skin cancer management including the implementation of primary and secondary prevention, the formation of standardized pathways of care for patients, the establishment of specialized skin cancer treatment centres, the coordination of an efficient multidisciplinary team and the setting up of specific follow-up plans for patients.

CONCLUSION

Skin cancers represent an important health issue for modern societies. The role of dermatologists is central to improving patient care and outcomes. In view of the emerging diagnostic methods and treatments for early and advanced skin cancer, and considering the increasingly diverse skills, knowledge and expertise needed for managing this heterogeneous group of diseases, dermato-oncology should be considered as a specific subspecialty of Dermatology-Venereology.

Integrated photoacoustic and hyperspectral dual-modality microscopy for co-imaging of melanoma and cutaneous squamous cell carcinoma in vivo

Skin carcinoma such as melanoma (MM) and cutaneous squamous cell carcinoma (cSCC) are considered as the highest mortality and the most aggressive skin cancers in dermatology. In view that early diagnosis and treatment can greatly improve the survival rate and life quality of the patients, developing noninvasive and effective evaluation methods is of great significance for the detection and identification of early stage cutaneous cancers. In this article, we propose a hybrid photoacoustic and hyperspectral dual-modality microscopy to evaluate and differentiate skin carcinoma by structural and multiphysiological parameters. The proposed system's imaging abilities are verified by mimic phantoms and normal mice experiments. Furthermore, in vivo characterization and evaluation results of MM and cSCC mice are obtained successfully, which prove this novel method could be used as a reliable and useful method for skin cancer detection in early stages.

A Review of Noninvasive Techniques for Skin Cancer Detection in Dermatology

As a result of increasing melanoma incidence and challenges with clinical and histopathologic evaluation of pigmented lesions, noninvasive techniques to assist in the assessment of skin lesions are highly sought after. This review discusses the methods, benefits, and limitations of adhesive patch biopsy, electrical impedance spectroscopy (EIS), multispectral imaging, high-frequency ultrasonography (HFUS), optical coherence tomography (OCT), and reflectance confocal microscopy (RCM) in the detection of skin cancer. Adhesive patch biopsy provides improved sensitivity and specificity for the detection of melanoma without a trade-off of higher sensitivity for lower specificity seen in other diagnostic tools to aid in skin cancer detection, including EIS and multispectral imaging. EIS and multispectral imaging provide objective information based on computer-assisted diagnosis to assist in the decision to biopsy and/or excise an atypical melanocytic lesion. HFUS may be useful for the determination of skin tumor depth and identification of surgical borders, although further studies are necessary to determine its accuracy in the detection of skin cancer. OCT and RCM provide enhanced resolution of skin tissue and have been applied for improved accuracy in skin cancer diagnosis, as well as monitoring the response of nonsurgical treatments of skin cancers and the determination of tumor margins and recurrences. These novel approaches to skin cancer assessment offer opportunities to dermatologists, but are dependent on the individual dermatologist's comfort, knowledge, and desire to invest in training and implementation of noninvasive techniques. These noninvasive modalities may have a role in the complementary assessment of skin cancers, although histopathologic diagnosis remains the gold standard for the evaluation of skin cancer.

Melanoma diagnosed on digital dermoscopy monitoring: A side-by-side image comparison is needed to improve early detection

BACKGROUND

Digital dermoscopy monitoring (DDM) helps to recognize melanomas lacking specific dermoscopic features at baseline, but the number of melanomas eventually developing specific features is still unknown.

OBJECTIVE

To assess how many melanomas are identified because they develop melanoma-specific criteria over time compared with melanomas recognized by side-by-side image comparison.

METHODS

A case-control study was conducted collecting 206 melanomas: 103 melanomas diagnosed during DDM follow-up and 103 melanomas diagnosed at baseline. The control group was composed of 309 benign lesions consisting of 103 nevi excised for diagnostic reasons, 103 not excised nevi, and 103 not excised seborrheic keratoses. Dermoscopic images of all 515 lesions were randomly presented to 2 blinded experts to give a diagnosis and to score the criteria of the 7-point checklist.

