Dermoscopy and reflectance confocal microscopy-augmented characterization of pigmented micro-basal cell carcinoma (less than 2 mm diameter).

BACKGROUND: Basal cell carcinoma (BCC) is the most common skin cancer, accounting for approximately 80% of nonmelanoma skin cancer diagnoses each year. Among other factors, the staging of BCC is influenced by its measured diameter. Stage 1 BCC is defined as a lesion measuring 2 cm across or less. Of note, there have been increasing publications reporting features of "small-sized" BCCs, which can present smaller than 1 mm. However, few of these studies have characterized features of pigmented small-sized BCC. The application of in-vivo imaging such as dermoscopy and reflectance confocal microscopy (RCM) allows for the non-invasive distinction of these lesions from benign and malignant melanocytic neoplasms, thereby reducing unnecessary biopsies. METHODS: Within one year, three patients presented to Oregon Health and Science University's dermatology clinic with pigmented lesions of concern measuring less than 2 mm that were histologically confirmed as pigmented BCC. We sought to characterize the features of these lesions in a case series with the non-invasive imaging modalities of dermoscopy and RCM. RESULTS: All cases presented clinically as a small, brown, macule on the face. Each of the three cases exhibited differing features on dermoscopy. With the application of RCM, we were able to visualize characteristic BCC features, prompting removal by shave biopsy. CONCLUSION: To our knowledge, no other study has reported dermoscopic and RCM features of a cohort of pigmented BCCs 2 mm in diameter or smaller. We propose to define BCCs of this size as micro-BCCs. The variability of dermoscopic findings observed in our study, combined with the small size of these pigmented lesions, shows the utility of RCM as a non-invasive diagnostic tool for pigmented micro-BCCs.

Dermoscopic features associated with 3-GEP PLA: LINC00518, PRAME, and TERT expression in suspicious pigmented lesions.

Utilization of dermoscopy and novel molecular triage technologies augments visual triage of pigmented skin lesions, promoting early detection of melanoma. One emerging in vivo genomic test, 3-GEP pigmented lesion assay (3-GEP PLA) aids in pigmented lesion triage by noninvasively detecting the presence of three genes associated with melanoma: LINC00518, PRAME, and TERT. The purpose of our retrospective case-control study was to identify dermoscopic features uniquely associated with the presence of LINC00518, PRAME, or TERT in the stratum corneum as determined by 3-GEP PLA testing. Images of suspicious pigmented lesions that had undergone 3-GEP PLA testing and received a definitive positive or negative result (n = 393) were evaluated for the presence of specific clinical and dermoscopic features associated with melanoma. We found that asymmetry of color was a significant predictor for PRAME expression (Odds Ratio (OR) 5.5, 95% Confidence Interval (CI) 1.6-34.5, p = 0.004), blue color and negative pigment network were significant predictors for LINC00518 expression (adjusted OR 2.7, 95% CI 1.2-5.5, p = 0.014 and adjusted OR 5.4, 95% CI 1.6-16.9, p = 0.010, respectively), and atypical polymorphous vessels present in a pigmented skin lesion were a significant predictor for TERT promoter mutations (OR 5.8, 95% CI 1.3-23.4, p = 0.022). The results presented suggest a hierarchy in the significance of these dermoscopic features and may help guide evaluation and management of pigmented skin lesions.

Clinical Utility of Dermoscopy and 31-Gene Expression Profiling by Dermatology Providers in Melanoma Management Care.

OBJECTIVE: A 31-gene expression profile (31-GEP) test that predicts metastatic risk in patients with cutaneous malignant melanoma (CMM) has previously been validated and is available for clinical use. The test dichotomizes patients into lower risk and higher risk groups based on differences that correspond to unique genetic expression patterns. Although the impact of such a test on dermatology providers' clinical decision-making has been studied, little is known about whether there exists an association between certain clinical features, such as dermoscopy, and 31-GEP results. METHODS: In this retrospective analysis of 31-GEP test results ordered by dermatologists, we evaluated the frequency of dermoscopic features, using a modified dermoscopy three-point checklist, in 17 cases (n=17) and compared these findings to other key clinicopathologic features including tumor thickness, ulceration, and mitotic rate to 31-GEP results. Additionally, we evaluated the dermatologist's perspective and incorporation of GEP testing as part of patient discussion on melanoma management. RESULTS: 31-GEP stratified patients into 4 groups; groups 1A and 1B are considered low risk of metastasis or recurrence, while 2A and 2B are considered high risk. Of the 17 cases, we had fifteen group 1A (88.23%), one 1B (5.88%), and one 2B (5.88%) result. Overall frequency of dermoscopic features is as follows; 100% of lesions presented with asymmetry, 47% with round structures, and 70.6% with blue-white color. The average time providers spent explaining and ordering the test was 15 minutes, with a range of 10 to 20 minutes. CONCLUSIONS: This study represents our experience and understanding of the dermatologist’s role ordering 31-GEP in the care pathway of melanoma patients and we recommend that dermatology providers consider ordering the test for newly diagnosed CMM patients. J Drugs Dermatol. 2022;21(12): doi:10.36849/JDD.6889.

