Dupilumab increases aspirin tolerance in NSAID-exacerbated respiratory disease.

BACKGROUND: Nonsteroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (N-ERD) comprises the triad of chronic rhinosinusitis with nasal polyps, asthma and intolerance to NSAIDs. Dupilumab treatment, targeting the interleukin-4 (IL-4) receptor α, significantly reduces polyp burden as well as asthma symptoms. Here we aimed to investigate the effect of dupilumab on aspirin intolerance, burden of disease and nasal cytokine profiles in patients with N-ERD. METHODS: In this open-label trial, adult patients with confirmed N-ERD were treated with dupilumab for 6 months. Clinical parameters (e.g. total polyp scores, quality of life questionnaires, smell test, spirometry), oral aspirin provocation testing and blood, nasal and urine sampling were monitored at regular intervals for up to 6 months after starting dupilumab therapy. RESULTS: Of the 31 patients included in the study, 30 completed both aspirin provocation tests. After 6 months of treatment with dupilumab, 23% of patients (n=7 of 30) developed complete aspirin tolerance and an additional 33% of patients (n=10 of 30) tolerated higher doses. Polyp burden was significantly reduced (total polyp score: -2.68±1.84, p<0.001), while pulmonary symptoms (asthma control test: +2.34±3.67, p<0.001) and olfactory performance improved (University of Pennsylvania Smell Identification Test: +11.16±9.54, p<0.001) in all patients after therapy. Patients with increased aspirin tolerance showed a significant decrease in urinary leukotriene E4 levels and their improvement in clinical parameters was associated with a reduction of eotaxin-1, C-C motif chemokine ligand 17, IL-5, IL-17A and IL-6. CONCLUSION: In this study, 57% of N-ERD patients tolerated higher doses of aspirin under dupilumab therapy.

In vivo reflectance confocal microscopy can detect the invasive component of lentigo maligna melanoma: Prospective analysis and case-control study.

BACKGROUND: Lentigo maligna (LM), a form of melanoma in situ, has no risk of causing metastasis unless dermal invasive melanoma (LMM) supervenes. Furthermore, the detection of invasion impacts prognosis and management. OBJECTIVE: To assess the accuracy of RCM for the detection of invasion component on LM/LMM lesions. METHODS: In the initial case-control study, the performance of one expert in detecting LMM at the time of initial RCM assessment of LM/LMM lesions was recorded prospectively (n = 229). The cases were assessed on RCM-histopathology correlation sessions and a panel with nine RCM features was proposed to identify LMM, which was subsequently tested in a subset of initial cohort (n = 93) in the matched case-control study by two blinded observers. Univariable and multivariable logistic regression models were performed to evaluate RCM features predictive of LMM. Reproducibility of assessment of the nine RCM features was also evaluated. RESULTS: A total of 229 LM/LMM cases evaluated by histopathology were assessed blindly and prospectively by an expert confocalist. On histopathology, 210 were LM and 19 were LMM cases. Correct identification of an invasive component was achieved for 17 of 19 LMM cases (89%) and the absence of a dermal component was correctly diagnosed in 190 of 210 LM cases (90%). In the matched case-control (LMM n = 35, LM n = 58), epidermal and junctional disarray, large size of melanocytes and nests of melanocytes were independent predictors of LMM on multivariate analysis. The interobserver analysis demonstrated that these three features had a fair reproducibility between the two investigators (K = 0.4). The multivariable model including those three features showed a high predictive performance AUC = 74% (CI 95% 64-85%), with sensitivity of 63% (95% CI 52-78%) and specificity of 79% (CI 95% 74-88%), and likelihood ratio of 18 (p-value 0.0026). CONCLUSION: Three RCM features were predictive for identifying invasive melanoma in the background of LM.

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.

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.

BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. METHODS: For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. FINDINGS: Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p<0·0001) more correct diagnoses (17·91 [SD 3·42] vs 19·92 [4·27]). 27 human experts with more than 10 years of experience achieved a mean of 18·78 (SD 3·15) correct answers, compared with 25·43 (1·95) correct answers for the top three machine algorithms (mean difference 6·65, 95% CI 6·06-7·25; p<0·0001). The difference between human experts and the top three algorithms was significantly lower for images in the test set that were collected from sources not included in the training set (human underperformance of 11·4%, 95% CI 9·9-12·9 vs 3·6%, 0·8-6·3; p<0·0001). INTERPRETATION: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. FUNDING: None.