RESULTS

Of the 103 melanomas diagnosed at baseline, 78.6% (n = 81) were correctly identified compared with only 40.8% (n = 42) of melanomas diagnosed after DDM (P < .001). Of the 103 melanomas excised after DDM, 59.2% (n = 61), did not develop melanoma-specific criteria and were identified only because of the side-by-side image comparison.

LIMITATIONS

The type of morphologic changes considered as suspicious on DDM was not assessed.

CONCLUSIONS

Most melanomas are diagnosed with DDM by side-by-side image comparison.

Real-Time Mobile-Phone-Aided Melanoma Skin Lesion Detection Using Triangulation Technique

Melanoma is a harmful disease among all types of skin cancer. Genetic factors and the exposure of UV rays causes melanoma skin lesions. Early diagnosis is important to identify malignant melanomas to improve the patient prognosis. A biopsy is a traditional method which is painful and invasive when used for skin cancer detection. This method requires laboratory testing which is not very efficient and time-consuming to detect skin lesions. To solve the above issue, a computer aided diagnosis (CAD) for skin lesion detection is needed. In this article, we have developed a mobile application with the capabilities to segment skin lesions in dermoscopy images using a triangulation method and categorize them into malignant or bengin lesions through a supervised method which is convolution neural network (CNN). This mobile application will make the skin cancer detection non-invasive which does not require any laboratory testing, making the detection less time consuming and inexpensive with a detection accuracy of 81%.

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

Reliable test of clinicians' mastery in skin cancer diagnostics

Differentiating between benign and malignant skin lesions can be very difficult and should only be done by sufficiently trained and skilled clinicians. To our knowledge there are no validated tests for reliable assessments of clinicians' ability to perform skin cancer diagnostics. To develop and gather validity evidence for a test in skin cancer diagnostics, a multiple-choice questionnaire (MCQ) was developed based on informal interviews with seven content experts from five skin cancer centers in Denmark. Validity evidence for the test was gathered from May until July 2019 using Messick's validity framework (content, response process, internal structure, relationship to other variables and consequences). Item content was revised through a Delphi-like review process and then piloted on 36 medical students and 136 doctors using a standardized response process. Results enabled an analysis of the internal structure and relationship to other variables of the test. Finally, the contrasting groups method was used to investigate the test's consequences (pass-fail standard). The initial 90-item MCQ was reduced to 40 items during the Delphi-like review process. Item analysis revealed that 25 of the 40 selected items were level I-III quality items with a high internal consistency (Cronbach's α = 0.83) and highly significant (P ≤ 0.0001) differences in test scores between participants with different occupations or levels of experience. A pass-fail standard of 12 (48%) correct answers was established using the contrasting groups' method. The skin cancer diagnostics MCQ developed in this study can be used for reliable assessments of clinicians' competencies.

Reflectance confocal microscopy: Principles, basic terminology, clinical indications, limitations, and practical considerations

Reflectance confocal microscopy (RCM) is a noninvasive imaging tool used for in vivo visualization of the skin. It has been extensively studied for use in the evaluation of equivocal cutaneous neoplasms to decrease the number of biopsy procedures in patients with benign lesions. Furthermore, its applications are broadening to include presurgical cancer margin mapping, tumor recurrence surveillance, monitoring of ablative and noninvasive therapies, and stratification of inflammatory disorders. With the approval of category I Current Procedural Terminology reimbursement codes for RCM image acquisition and interpretation, use of this technology has been increasingly adopted by dermatologists. The first article in this 2-part continuing medical education series highlights basic terminology, principles, clinical applications, limitations, and practical considerations in the clinical use of RCM technology.