New telemedicine techniques in dermatology - evaluation with reflectance confocal microscopy via cloud-based platform.

Dermoscopically equivocal skin lesions may present a diagnostic challenge in daily clinical practice and are regularly sent for second expert opinion. We present a new approach to handling these cases in a consultation referral system that enables communication between the initial doctor at the image upload site and dermatology experts at a distance via cloud-based telemedicine. In our study we retrospectively evaluated 100 equivocal cases with complete digital dermoscopy-reflectance confocal microscopy image sets and compared suggested management of the initial doctor to a second expert confocal reader. We evaluated the effect of reader concordance on final management of these lesions resulting in a single reader overall sensitivity of 89% and specificity of 66% and double reader concordance method sensitivity of 98% and specificity of 54%. In conclusion, we found that application of double reader evaluation of these image sets with automatic referral of lesions for removal in the case of discordant diagnosis between two doctors improved the sensitivity of diagnosis in this subset of lesions and may increase the safety threshold of management choice reducing potential misdiagnosis in telemedicine settings. This paper concerns the application of telemedicine in practical medicine.

Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review.

BACKGROUND: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. METHODS: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. RESULTS: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. CONCLUSION: AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.

A clinical impact study of dermatologists' use of diagnostic gene expression profile testing to guide patient management.

Aim: Cutaneous melanocytic neoplasms with diagnostic and/or clinical ambiguity pose patient management challenges. Methods: Six randomized case scenarios with diagnostic/clinical uncertainty were described with/without a benign or malignant diagnostic gene expression profile (GEP) result. Results: Clinical impact was assessed by reporting the mean increase/decrease of management changes normalized to baseline (n = 32 dermatologists). Benign GEP results prompted clinicians to decrease surgical margins (84.2%). Malignant GEP results escalated surgical excision recommendations (100%). A majority (72.2%) reduced and nearly all (98.9%) increased follow-up frequency for benign or malignant GEP results, respectively. There was an overall increase in management plan confidence with GEP results. Conclusion: Diagnostic GEP tests help guide clinical decision-making in a variety of diagnostically ambiguous or clinicopathologically discordant scenarios.

Dermatologists' use of diagnostic gene expression profiles for personalized patient care. When your doctor takes a piece of a mole, that mole is looked at under the microscope by a pathologist. The pathologist is responsible for figuring out if the mole is dangerous or not. Dangerous moles are removed with surgery to make sure all the dangerous tissue is gone. Moles without a health threat are left alone. Sometimes figuring out how dangerous a mole is is difficult. The pathologist may not provide the doctor with enough information for them to know how to treat your mole. There is a test that can provide information on whether your mole is unsafe. This test is called diagnostic gene expression profiling or GEP. In this study, GEP is used to help doctors figure out how to treat a mole and how often the patient should be seen in the office for skin checks. With GEP, important changes in patient treatment were identified. These include the need for an additional surgery, how much healthy tissue should be removed during surgery and how often the patient should be seen in the office. For suspicious moles where the pathology report is unclear, GEP can provide information that leads to more appropriate and personalized patient care.

Ancillary diagnostic gene expression profile testing for ambiguous cutaneous melanocytic lesions helps optimize dermatologist recommendations for excision margin and follow-up.

The role of mobile teledermoscopy in skin cancer triage and management during the COVID-19 pandemic.

