Hemodynamic changes during conventional and daylight photodynamic therapy of actinic keratoses - a randomized controlled trial.

BACKGROUND: Photodynamic therapy (PDT) is an effective treatment for actinic keratosis (AK). However, pain and hypertension are important side effects of conventional PDT (c-PDT). Several studies have demonstrated that daylight PDT (dl-PDT) is less painful while being as effective as c-PDT. OBJECTIVE: To observe the effect of c-PDT and dl-PDT on different hemodynamic parameters (systolic blood pressure and diastolic blood pressure, pulse rate, and peripheral oxygen saturation). METHODS: Fifty patients with AK on the head were enrolled into this prospective, randomized, controlled study and treated with c-PDT or dl-PDT in a 1:1 ratio. Hemodynamic parameters were measured at four different time points during treatment. Pain was quantified using a visual analog scale. AK was counted before treatment and after one month. RESULTS: C-PDT is associated with significantly more pain, a significant increase in blood pressure and a higher rate of patients with grade 3 hypertension. Whereas dl-PDT is almost painless and does not lead to any changes in hemodynamic parameters. For both treatments, a similar lesion response rate was found after one month. CONCLUSIONS: dl-PDT has a better tolerability while being as effective as c-PDT and therefore may be the more favorable treatment option in certain patient groups.

Metabolic reprogramming and angiogenesis in primary cutaneous Merkel cell carcinoma: expression of hypoxia-inducible factor-1α and its central downstream factors.

BACKGROUND: Metabolic reprogramming and altered gene expression mediated by hypoxia-inducible factors play crucial roles during tumour growth and progression. Nevertheless, studies analysing the expression of hypoxia-inducible factor-1α and its downstream targets in Merkel cell carcinoma (MCC) are lacking but are warranted to shed more light on MCC pathogenesis and to potentially provide new therapeutic options. OBJECTIVES: To analyse the immunohistochemical expression of hypoxia-inducible factor-1α (HIF-1α), vascular endothelial growth factor-A (referred to as VEGF throughout the manuscript), VEGF receptor-2 (VEGFR-2), VEGF receptor-3 (VEGFR-3), glucose transporter-1 (Glut-1), monocarboxylate transporter 4 (MCT4) and carbonic anhydrase IX (CAIX) in primary cutaneous MCC. METHODS: The 16 paraffin-embedded primary cutaneous MCCs (Merkel cell polyomavirus (McPyV) positive/negative: 11/5) were analysed by immunohistochemistry, namely HIF-1α, VEGF, VEGFR-2 (KDR), VEGFR-3 (FLT4), Glut-1, MCT4 and CAIX. An established quantification score (QS) was applied to quantitate the protein expression by considering the percentage of positive tumour cells (0: 0%; 1: up to 1%; 2: 2-10%; 3: 11-50%; 4: >50%) in relation to the staining intensity (0: negative; 1: low; 2: medium; 3: strong). RESULTS: HIF-1α was expressed in all MCCs and predominantly found at the invading edges of tumour margins. The HIF-1α downstream factors Glut-1, MCT4 and CAIX were expressed in 13 of 16 MCC (81%), 14 of 16 MCC (88%) and 16 of 16 MCC (100%), respectively. Interestingly, VEGF and VEGFR-2 were not expressed in tumour cells, whereas VEGFR-3 was expressed in all MCCs. HIF-1α was expressed significantly stronger in McPyV+ tumours (QS: 10.36 ± 2.41) than in McPyV- tumours (QS: 5.40 ± 1.34; P = 0.002). Similarly, VEGFR-3 was also expressed significantly stronger in McPyV+ tumours (QS: 10.00 ± 2.52) than in McPyV- tumours (QS: 5.40 ± 3.43, P = 0.019). CONCLUSIONS: Our data provide first evidence for a role of HIF-1α in induced metabolic reprogramming contributing to MCC pathogenesis. The metabolic signatures of McPyV+ and McPyV- tumours seem to show relevant differences.