Dermatoscopy of Neoplastic Skin Lesions: Recent Advances, Updates, and Revisions.

OPINION STATEMENT: Dermatoscopy (dermoscopy) improves the diagnosis of benign and malignant cutaneous neoplasms in comparison with examination with the unaided eye and should be used routinely for all pigmented and non-pigmented cutaneous neoplasms. It is especially useful for the early stage of melanoma when melanoma-specific criteria are invisible to the unaided eye. Preselection by the unaided eye is therefore not recommended. The increased availability of polarized dermatoscopes, and the extended use of dermatoscopy in non-pigmented lesions led to the discovery of new criteria, and we recommend that lesions should be examined with polarized and non-polarized dermatoscopy. The "chaos and clues algorithm" is a good starting point for beginners because it is easy to use, accurate, and it works for all types of pigmented lesions not only for those melanocytic. Physicians, who use dermatoscopy routinely, should be aware of new clues for acral melanomas, nail matrix melanomas, melanoma in situ, and nodular melanoma. Dermatoscopy should also be used to distinguish between different subtypes of basal cell carcinoma and to discriminate highly from poorly differentiated squamous cell carcinomas to optimize therapy and management of non-melanoma skin cancer. One of the most exciting areas of research is the use of dermatoscopic images for machine learning and automated diagnosis. Convolutional neural networks trained with dermatoscopic images are able to diagnose pigmented lesions with the same accuracy as human experts. We humans should not be afraid of this new and exciting development because it will most likely lead to a peaceful and fruitful coexistence of human experts and decision support systems.

Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin.

BACKGROUND: Nonpigmented skin cancer is common, and diagnosis with the unaided eye is error prone. OBJECTIVE: To investigate whether dermatoscopy improves the diagnostic accuracy for nonpigmented (amelanotic) cutaneous neoplasms. METHODS: We collected a sample of 2072 benign and malignant neoplastic lesions and inflammatory conditions and presented close-up images taken with and without dermatoscopy to 95 examiners with different levels of experience. RESULTS: The area under the curve was significantly higher with than without dermatoscopy (0.68 vs 0.64, P < .001). Among 51 possible diagnoses, the correct diagnosis was selected in 33.1% of cases with and 26.4% of cases without dermatoscopy (P < .001). For experts, the frequencies of correct specific diagnoses of a malignant lesion improved from 40.2% without to 51.3% with dermatoscopy. For all malignant neoplasms combined, the frequencies of appropriate management strategies increased from 78.1% without to 82.5% with dermatoscopy. LIMITATIONS: The study deviated from a real-life clinical setting and was potentially affected by verification and selection bias. CONCLUSIONS: Dermatoscopy improves the diagnosis and management of nonpigmented skin cancer and should be used as an adjunct to examination with the unaided eye.

Die Bedeutung der blauen Farbe in der Dermatoskopie.

HINTERGRUND UND ZIELE: Hautläsionen mit blauer Färbung werden häufig chirurgisch entfernt, um Malignität auszuschließen zu können. Das Ziel der vorliegenden Studie war es, die Bedeutung der blauen Färbung zu untersuchen. METHODEN: Wir untersuchten dermatoskopische Bilder retrospektiv auf blaue Farbe und klassifizierten sie gemäß der Musteranalyse. ERGEBNISSE: Von 1.123 pigmentierten Hautläsionen wiesen 144 (12,8 %) eine blaue Färbung auf. Von diesen waren 92 (63,9 %) bösartig. Unter den Läsionen mit Blaufärbung waren Nävi (n = 35, 24,3 %) und seborrhoische Keratosen (n = 8, 5,6 %) die häufigsten gutartigen Diagnosen. Von 103 (71,5 %) Läsionen mit einem strukturlosen blauen Muster waren acht (7,8 %) vollständig blau gefärbt und 95 (92,2 %) teilweise blau, davon zeigten 81 (78,6 %) periphere oder fleckige und 14 (13.6 %) eine zentrale Blaufärbung. Die meisten Läsionen mit peripherer oder fleckiger blauer Färbung waren Melanome (n = 47, 58 %), wohingegen die meisten Läsionen mit zentraler Blaufärbung Nävi darstellten (n = 9, 64,3 %). Von 28 Läsionen mit blauen Schollen waren 17 (60,7 %) Basalzellkarzinome. Bezüglich der Malignität war der positive prädiktive Wert der blauen Farbe 63,9 % (95 % KI: 56,0-71,8 %). SCHLUSSFOLGERUNGEN: Unter blaugefärbten bösartigen Läsionen ist eine strukturlose periphere oder fleckige blaue Farbe ein Hinweis auf Melanome, während blaue Schollen auf ein Basalzellkarzinom hinweisen. Als Fallstricke können sich seborrhoische Keratosen erweisen, die eine Blaufärbung zeigen können, sowie einige Nävi, vor allem kombinierte.