The unprecedented onset of the COVID-19 crisis poses a significant challenge to all fields of medicine, including dermatology. Since the start of the coronavirus outbreak, a stark decline in new skin cancer diagnoses has been reported by countries worldwide. One of the greatest challenges during the pandemic has been the reduced access to face-to-face dermatologic evaluation and non-urgent procedures, such as biopsies or surgical excisions. Teledermatology is a well-integrated alternative when face-to-face dermatological assistance is not available. Teledermoscopy, an extension of teledermatology, comprises consulting dermoscopic images to improve the remote assessment of pigmented and non-pigmented lesions when direct visualisation of lesions is difficult. One of teledermoscopy's greatest strengths may be its utility as a triage and monitoring tool, which is critical in the early detection of skin cancer, as it can reduce the number of unnecessary referrals, wait times, and the cost of providing and receiving dermatological care. Mobile teledermoscopy may act as a communication tool between medical practitioners and patients. By using their smartphone (mobile phone) patients can monitor a suspicious skin lesion identified by their medical practitioner, or alternatively self-detect concerning lesions and forward valuable dermoscopic images for remote medical evaluation. Several mobile applications that allow users to photograph suspicious lesions with their smartphones and have them evaluated using artificial intelligence technology have recently emerged. With the growing popularity of mobile apps and consumer-involved healthcare, this will likely be a key component of skin cancer screening in the years to come. However, most of these applications apply artificial intelligence technology to assess clinical images rather than dermoscopic images, which may lead to lower diagnostic accuracy. Incorporating the direct-to-consumer mobile dermoscopy model in combination with mole-scanning artificial intelligence as a mobile app may be the future of skin cancer detection.

Sequential Dermoscopy and Reflectance Confocal Microscopy Paired With Pigmented Lesion Assay Gene Expression Profiling for In Vivo Monitoring of Multiple Halo Nevi on an Adult Patient.

The halo nevus is characterized by a ring of depigmentation appearing around an acquired or congenital melanocytic nevus. When observed in children, halo nevi are generally not a cause of concern. However, adult-onset halo nevi have an associated risk of primary cutaneous melanoma that corresponds to the risk of melanoma in patients with atypical nevi or a personal/familial history of melanoma. Thus, new-onset halo nevi in adults requires close follow-up and monitoring for malignancy. Herein we present a case of an adult patient who received sequential digital dermoscopy, reflectance confocal microscopy, and pigmented lesion assay gene expression profiling to monitor two halo nevi over a three-month period.

Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review.

BACKGROUND: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. OBJECTIVE: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. METHODS: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. RESULTS: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. CONCLUSIONS: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.

A Physician's Guide to the Use of Gene Expression Profile Ancillary Diagnostic Testing for Cutaneous Melanocytic Neoplasms.

Objectives: Some melanocytic neoplasms suspicious for melanoma require additional workup to arrive at a final diagnosis. Within the last eight years, gene expression profiling (GEP) has become an important ancillary tool to aid in the diagnosis of melanocytic neoplasms with uncertain malignant potential. As the usage of two commercially available tests (23-GEP and 35-GEP) evolves, it is important to answer key questions about optimal utilization and their impact on patient care. Methods: Recent and relevant articles answering the following questions were included in the review. First, how do dermatopathologists synthesize the available literature, the latest guidelines, and their clinical experience to determine which cases would be most likely to benefit from GEP testing? Second, how best can a dermatologist convey to their dermatopathologist that the use of GEP in the diagnostic process could provide a more clearly defined result and thereby help empower the dermatologist to provide higher-quality patient care when making specific patient management decisions for otherwise pathologically ambiguous lesions? Results: When interpreted in the context of the clinical, pathologic, and laboratory information, GEP results can facilitate the rendering of timely, accurate, and definitive diagnoses for melanocytic lesions with otherwise uncertain malignant potential to inform personalized treatment and management plans. Limitations: This was a narrative review focused on clinical use of GEP compared to other ancillary diagnostic tests performed postbiopsy. Conclusion: Open communication between dermatopathologists and dermatologists, especially regarding GEP testing, can be a vital component to achieve appropriate clinicopathologic correlation for otherwise ambiguous melanocytic lesions.

Expert Consensus Statement on Proficiency Standards for Dermoscopy Education in Primary Care.