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.

Livedo racemosa in neurological diseases: an update on the differential diagnoses.

Livedo is a net-like violaceous skin pattern. It can be classified as physiological or pathological. The physiological livedo reticularis usually appears in cold conditions, whereas the pathological and irregular livedo, which persists in warm temperatures, is often labeled as 'livedo racemosa'. Some neurological pathologies are associated with livedo, most commonly those with an inflammatory component or those derived from systemic disorders. The present review summarizes the most important central and peripheral neurological diseases in pediatric and adult age groups associated with livedo, providing physicians with an overview of the clinical presentation, etiology, diagnosis and management of these conditions.

Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.

BACKGROUND: Convolutional neural networks (CNNs) efficiently differentiate skin lesions by image analysis. Studies comparing a market-approved CNN in a broad range of diagnoses to dermatologists working under less artificial conditions are lacking. MATERIALS AND METHODS: One hundred cases of pigmented/non-pigmented skin cancers and benign lesions were used for a two-level reader study in 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Additionally, dermoscopic images were classified by a CNN approved for the European market as a medical device (Moleanalyzer Pro, FotoFinder Systems, Bad Birnbach, Germany). Primary endpoints were the sensitivity and specificity of the CNN's dichotomous classification in comparison with the dermatologists' management decisions. Secondary endpoints included the dermatologists' diagnostic decisions, their performance according to their level of experience, and the CNN's area under the curve (AUC) of receiver operating characteristics (ROC). RESULTS: The CNN revealed a sensitivity, specificity, and ROC AUC with corresponding 95% confidence intervals (CI) of 95.0% (95% CI 83.5% to 98.6%), 76.7% (95% CI 64.6% to 85.6%), and 0.918 (95% CI 0.866-0.970), respectively. In level I, the dermatologists' management decisions showed a mean sensitivity and specificity of 89.0% (95% CI 87.4% to 90.6%) and 80.7% (95% CI 78.8% to 82.6%). With level II information, the sensitivity significantly improved to 94.1% (95% CI 93.1% to 95.1%; P < 0.001), while the specificity remained unchanged at 80.4% (95% CI 78.4% to 82.4%; P = 0.97). When fixing the CNN's specificity at the mean specificity of the dermatologists' management decision in level II (80.4%), the CNN's sensitivity was almost equal to that of human raters, at 95% (95% CI 83.5% to 98.6%) versus 94.1% (95% CI 93.1% to 95.1%); P = 0.1. In contrast, dermatologists were outperformed by the CNN in their level I management decisions and level I and II diagnostic decisions. More experienced dermatologists frequently surpassed the CNN's performance. CONCLUSIONS: Under less artificial conditions and in a broader spectrum of diagnoses, the CNN and most dermatologists performed on the same level. Dermatologists are trained to integrate information from a range of sources rendering comparative studies that are solely based on one single case image inadequate.

Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.

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.

Skin cancer in organ transplant recipients: dynamics in the incidence and clinical predictors for the first and subsequent post-transplant non-melanoma skin cancer.

BACKGROUND: Risk factors for primary non-melanoma skin cancer (NMSC) in organ transplant recipients (OTR) have been well described. Data for subsequent NMSC and dynamics in their occurrence in OTR are limited. OBJECTIVE: To study long-term risks of primary and subsequent NMSC and associated risk factors in OTR. METHODS: A retrospective single-centre cohort study analysing medical records from a dermato-oncological specialty clinic. RESULTS: Of 464 OTR 110 (23.7%) developed at least one, 73 (15.7%) two and 51 (11%) three NMSC during a median follow-up of 9.6 years. Cumulative incidences at 5, 10 and 15 years were 14.7%, 23.5% and 34.5% for the first and 75.8%, 86.5% and 93.3% for the second. Median time-to-diagnosis declined from 22 years (95% CI 19-25) to 2 years (1-3) and about 1 year (0-2) for the first, second and third NMSC. Risk for subsequent NMSC only partially related to risk factors for the primary NMSC. Histologic type of the first NMSC predicted subtype and time-to-diagnosis of the subsequent NMSC. CONCLUSIONS: A first post-transplant NMSC, particularly a SCC, confers a high risk for subsequent NMSC arising with accelerated dynamics. Risk-adapted dermato-oncologic surveillance is advisable for all OTR.