The significance of blue color in dermatoscopy.

BACKGROUND AND OBJECTIVES: Skin lesions with blue color are frequently excised to rule out malignancy. The objective of the present study was to investigate the significance of blue color. METHODS: We retrospectively scanned dermatoscopic images for blue color and classified them according to pattern analysis. RESULTS: Of 1,123 pigmented skin lesions, 144 (12.8 %) showed blue color, 92 of which (63.9 %) were malignant. Among lesions with blue color, the most common benign diagnoses were nevi (n = 35, 24.3 %) and seborrheic keratoses (n = 8, 5.6 %). Of 103 (71.5 %) lesions with a structureless blue pattern, eight (7.8 %) were entirely blue and 95 (92.2 %) were partly blue, of which 81 (78.6 %) showed peripheral or patchy and 14 (13.6 %) central blue color. Most lesions with peripheral or patchy blue color were melanomas (n = 47, 58 %), whereas most lesions with central blue color were nevi (n = 9, 64.3 %). Of 28 lesions with blue clods, 17 (60.7 %) were basal cell carcinomas. With respect to malignancy, the positive predictive value of blue color was 63.9 % (95 % CI: 56.0-71.8 %). CONCLUSIONS: Among malignant lesions with blue color, structureless peripheral or patchy blue color is a clue for melanoma, while blue clods point to basal cell carcinoma. Pitfalls include seborrheic keratoses, which may show blue color, as well as some nevi, especially combined nevi.

Langzeitauswertung des Nutzens der digitalen Dermatoskopie an einem Referenzzentrum.

HINTERGRUND UND ZIELE: Wir untersuchten den Nutzen der sequentiellen digitalen Dermatoskopie für die Kontrolle von Patienten mit multiplen Nävi in einem spezialisierten Zentrum. PATIENTEN UND METHODIK: Das ist eine retrospektive Kohortenstudie mit 2 824 Patienten und 23 241 kontrollierten Läsionen, wobei für Schlüsselparameter, wie der Anzahl der kontrollierten und exzidierten Melanome und Nävi, eine Trendberechnung durchgeführt wurde. ERGEBNISSE: Im Zuge der Kontrolluntersuchungen haben wir bei 709 Patienten 1 266 Läsionen exzidiert, darunter waren 146 (11,5 %) Melanome. Der Anteil der im Zuge der Kontrolle detektierten in-situ-Melanome war signifikant höher als bei der Erstuntersuchung. (46,6 % gegenüber 23,4 %; p ≤ 0,001). Bei Patienten mit einem Melanom bei der Erstuntersuchung war das Risiko, während der Nachsorge ein Melanom zu detektieren, höher als bei Patienten ohne Melanom (relatives Risiko: 3,59; 95 % CI: 2,15 bis 6,00). Die Zahl der dokumentierten Läsionen korrelierte positiv mit dem Verhältnis von gutartigen zu bösartigen excidierten Läsionen und die Exzisionen bei Erstuntersuchungen gingen während des Untersuchungszeitraums signifikant zurück. SCHLUSSFOLGERUNGEN: Die Überwachung mit digitaler Dermatoskopie verbessert die Detektion dünner Melanome bei Patienten mit multiplen Nävi. Patienten mit einem Melanom im Rahmen der Erstuntersuchung haben ein erhöhtes Risiko, während der Nachsorge ein Melanom zu entwickeln und sollten daher eine Zielgruppe für die sequentielle Dermatoskopie sein.

Long-term evaluation of the efficacy of digital dermatoscopy monitoring at a tertiary referral center.