Heterogeneity in the linear shiny white structures in melanomas seen with polarized light according to histopathological association: Cross-sectional observational study in 118 cutaneous melanomas

Polarized dermoscopy enables visualization of linear shiny white structures in melanomas, thought to be due to the existence of fibrosis in the dermis. Our objective was to establish the existence of two types of linear shiny white structures and assess their association with different histological structures. We performed a cross-sectional study including all non-acral, non-facial melanomas from our hospital with linear shiny white structures. The outcome variable was the type of linear shiny white structures: shiny white streaks and white strands. We evaluated their association with explanatory variables that may affect the reflectance of melanomas and Breslow index. We used χ² statistics and also calculated the sensitivity and specificity of each linear shiny white structure to predict those variables. We detected linear shiny white structures in 118 melanomas. Regarding shiny white streaks, we only found a statistically significant positive relationship with fibrosis in the papillary dermis. Regarding white strands, we found statistically significant and positive relationships with hyperkeratosis, Breslow index of 0.8 mm or more and acanthosis. Sensitivity and specificity study revealed that the presence of shiny white streaks was the most sensitive (81.7%) and specific (72.3%) for fibrosis in the papillary dermis, and presence of white strands was the most sensitive (91.1%) and specific (85.7%) for hyperkeratosis.

Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions

BACKGROUND

Convolutional neural networks (CNNs) have shown a dermatologist-level performance in the classification of skin lesions. We aimed to deliver a head-to-head comparison of a conventional image analyser (CIA), which depends on segmentation and weighting of handcrafted features, to a CNN trained by deep learning.

METHODS

Cross-sectional study using a real-world, prospectively acquired, dermoscopic dataset of 1981 skin lesions to compare the diagnostic performance of a market-approved CNN (Moleanalyzer-Pro™, developed in 2018) to a CIA (Moleanalyzer-3™/Dynamole™; developed in 2004, all FotoFinder Systems Inc, Germany). As a reference standard, we used histopathological diagnoses (n = 785) or, in non-excised benign lesions (n = 1196), expert consensus plus an uneventful follow-up by sequential digital dermoscopy for at least 2 years.

RESULTS

A total of 281 malignant lesions and 1700 benign lesions from 435 patients (62.2% male, mean age: 52 years) were prospectively imaged. The CNN showed a sensitivity of 77.6% (95% confidence interval [CI]: [72.4%-82.1%]), specificity of 95.3% (95% CI: [94.2%-96.2%]), and receiver operating characteristic (ROC)-area under the curve (AUC) of 0.945 (95% CI: [0.930-0.961]). In contrast, the CIA achieved a sensitivity of 53.4% (95% CI: [47.5%-59.1%]), specificity of 86.6% (95% CI: [84.9%-88.1%]) and ROC-AUC of 0.738 (95% CI: [0.701-0.774]). The data set included melanomas originally diagnosed by dynamic changes during sequential digital dermoscopy (52 of 201, 20.6%), which reduced the sensitivities of both classifiers. Pairwise comparisons of sensitivities, specificities, and ROC-AUCs indicated a clear outperformance by the CNN (all p < 0.001).

CONCLUSIONS

The superior diagnostic performance of the CNN argues against a continued application of former CIAs as an aide to physicians' clinical management decisions.

The presence of eccentric hyperpigmentation should raise the suspicion of melanoma

BACKGROUND

Melanocytic lesions with eccentric hyperpigmentation (EH), even though without other dermatoscopic features of melanoma, are often excised.

OBJECTIVE

Aiming to understand whether the EH in a pigmented lesion is an accurate criterion of malignancy, we evaluated the capability of two evaluators, with different expertise, to correctly diagnose a melanoma when analysing a given lesion in toto versus a partial analysis, with only the EH or the non-hyperpigmented portion (non-EH) visible.

METHODS

Dermatoscopic images of 240 lesions (107 melanomas and 133 nevi) typified by EH were selected. Facial, acral, mucosal lesions and lesions showing clear-cut features of melanoma (except for atypical network) were excluded. Clinical and dermoscopic features (main pattern and numbers of colours) were described for all cases. Each image was split in two through a software so that only the EH or the non-EH was visible. Two blinded evaluators examined three sets of images, two with customized images and one with the non-modified ones: they were asked to give a dichotomous diagnosis (melanoma or nevus) for each image.

