This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).

IMPORTANCE Deep learning convolutional neural networks (CNNs) have shown a performance at the level of dermatologists in the diagnosis of melanoma. Accordingly, further exploring the potential limitations of CNN technology before broadly applying it is of special interest. OBJECTIVE To investigate the association between gentian violet surgical skin markings in dermoscopic images and the diagnostic performance of a CNN approved for use as a medical device in the European market. DESIGN AND SETTING A cross-sectional analysis was conducted from August 1, 2018, to November 30, 2018, using a CNN architecture trained with more than 120 000 dermoscopic images of skin neoplasms and corresponding diagnoses. The association of gentian violet skin markings in dermoscopic images with the performance of the CNN was investigated in 3 image sets of 130 melanocytic lesions each (107 benign nevi, 23 melanomas). EXPOSURES The same lesions were sequentially imaged with and without the application of a gentian violet surgical skin marker and then evaluated by the CNN for their probability of being a melanoma. In addition, the markings were removed by manually cropping the dermoscopic images to focus on the melanocytic lesion. MAIN OUTCOMES AND MEASURES Sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the CNN's diagnostic classification in unmarked, marked, and cropped images. RESULTS In all, 130 melanocytic lesions (107 benign nevi and 23 melanomas) were imaged. In unmarked lesions, the CNN achieved a sensitivity of 95.7% (95% CI, 79%-99.2%) and a specificity of 84.1% (95% CI, 76.0%-89.8%). The ROC AUC was 0.969. In marked lesions, an increase in melanoma probability scores was observed that resulted in a sensitivity of 100% (95% CI, 85.7%-100%) and a significantly reduced specificity of 45.8% (95% CI, 36.7%-55.2%, P < .001). The ROC AUC was 0.922. Cropping images led to the highest sensitivity of 100% (95% CI, 85.7%-100%), specificity of 97.2% (95% CI, 92.1%-99.0%), and ROC AUC of 0.993. Heat maps created by vanilla gradient descent backpropagation indicated that the blue markings were associated with the increased false-positive rate. CONCLUSIONS AND RELEVANCE This study's findings suggest that skin markings significantly interfered with the CNN's correct diagnosis of nevi by increasing the melanoma probability scores and consequently the false-positive rate. A predominance of skin markings in melanoma training images may have induced the CNN's association of markings with a melanoma diagnosis. Accordingly, these findings suggest that skin markings should be avoided in dermoscopic images intended for analysis by a CNN. TRIAL REGISTRATION German Clinical Trial Register (DRKS) Identifier: DRKS00013570

No abstract

No abstract

No abstract

Background Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not been investigated. Objective To assess the diagnostic performance of a CNN when used to differentiate melanomas from combined naevi in comparison with dermatologists. Methods In this study, a CNN with regulatory approval for the European market (Moleanalyzer-Pro, FotoFinder Systems GmbH, Bad Birnbach, Germany) was used. We attained a dichotomous classification (benign, malignant) in dermoscopic images of 36 combined naevi and 36 melanomas with a mean Breslow thickness of 1.3 mm. Primary outcome measures were the CNN's sensitivity, specificity and the diagnostic odds ratio (DOR) in comparison with 11 dermatologists with different levels of experience. Results The CNN revealed a sensitivity, specificity and DOR of 97.1% (95% CI [82.7-99.6]), 78.8% (95% CI [62.8-89.1.3]) and 34 (95% CI [4.8-239]), respectively. Dermatologists showed a lower mean sensitivity, specificity and DOR of 90.6% (95% CI [84.1-94.7]; P = 0.092), 71.0% (95% CI [62.6-78.1]; P = 0.256) and 24 (95% CI [11.6-48.4]; P = 0.1114). Under the assumption that dermatologists use the CNN to verify their (initial) melanoma diagnosis, dermatologists achieve an increased specificity of 90.3% (95% CI [79.8-95.6]) at an almost unchanged sensitivity. The largest benefit was observed in 'beginners', who performed worst without CNN verification (DOR = 12) but best with CNN verification (DOR = 98). Conclusion The tested CNN more accurately classified combined naevi and melanomas in comparison with trained dermatologists. Their diagnostic performance could be improved if the CNN was used to confirm/overrule an initial melanoma diagnosis. Application of a CNN may therefore be of benefit to clinicians.

Cutaneous lupus erythematosus (CLE) is a heterogeneous autoimmune disease. Different pathogenetic mechanisms, including the accumulation of apoptotic keratinocytes in CLE, have been reported. Therefore, we investigated whether CLE and other inflammatory skin diseases differ with regard to the epidermal expression of molecules that are crucial for the initiation and regulation of apoptosis. In this study, 241 skin biopsies from patients with CLE, psoriasis (PSO), lichen planus (LP) and healthy controls (HCs) were analysed immunohistochemically using the tissue microarray (TMA) technique. The TUNEL assay and anti-activated caspase-3 antibodies revealed a significant increase of apoptotic keratinocytes in CLE lesions compared with HCs. Furthermore, we detected a significant increase in the epidermal expression of CD95 in CLE specimens compared with PSO, LP and HCs. These data suggest that the accumulation of apoptotic keratinocytes in CLE might be due to the increased epidermal expression of CD95, resulting in increased activity of the extrinsic apoptotic pathway in the disease.

A 19-year-old man suffering from testicular choriocarcinoma presented to the dermatology department with a cutaneous metastasis on his head. This metastasis was the first sign of disease that led to medical consultation. Histopathology revealed cytotrophoblasts and syncytiotrophoblasts, the later expressing human chorionic gonadotropin antigen. Whole body computed tomography showed multiple metastases of the brain, lung, liver, bone, paraaortic lymph nodes and left uvea; the primary was found in the left testicle. Despite neurosurgical intervention and chemotherapy the patient died 9 days after the biopsy of the cutaneous metastasis. Cutaneous metastases of testicular choriocarcinoma are exceptionally rare, with fewer than a dozen cases reported in the English-language literature. The present case highlights that testicular choriocarcinoma metastatic to the skin should be included in the differential of cutaneous scalp tumors.