BACKGROUND AND OBJECTIVES: We examined the value of monitoring patients with multiple nevi using sequential digital dermatoscopy imaging at a tertiary referral center. PATIENTS AND METHODS: This is a retrospective cohort study including 2,824 patients and 23,241 monitored lesions. We calculated trends in key parameters such as the number of melanomas and nevi monitored and excised. RESULTS: During follow-up, we excised 1,266 lesions in 709 patients, including 146 (11.5 %) melanomas. The percentage of in situ melanomas detected at follow-up was significantly higher than at baseline (46.6 % versus 23.4 %, p ≤ 0.001). The risk of detecting a melanoma during follow-up was higher for patients with a melanoma at baseline, compared to those without (relative risk: 3.59, 95 % CI: 2.15 to 6.00). The number of documented lesions showed a positive correlation with the benign/malignant ratio, and excisions at baseline decreased significantly over the course of the study period. CONCLUSION: Digital dermatoscopy monitoring improves the detection of thin melanomas in patients with multiple nevi. Patients with a melanoma at baseline are at an increased risk of developing a melanoma during follow-up and should therefore be a target group for sequential dermatoscopy imaging.

Endovenous thermal ablation of superficial truncal veins and compression therapy result in symptom relief in venous valve aplasia.

Venous valve aplasia (or valvular rarefication) is a rare cause of chronic venous insufficiency. In the present report, we have described the case of a 33-year-old man with severe symmetric lower leg edema and heaviness and pain in both lower legs. Duplex ultrasound demonstrated severe venous insufficiency in the superficial and deep venous system of both legs. Further imaging examinations supported the diagnosis of venous valvular aplasia. Treatment consisted of endovenous thermal ablation of the great saphenous vein and small saphenous vein as well as consistent compression therapy, resulting in a marked reduction of his leg edema, heaviness, and pain.

Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial.

BACKGROUND: Diagnosis of skin cancer requires medical expertise, which is scarce. Mobile phone-powered artificial intelligence (AI) could aid diagnosis, but it is unclear how this technology performs in a clinical scenario. Our primary aim was to test in the clinic whether there was equivalence between AI algorithms and clinicians for the diagnosis and management of pigmented skin lesions. METHODS: In this multicentre, prospective, diagnostic, clinical trial, we included specialist and novice clinicians and patients from two tertiary referral centres in Australia and Austria. Specialists had a specialist medical qualification related to diagnosing and managing pigmented skin lesions, whereas novices were dermatology junior doctors or registrars in trainee positions who had experience in examining and managing these lesions. Eligible patients were aged 18-99 years and had a modified Fitzpatrick I-III skin type; those in the diagnostic trial were undergoing routine excision or biopsy of one or more suspicious pigmented skin lesions bigger than 3 mm in the longest diameter, and those in the management trial had baseline total-body photographs taken within 1-4 years. We used two mobile phone-powered AI instruments incorporating a simple optical attachment: a new 7-class AI algorithm and the International Skin Imaging Collaboration (ISIC) AI algorithm, which was previously tested in a large online reader study. The reference standard for excised lesions in the diagnostic trial was histopathological examination; in the management trial, the reference standard was a descending hierarchy based on histopathological examination, comparison of baseline total-body photographs, digital monitoring, and telediagnosis. The main outcome of this study was to compare the accuracy of expert and novice diagnostic and management decisions with the two AI instruments. Possible decisions in the management trial were dismissal, biopsy, or 3-month monitoring. Decisions to monitor were considered equivalent to dismissal (scenario A) or biopsy of malignant lesions (scenario B). The trial was registered at the Australian New Zealand Clinical Trials Registry ACTRN12620000695909 (Universal trial number U1111-1251-8995). FINDINGS: The diagnostic study included 172 suspicious pigmented lesions (84 malignant) from 124 patients and the management study included 5696 pigmented lesions (18 malignant) from the whole body of 66 high-risk patients. The diagnoses of the 7-class AI algorithm were equivalent to the specialists' diagnoses (absolute accuracy difference 1·2% [95% CI -6·9 to 9·2]) and significantly superior to the novices' ones (21·5% [13·1 to 30·0]). The diagnoses of the ISIC AI algorithm were significantly inferior to the specialists' diagnoses (-11·6% [-20·3 to -3·0]) but significantly superior to the novices' ones (8·7% [-0·5 to 18·0]). The best 7-class management AI was significantly inferior to specialists' management (absolute accuracy difference in correct management decision -0·5% [95% CI -0·7 to -0·2] in scenario A and -0·4% [-0·8 to -0·05] in scenario B). Compared with the novices' management, the 7-class management AI was significantly inferior (-0·4% [-0·6 to -0·2]) in scenario A but significantly superior (0·4% [0·0 to 0·9]) in scenario B. INTERPRETATION: The mobile phone-powered AI technology is simple, practical, and accurate for the diagnosis of suspicious pigmented skin cancer in patients presenting to a specialist setting, although its usage for management decisions requires more careful execution. An AI algorithm that was superior in experimental studies was significantly inferior to specialists in a real-world scenario, suggesting that caution is needed when extrapolating results of experimental studies to clinical practice. FUNDING: MetaOptima Technology.