RESULTS

Melanomas were significantly more frequently typified by colour variegation (3 colours in 44.8% and 4 colours in 41.1% of cases) and atypical network (88.1% in the EH). No significant differences in diagnostic accuracy emerged between the two evaluators. Sensitivity improved in the evaluation of the whole lesions (mean sensitivity 89.7%) in comparison with the evaluation of EH or non-EH alone (72.7-62.6%). Specificity increased when evaluating the EH (54.1%). Positive predictive value (PPV) and likelihood ratio (LR+) of EH resulted 52.3% and 1.4, meaning that in one case out of two with EH is a melanoma.

CONCLUSIONS

Lesions with EH are challenging, regardless of dermoscopic experience. The EH is a robust criterion for malignancy, since the evaluation of the whole lesion, through an intralesional comparative approach, increases sensitivity.

Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation

The early and accurate diagnosis of skin cancer is crucial for providing patients with advanced treatment by focusing medical personnel on specific parts of the skin. Networks based on encoder–decoder architectures have been effectively implemented for numerous computer-vision applications. U-Net, one of CNN architectures based on the encoder–decoder network, has achieved successful performance for skin-lesion segmentation. However, this network has several drawbacks caused by its upsampling method and activation function. In this paper, a fully convolutional network and its architecture are proposed with a modified U-Net, in which a bilinear interpolation method is used for upsampling with a block of convolution layers followed by parametric rectified linear-unit non-linearity. To avoid overfitting, a dropout is applied after each convolution block. The results demonstrate that our recommended technique achieves state-of-the-art performance for skin-lesion segmentation with 94% pixel accuracy and a 88% dice coefficient, respectively.

Skin Lesion Classification With Deep Convolutional Neural Network: Process Development and Validation

Background

Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole.

Objective

We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners.

Methods

We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model.

Results

We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall.

Conclusions

Based on our research, we conclude that deep learning algorithms are highly suitable for classifying skin cancer images.

Incorporation of dermoscopy improves inter-observer agreement among dermatopathologists in histologic assessment of melanocytic neoplasms

Histopathologic assessment of melanocytic neoplasms is the current gold standard of diagnosis. However, there are well recognized limitations including inter-observer diagnostic discordance. This study aimed to determine if integrating dermoscopy with histopathology of melanocytic neoplasms impacts diagnosis and improves inter-observer agreement. We conducted a prospective cohort study in a pigmented lesion clinic. Consecutive melanocytic lesions were identified for biopsy based on atypical gross or dermoscopic features. Standardized immunohistochemistry and levels were ordered on each specimen. The cases were randomized. Three dermatopathologists blinded to the clinical impression assessed each lesion. The cases were then re-randomized and re-assessed with addition of gross clinical and dermoscopic images. Inter-rater reliability (IRR) using Fleiss' kappa statistic revealed an increase from 0.447 without to 0.496 with dermoscopy amongst all dermatopathologists. The kappa increased from 0.495 before to 0.511 with dermoscopy in separating high-grade atypia or melanoma from moderate atypia or less. In 16 of 136 cases, at least 2 of 3 dermatopathologists favored a diagnosis of melanoma only after dermoscopy. In total, the consensus grade of atypia changed in 24.3% (33/ 136) of cases thereby representing changes to excisional margins and patient follow up. This study is limited by the cohort size. Dermoscopy significantly impacts diagnosis and improves identification of early melanomas in high risk populations and improves inter-observer agreement.

Teledermatology: idea, benefits and risks of modern age - a systematic review based on melanoma

Telemedicine may be described as a modern technology supporting health care at a distance. Dermatology, as a visually-dependent specialty, is particularly suited for this kind of the health care model. This has been proven in a number of recent studies, which emphasized feasibility and reliability of teledermatology. Many patients in the world still do not have access to appropriate dermatological care, while skin cancers morbidity is on an upward trend. Technological development has enabled clinicians to care for diverse patient populations in need of skin expertise without increasing their overhead costs. Teledermatology has been used for various purposes: health care workers can use this technology to provide clinical services to patients, to monitor patient health, to consult with other health care providers and to provide patients with access to educational resources. It seems that teledermatology might be the answer to numerous issues concerning diagnosing, screening and managing cancers as well as pigmented skin lesions.