Precision and reproducibility of automated computer-guided Psoriasis Area and Severity Index measurements in comparison with trained physicians.

BACKGROUND: The Psoriasis Area and Severity Index (PASI) represents the gold standard for psoriasis severity assessments but is limited by its subjectivity and low intra- and inter-rater consistency. OBJECTIVES: To investigate the precision and reproducibility of automated, computer-guided PASI measurements (ACPMs) in comparison with three trained physicians. METHODS: This was a comparative observational study assessing ACPMs attained by automated total-body imaging and computerized digital image analysis in a cohort of 120 patients affected by plaque psoriasis of various severities. The level of agreement between ACPMs and physicians' PASI measurements was calculated by the intraclass correlation coefficient (ICC). The reproducibility of ACPMs in comparison with physicians' PASI measurements was investigated by performing two successive 'repeat PASI calculations' in the same patients. RESULTS: The agreement between ACPMs and physicians' PASI calculations in 120 fully evaluable patients was high (ICC 0·86, 95% confidence interval 0·80-0·90, mean absolute difference 2·5 PASI points). Repeat ACPMs to measure the reproducibility showed an excellent ICC of 0·99 (95% confidence interval 0·98-0·99) with a mean absolute difference of 0·5 PASI points. The ACPMs thus outperformed the three physicians for intrarater reliability (mean ICC 0·86). CONCLUSIONS: The results of this first clinical study investigating ACPMs in 120 patients with psoriasis indicate a similar precision and higher reproducibility in comparison with trained physicians. Limitations arise from poorly observable body sites and from patients unable to attain predefined postures during automated image acquisition.

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.

Background: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking. Methods: Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. Conclusions: For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification. Clinical trial number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).

Intra- and interobserver variability of image-based PASI assessments in 120 patients suffering from plaque-type psoriasis.

BACKGROUND: The Psoriasis Area and Severity Index (PASI) is the standard for psoriasis severity assessment. However, PASI measurement is complex and subjective, frequently leading to a high intra- and interobserver variability. To date, the precise extent of variability in PASI measurements and its underlying causes remain unknown. OBJECTIVE: To determine the inter- and intrarater variability of image-based PASI measurements by calculating Intra-Class-Correlation-Coefficients (ICCs) and to investigate the impact of the different PASI components and specific anatomic regions on the extent of variability. METHODS: First, the methodology of 'image-based' vs. commonly used 'live' PASI measurements was validated in a pilot study. Next, in an observational cohort study, PASI scores of 120 patients affected by plaque psoriasis were prospectively evaluated by three formally trained physicians by means of total body images (TBI). Each observer independently performed two rounds of image-based PASI calculations in all patients at two different time points. RESULTS: Overall, 720 image-based PASI scores were calculated with a mean PASI of 8.8 (range 0.7-34.8). An interrater variability with an ICC of 0.895 and mean absolute difference (MAD) of 3.3 PASI points were observed. Intrarater variability showed a mean ICC of 0.877 and a MAD of 2.2 points. When considering specific PASI components, the highest agreement was found for the assessment of the involved body surface area (BSA), while the lowest ICCs were calculated for severity scoring of 'scaling' and 'induration'. As BSA scores serve as a multiplier in the calculation of PASI, minor inaccuracies were capable of inducing a large share of variability. CONCLUSION: The overall inter- and intrarater reliability of image-based PASI measurements in this study was good. However, physicians were formally trained and experienced, which frequently is not the case in a real-life clinical setting. Therefore, new strategies for higher standardization and objectivity of PASI calculations are needed.