Adaptive compounding speckle-noise-reduction filter for optical coherence tomography images.

SIGNIFICANCE: Speckle noise limits the diagnostic capabilities of optical coherence tomography (OCT) images, causing both a reduction in contrast and a less accurate assessment of the microstructural morphology of the tissue. AIM: We present a speckle-noise reduction method for OCT volumes that exploits the advantages of adaptive-noise wavelet thresholding with a wavelet compounding method applied to several frames acquired from consecutive positions. The method takes advantage of the wavelet representation of the speckle statistics, calculated properly from a homogeneous sample or a region of the noisy volume. APPROACH: The proposed method was first compared quantitatively with different state-of-the-art approaches by being applied to three different clinical dermatological OCT volumes with three different OCT settings. The method was also applied to a public retinal spectral-domain OCT dataset to demonstrate its applicability to different imaging modalities. RESULTS: The results based on four different metrics demonstrate that the proposed method achieved the best performance among the tested techniques in suppressing noise and preserving structural information. CONCLUSIONS: The proposed OCT denoising technique has the potential to adapt to different image OCT settings and noise environments and to improve image quality prior to clinical diagnosis based on visual assessment.

Dupilumab for the Treatment of Atopic Dermatitis in an Austrian Cohort-Real-Life Data Shows Rosacea-Like Folliculitis.

Dupilumab is the first biological treatment approved for moderate-to-severe atopic dermatitis (AD). Efficacy and safety have been demonstrated in clinical trials, but real-life data is still limited. The objective of this study was to retrospectively evaluate Dupilumab treatment in AD patients in a real-life clinical setting. Effectiveness and safety outcomes were collected at baseline and after 2, 6, 10, 24, 39, and 52 weeks by using clinical scores for disease activity, as well as serological markers. Ninety-four patients from five dermatological hospitals were included. After 24 weeks of treatment, the median Investigator Global Assessment (IGA) and Eczema Area and Severity Index (EASI) showed a significant reduction compared to baseline (3.9 ± 0.7 vs. 1.4 ± 0.8 and 26.5 ± 12.5 vs. 6.4 ± 6.5). Interestingly, we observed rosacea-like folliculitis as an unexpected side effect in 6.4% of patients. Dupilumab proves to be an effective and well-tolerated treatment under real-life conditions. The occurrence of rosacea-like folliculitis warrants further mechanistic investigation.

Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation.

BACKGROUND AND OBJECTIVE: Fully convolutional neural networks have been shown to perform well for automated skin lesion segmentation on digital dermatoscopic images. Our concept is that transferring encoder weights from a network trained on a classification task on images of the same domain may contain useful information for segmentation. METHODS: We trained a fully convolutional network where ResNet34 layers are reused as encoding layers of a U-Net style architecture. We entered the encoding layers i) with He uniform ("random") initialization, ii) pretrained ImageNet weights, or iii) after fine-tuning ResNet34 for skin lesion classification. After transferring the layers to the fully convolutional network architecture we trained for a binary segmentation task using official ISIC 2017 challenge data. RESULTS: Pretraining of ResNet34-layers with either ImageNet or fine-tuning for skin lesion classification achieved a higher Jaccard than random initialization (0.763 and 0.768 vs 0.740) on the ISIC 2017 test-set. This improved performance warrants further exploration on how to implement cross-task learning for skin lesion segmentation. In additional experiments we found that post-processing with fully connected conditional random fields consistently decreased Jaccard on ISIC 2017 test-set images despite reasonable visual results. Further exploration of the test-set revealed that conditional random field - post-processing decreased segmentation performance only if ground truth annotations consisted of simple shapes but increased it if shapes were complex. CONCLUSIONS: Our findings suggest that domain specific pretraining of encoders can be helpful when there are only few ground truth masks available for segmentation training, but may not be of additional benefit to ImageNet pretraining given enough segmentation training data. Complexity of ground truth annotations have a large impact on segmentation metrics and should be taken into account in skin lesion segmentation research.

Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis.