BACKGROUND: Primary care providers (PCPs) frequently address dermatologic concerns and perform skin examinations during clinical encounters. For PCPs who evaluate concerning skin lesions, dermoscopy (a noninvasive skin visualization technique) has been shown to increase the sensitivity for skin cancer diagnosis compared with unassisted clinical examinations. Because no formal consensus existed on the fundamental knowledge and skills that PCPs should have with respect to dermoscopy for skin cancer detection, the objective of this study was to develop an expert consensus statement on proficiency standards for PCPs learning or using dermoscopy. METHODS: A 2-phase modified Delphi method was used to develop 2 proficiency standards. In the study's first phase, a focus group of PCPs and dermatologists generated a list of dermoscopic diagnoses and associated features. In the second phase, a larger panel evaluated the proposed list and determined whether each diagnosis was reflective of a foundational or intermediate proficiency or neither. RESULTS: Of the 35 initial panelists, 5 PCPs were lost to follow-up or withdrew; 30 completed the fifth and last round. The final consensus-based list contained 39 dermoscopic diagnoses and associated features. CONCLUSIONS: This consensus statement will inform the development of PCP-targeted dermoscopy training initiatives designed to support early cancer detection.

Potential Limitations in the Clinical Adoption of 3-GEP Pigmented Lesion Assay for Melanoma Triage by Dermatologists and Advanced Practice Practitioners.

Background A pigmented lesion assay (PLA) is used to non-invasively detect the presence of three genes associated with melanoma (LINC00518, PRAME, and TERT) using adhesive patch testing and has the potential to reduce unnecessary biopsies. However, few studies have evaluated the clinical applicability of PLA testing and its potential limitations in real-world practice. We aim to identify possible barriers that inhibit the clinical utility of PLA testing by dermatologists. Methods Retrospective case-control study analyzing the PLA testing by two pigmented-lesion specialists that underwent PLA testing as part of clinical management. Data was collected from April 2021 to April 2022 from an academic tertiary-level center. Results The total cohort consists of 472 lesions. Genetic analysis failure for LINC00518 and PRAME occurred in 12.5% of cases and in 70.9% of cases for TERT. In 38.5% of cases, PLA results were discrepant with histopathology. The additional time associated with PLA use independent from the patient's visit was 15 min on average. Conclusion The high proportion of non-actionable results and discrepant cases highlights potential barriers to the widespread adoption of PLA testing. The high proportion of genetic analysis failure seen for TERT and limited influence on the proposed risk suggests TERT does not offer significant clinical value.

Case Report: Chilblains-like lesions (COVID-19 toes) during the pandemic - is there a diagnostic window?

The COVID-19 outbreak caused by the novel coronavirus, SARS-CoV-2, typically presents with symptoms including fever, cough, headache, myalgia, asthenia, anosmia, diarrhea, and sometimes pneumonia, which can be fatal.  Recently, new dermatologic findings have been described in association with the disease that can potentially be a distinguishing feature of infection. One such feature resembles chilblains and this case report represents a presentation of this feature with a 48-year-old female with violaceous lesions with surrounding pink erythema on her toes who tested negative for COVID-19.

Automatic Quality Assessment of Reflectance Confocal Microscopy Mosaics using Attention-Based Deep Neural Network.

Skin cancers are the most common cancers with an increased incidence, and a valid, early diagnosis may significantly reduce its morbidity and mortality. Reflectance confocal microscopy (RCM) is a relatively new, non-invasive imaging technique that allows screening lesions at a cellular resolution. However, one of the main disadvantages of the RCM is frequently occurring artifacts which makes the diagnostic process more time consuming and hard to automate using e.g. end-to-end deep learning approach. A tool to automatically determine the RCM mosaic quality could be beneficial for both the lesion classification and informing the user (dermatologist) about its quality in real-time, during the examination procedure. In this work, we propose an attention-based deep network to automatically determine if a given RCM mosaic has an acceptable quality. We achieved accuracy above 87% on the test set which may considerably improve further classification results and the RCM-based examination.

Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy.

We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both morbidity and mortality. RCM is an in-vivo non-invasive screening tool that produces virtual biopsies of skin lesions but its proficient and safe use requires hard to obtain expertise. Therefore, it may be useful to have an additional tool to aid diagnosis. The proposed network is based on the ResNet architecture. The dataset consists of 429 RCM mosaics and is divided into 3 classes: melanoma, basal cell carcinoma, and benign naevi with the ground-truth confirmed by a histopathological examination. The test set classification accuracy was 87%, higher than the accuracy achieved by medical, confocal users. The results show that the proposed classification system can be a useful tool to aid in early, noninvasive melanoma detection.