Teledermoscopy for Skin Cancer Prevention: a Comparative Study of Clinical and Teledermoscopic Diagnosis

Introduction

The number of newly diagnosed skin cancers per year is greater than the sum of the four most common cancers: breast, prostate, lung, and colon. The implementation of primary and secondary prevention measures, over the last 2 to 3 decades, has made a major contribution to successful treatment.

Aim

Evaluate the accuracy and reliability of teledermoscopic versus clinical diagnosis for skin cancers when diagnostic algorithms are used, and when GPs and surgical specialties are involved in the clinical procedure.

Methods

Digital dermoscope (TS-DD, by Teleskin company) was used for the acquisition of teledermoscopic photographs and specialized teledermoscopic software was used for clinical examination and teledermoscopic consultation. The teledermoscopic procedure itself was performed in two steps. The first step was a clinical examination using the ABCDE rule with digital dermoscopic photography of the suspected lesion. The second step was a 2-step dermoscopic evaluation using the second step ABCD algorithm for the second step. Accuracy and diagnostic reliability were calculated for: teledermoscopic diagnosis versus histopathological diagnosis; clinical diagnosis versus histopathological diagnosis and teledermoscopic diagnosis versus clinical diagnosis.

Results

The study included 120 patients with 121 Pigmented Skin Lesions, of which 75 (62%) were benign and 46 (38%) were malignant lesions (6 melanomas and 40 NonMelanoma Skin Cancers). Diagnostic accuracy between teledermoscopic and histopathologic diagnosis was 90.91% and reliability k=0.81; between clinical and histopathological diagnosis the diagnostic accuracy was 82.64% and the reliability k=0.64 and between the clinical and teledermoscopic diagnosis the diagnostic accuracy was 81.82% and the reliability k=0.62.

Conclusion

The achieved diagnostic accuracy between clinical and teledermoscopic diagnosis, when using diagnostic algorithms, establishes a feasible screening path for skin cancers and indicates that general practitioners and specialized surgeons may equally be involved in prevention.

The Role of Teledermatology and Teledermoscopy in the Diagnosis of Actinic Keratosis and Field Cancerization

Actinic keratosis (AK) and field cancerization are increasing health problems insufficiently diagnosed by primary care physicians. The objective of this study was to assess the validity and reliability of teledermatology (TD) and teledermoscopy in the diagnosis of AK and field cancerization in a gatekeeper healthcare model. A prospective diagnostic test evaluation was done to assess the diagnostic concordance, accuracy, and performance parameters and the interobserver and intraobserver concordances of TD and teledermoscopy compared with dermatologists' face-to-face evaluation or histopathology. A total of 636 patients with 1,000 keratotic skin lesions were included. TD diagnostic concordance for AK and field cancerization evaluation was very high and superior to primary care physicians' diagnosis (92.4% vs. 62.4% and 96.7% vs. 51.8%, P < 0.001). TD sensitivity, specificity, and positive and negative predictive values for AK diagnosis and field cancerization were high (range = 82.2-95.0) and better than primary care physicians' diagnosis. Teledermoscopy yielded better results in diagnostic concordance, performance parameters, and AK subtypes. Intraobserver and interobserver agreement was >0.83. TD and, to a greater extent, teledermoscopy may be valid and reliable tools for the diagnosis of AK and field cancerization and may improve diagnosis and correct allocation and management in gatekeeper healthcare systems. It can be an alternative tool to training primary care physicians in direct diagnosis of these lesions.

Augmented decision-making for acral lentiginous melanoma detection using deep convolutional neural networks

BACKGROUND

Several studies have achieved high-level performance of melanoma detection using convolutional neural networks (CNNs). However, few have described the extent to which the implementation of CNNs improves the diagnostic performance of the physicians.

OBJECTIVE

This study is aimed at developing a CNN for detecting acral lentiginous melanoma (ALM) and investigating whether its implementation can improve the initial decision for ALM detection made by the physicians.

