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.

[Artificial intelligence and smartphone program applications (Apps) : Relevance for dermatological practice]. / Künstliche Intelligenz und Smartphone-Programm-Applikationen (Apps) : Bedeutung für die dermatologische Praxis.

ADVANTAGES OF ARTIFICIAL INTELLIGENCE (AI): With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2) patients can be more quickly and effectively treated despite the increasing incidence of skin cancer and the decreasing number of actively working dermatologists and (3) users can learn from the AI results. POTENTIAL DISADVANTAGES AND RISKS OF AI USE: (1) Lack of mutual trust can develop due to the decreased patient-physician contact, (2) additional time effort will be necessary to promptly evaluate the AI-classified benign lesions, (3) lack of adequate medical experience to recognize misclassified AI decisions and (4) recontacting a patient in due time in the case of incorrect AI classifications. Still problematic in the use of AI are the medicolegal situation and remuneration. Apps using AI currently cannot provide sufficient assistance based on clinical images of skin cancer. REQUIREMENTS AND POSSIBLE USE OF SMARTPHONE PROGRAM APPLICATIONS: Smartphone program applications (apps) can be implemented responsibly when the image quality is good, the patient's history can be entered easily, transmission of the image and results are assured and medicolegal aspects as well as remuneration are clarified. Apps can be used for disease-specific information material and can optimize patient care by using teledermatology.

[New optical examination procedures for the diagnosis of skin diseases]. / Neue optische Untersuchungsverfahren für die Diagnostik von Hautkrankheiten.

BACKGROUND: Since the establishment of dermoscopy as a routine examination procedure in dermatology, the spectrum of noninvasive, optical devices has further expanded. In difficult-to-diagnose clinical cases, these systems may support dermatologists to arrive at a correct diagnosis without the need for a surgical biopsy. OBJECTIVE: To give an overview about technical background, indications and diagnostic performance regarding four new optical procedures: reflectance confocal microscopy, in vivo multiphoton tomography, dermatofluoroscopy, and systems based on image analysis by artificial intelligence (AI). MATERIALS AND METHODS: This article is based on a selective review of the literature, as well as the authors' personal experience from clinical studies relevant for market approval of the devices. RESULTS: In contrast to standard histopathological slides with vertical cross sections, reflectance confocal microscopy and in vivo multiphoton tomography allow for "optical biopsies" with horizontal cross sections. Dermatofluoroscopy and AI-based image analyzers provide a numerical score, which helps to correctly classify a skin lesion. The presented new optical procedures may be applied for the diagnosis of skin cancer as well as inflammatory skin diseases. CONCLUSION: The presented optical procedures provide valuable additional information that supports dermatologists in making the correct diagnosis. However, a surgical biopsy followed by dermatohistopathological examination remains the diagnostic gold standard in dermatology.

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.

[Dermoscopy in special locations : Nails, acral skin, face, and mucosa]. / Dermatoskopie in Sonderlokalisationen : Nägel, akrale Haut, Gesicht und Mukosa.

The use of dermoscopy by dermatologists across Europe has become a standard examination for benign and malignant skin lesions and increasingly also for inflammatory skin diseases. However, based on the experience of the authors from numerous dermoscopy courses, knowledge about important dermoscopic features in special locations such as mucosa or nails is often limited. This may be explained by (1) a different anatomy of the skin and its adnexa in special locations in comparison to the remaining integument, (2) difficult technical access to special locations with a dermatoscope, and (3) a rather low incidence of malignant skin neoplasms in areas of special locations (with the exception of facial skin/scalp). This article aims at explaining dermoscopic characteristics and features of important benign and malignant lesions of nails, acral skin, face, and mucosa.

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/).

Diagnostic accuracy of dermatofluoroscopy in cutaneous melanoma detection: results of a prospective multicentre clinical study in 476 pigmented lesions.

BACKGROUND: Early detection is a key factor in improving survival from melanoma. Today, the clinical diagnosis of cutaneous melanoma is based mostly on visual inspection and dermoscopy. Preclinical studies in freshly excised or paraffin-embedded tissue have shown that the melanin fluorescence spectra after stepwise two-photon excitation, a process termed dermatofluoroscopy, differ between cutaneous melanoma and melanocytic naevi. However, confirmation from a larger prospective clinical study is lacking. OBJECTIVES: The primary end point of this study was to determine the diagnostic accuracy of dermatofluoroscopy in melanoma detection. Secondary end points included the collection of data for improving the computer algorithm that classifies skin lesions based on melanin fluorescence and the assessment of safety aspects. METHODS: This was a prospective, blinded, multicentre clinical study in patients with pigmented skin lesions (PSLs) indicated for excision either to rule out or to confirm cutaneous melanoma. All included lesions underwent dermoscopy and dermatofluoroscopy in vivo before lesions were excised and subjected to histopathological examination. RESULTS: In total, 369 patients and 476 PSLs were included in the final analysis. In 101 of 476 lesions (21·2%) histopathology revealed melanoma. The observed sensitivity of dermatofluoroscopy was 89·1% (90 of 101 melanomas identified), with an observed specificity of 44·8%. The positive and negative predictive values were 30·3% and 93·9%, respectively. No adverse events occurred. CONCLUSIONS: Dermatofluoroscopy is a safe and accurate diagnostic method to aid physicians in diagnosing cutaneous melanoma. Limitations arise from largely amelanotic or regressing lesions lacking sufficient melanin fluorescence.

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.

[Dermoscopy for malignant and benign skin tumors : Indication and standardized terminology]. / Dermatoskopie bei malignen und benignen Hauttumoren : Indikation und standardisierte Terminologie.

Dermoscopy has a high diagnostic accuracy in pigmented and nonpigmented malignant and benign skin tumors. These microscopic in vivo examinations with polarized and nonpolarized light are effective in the early detection of malignant skin tumors and reduce the number of unnecessary excisions of benign skin tumors. The selection of the skin lesions is crucial for the diagnostic accuracy of the dermoscopic examination. Not only large pigmented skin lesions, but also small hypo-, de-, or nonpigmented skin lesions, should be examined dermatoscopically as well as skin lesions that have changed in shape and/or color. In clinical routine, research and teaching, the dermoscopic diagnosis should be performed by describing the visible structures, their distribution and colors by means of descriptive and/or metaphoric terminology. Optionally, a diagnostic algorithm can also be used. Especially in benign lesions, the dermatoscopic diagnosis should be uniform for the complete area. Comparison with other nearby skin tumors of the same patient (comparative approach) is helpful in the evaluation of numerous melanocytic skin tumors. If it is unclear whether the lesion is malignant, a biopsy or complete excision should be performed with subsequent histopathological examination.