IMPORTANCE: The recent advances in the field of machine learning have raised expectations that computer-aided diagnosis will become the standard for the diagnosis of melanoma. OBJECTIVE: To critically review the current literature and compare the diagnostic accuracy of computer-aided diagnosis with that of human experts. DATA SOURCES: The MEDLINE, arXiv, and PubMed Central databases were searched to identify eligible studies published between January 1, 2002, and December 31, 2018. STUDY SELECTION: Studies that reported on the accuracy of automated systems for melanoma were selected. Search terms included melanoma, diagnosis, detection, computer aided, and artificial intelligence. DATA EXTRACTION AND SYNTHESIS: Evaluation of the risk of bias was performed using the QUADAS-2 tool, and quality assessment was based on predefined criteria. Data were analyzed from February 1 to March 10, 2019. MAIN OUTCOMES AND MEASURES: Summary estimates of sensitivity and specificity and summary receiver operating characteristic curves were the primary outcomes. RESULTS: The literature search yielded 1694 potentially eligible studies, of which 132 were included and 70 offered sufficient information for a quantitative analysis. Most studies came from the field of computer science. Prospective clinical studies were rare. Combining the results for automated systems gave a melanoma sensitivity of 0.74 (95% CI, 0.66-0.80) and a specificity of 0.84 (95% CI, 0.79-0.88). Sensitivity was lower in studies that used independent test sets than in those that did not (0.51; 95% CI, 0.34-0.69 vs 0.82; 95% CI, 0.77-0.86; P < .001); however, the specificity was similar (0.83; 95% CI, 0.71-0.91 vs 0.85; 95% CI, 0.80-0.88; P = .67). In comparison with dermatologists' diagnosis, computer-aided diagnosis showed similar sensitivities and a 10 percentage points lower specificity, but the difference was not statistically significant. Studies were heterogeneous and substantial risk of bias was found in all but 4 of the 70 studies included in the quantitative analysis. CONCLUSIONS AND RELEVANCE: Although the accuracy of computer-aided diagnosis for melanoma detection is comparable to that of experts, the real-world applicability of these systems is unknown and potentially limited owing to overfitting and the risk of bias of the studies at hand.

Euphorbia myrsinites Sap-Induced Phytodermatitis: A Prototype of Irritant Contact Dermatitis?

BACKGROUND: A hallmark of Euphorbia myrsinites (EM), a member of the widespread perennial Euphorbia species, is the extrusion of a poisonous, latex-like sap irritant to the skin and eye after contact. The exact mechanisms underlying these effects have not been unraveled so far. OBJECTIVES: The aims of the study were to allocate EM sap-induced phytodermatitis to irritant or allergic contact dermatitis (ACD) and to investigate mechanism(s) causing keratinocyte damage. METHODS: Cutaneous effects of EM sap on healthy human skin were investigated by clinical scoring and reflectance confocal microscopy analyses and compared with ACD. In addition, the effects of sap exposure to keratinocytes were analyzed in vitro using histological analyses and flow cytometry. CONCLUSIONS: We report on 2 cases of EM sap-induced phytodermatitis. Patch testing with fresh EM sap induced dermatitis in 100% of the tested sites with a clinical course following a decrescendo pattern. Compared with ACD, the lesional phenotype was more severe and epidermal disruption was more pronounced. Exposure of human skin tissues and cultivated keratinocytes to EM sap in vitro resulted in a dose-dependent increase in keratinocyte apoptosis. The reported findings support the primarily toxic irritant nature of EM sap-induced phytodermatitis. The contribution of ingenol mebutate to (nontoxic) proinflammatory effects remains to be elucidated.

Automatic skin lesion area determination of basal cell carcinoma using optical coherence tomography angiography and a skeletonization approach: Preliminary results.

Cutaneous blood flow plays a key role in numerous physiological and pathological processes and has significant potential to be used as a biomarker to diagnose skin diseases such as basal cell carcinoma (BCC). The determination of the lesion area and vascular parameters within it, such as vessel density, is essential for diagnosis, surgical treatment and follow-up procedures. Here, an automatic skin lesion area determination algorithm based on optical coherence tomography angiography (OCTA) images is presented for the first time. The blood vessels are segmented within the OCTA images and then skeletonized. Subsequently, the skeleton is searched over the volume and numerous quantitative vascular parameters are calculated. The vascular density is then used to segment the lesion area. The algorithm is tested on both nodular and superficial BCC, and comparing with dermatological and histological results, the proposed method provides an accurate, non-invasive, quantitative and automatic tool for BCC lesion area determination.

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

Importance: Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose. Objective: To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience. Design, Setting, and Participants: A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy. Main Outcomes and Measures: The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures. Results: Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P < .001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P = .001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P = .18). Conclusions and Relevance: Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.