METHODS

A CNN was trained using 1072 dermoscopic images of acral benign nevi, ALM and intermediate tumours. To investigate whether the implementation of CNN can improve the initial decision for ALM detection, 60 physicians completed a three-stage survey. In Stage I, they were asked for their decisions solely on the basis of dermoscopic images provided to them. In Stage II, they were also provided with clinical information. In Stage III, they were provided with the additional diagnosis and probability predicted by the CNN.

RESULTS

The accuracy of ALM detection in the participants was 74.7% (95% confidence interval [CI], 72.6-76.8%) in Stage I and 79.0% (95% CI, 76.7-81.2%) in Stage II. In Stage III, it was 86.9% (95% CI, 85.3-88.4%), which exceeds the accuracy delivered in Stage I by 12.2%p (95% CI, 10.1-14.3%p) and Stage II by 7.9%p (95% CI, 6.0-9.9%p). Moreover, the concordance between the participants considerably increased (Fleiss-κ of 0.436 [95% CI, 0.437-0.573] in Stage I, 0.506 [95% CI, 0.621-0.749] in Stage II and 0.684 [95% CI, 0.621-0.749] in Stage III).

CONCLUSIONS

Augmented decision-making improved the performance of and concordance between the clinical decisions of a diverse group of experts. This study demonstrates the potential use of CNNs as an adjoining, decision-supporting system for physicians' decisions.

Differentiation of combined nevi and melanomas: Case-control study with comparative analysis of dermoscopic features

BACKGROUND AND OBJECTIVES

Combined nevi (CN) show two or more components of major nevus subtypes and simulate melanomas. We investigated a panel of dermoscopic features and three dermoscopic algorithms for differentiating CN from melanomas.

PATIENTS AND METHODS

Retrospective, blinded case-control study using dermoscopic images of 36 CN and 36 melanoma controls. Twenty-one dermoscopic features validated for the diagnosis of melanocytic lesions, the number of colors, and three dermoscopic algorithms were investigated (ABCD rule of dermoscopy, Menzies scoring method, 7-point checklist).

RESULTS

Five of seven features indicative of nevi were observed significantly more frequently in CN than in melanomas (all p < 0.05) and two were exclusively found in CN. Eleven out of 14 features indicative of melanomas were observed significantly more frequently in melanomas than in CN (all p < 0.03) and five were exclusively found in melanomas. The mean (± SD) number of colors in CN was lower than in melanomas (2.1 ± 0.6 versus 3.4 ± 0.7; p < 0.001). Among tested algorithms the ABCD rule of dermoscopy performed best (sensitivity 91.7 %, specificity 77.8 %).

CONCLUSIONS

The ABCD rule of dermoscopy differentiated CN from melanomas most efficiently. Additional knowledge of dermoscopic features to be expected exclusively in either CN or melanomas should help dermatologists to make a correct clinical diagnosis.

Meta-analysis of number needed to treat for diagnosis of melanoma by clinical setting

OBJECTIVE

To provide a formal statistical comparison of the efficacy of melanoma detection among different clinical settings.

METHODS

A systematic review and meta-analysis of all relevant observational studies on number needed to treat (NNT) in relation to melanoma was performed in MEDLINE. We performed a random-effects model meta-analysis and reported NNTs with 95% confidence intervals (CIs). The subgroup analysis was related to clinical setting.

RESULTS

In all, 29 articles including a total of 398,549 biopsies/excisions were analyzed. The overall NNT was 9.71 (95% CI, 7.72-12.29): 22.62 (95% CI, 12.95-40.10) for primary care, 9.60 (95% CI, 6.97-13.41) for dermatology, and 5.85 (95% CI, 4.24-8.27) for pigmented lesion specialists.

LIMITATIONS

There is heterogeneity in data reporting and the possibility of missing studies. In addition, the incidence of melanoma varies among clinical settings, which could affect NNT calculations.

CONCLUSION

Pigmented lesion specialists have the lowest NNT, followed by dermatologists, suggesting that involving specialists in the diagnosis and treatment of pigmented skin lesions can likely improve patient outcomes.