Search

ZusammenfassungWeltweit steigt die Inzidenz des malignen Melanoms an. Bei frühzeitiger Erkennung ist das Melanom gut behandelbar, eine Früherkennung ist also lebenswichtig.Die Hautkrebs-Früherkennung hat sich in den letzten Jahrzehnten bspw. durch die Einführung des Screenings im Jahr 2008 und die Dermatoskopie deutlich verbessert. Dennoch bleibt die visuelle Erkennung insbesondere von frühen Melanomen eine Herausforderung, weil diese viele morphologische Überlappungen mit Nävi zeigen. Daher ist der medizinische Bedarf weiterhin hoch, die Methoden zur Hautkrebsfrüherkennung gezielt weiterzuentwickeln, um Melanome bereits in einem sehr frühen Stadium sicher diagnostizieren zu können.Die Routinediagnostik zur Hautkrebs-Früherkennung umfasst die visuelle Ganzkörperinspektion, oft ergänzt durch die Dermatoskopie, durch die sich die diagnostische Treffsicherheit erfahrener Hautärzte deutlich erhöhen lässt. Ein Verfahren, was in einigen Praxen und Kliniken zusätzlich angeboten wird, ist die kombinierte Ganzkörperfotografie mit der digitalen Dermatoskopie für die Früherkennung maligner Melanome, insbesondere für das Monitoring von Hochrisiko-Patienten.In den letzten Jahrzenten wurden zahlreiche nicht invasive zusatzdiagnostische Verfahren zur Beurteilung verdächtiger Pigmentmale entwickelt, die das Potenzial haben könnten, eine verbesserte und z. T. automatisierte Bewertung dieser Läsionen zu ermöglichen. In erster Linie ist hier die konfokale Lasermikroskopie zu nennen, ebenso die elektrische Impedanzspektroskopie, die Multiphotonen-Lasertomografie, die Multispektralanalyse, die Raman-Spektroskopie oder die optische Kohärenztomografie. Diese diagnostischen Verfahren fokussieren i. d. R. auf hohe Sensitivität, um zu vermeiden, ein malignes Melanom zu übersehen. Dies bedingt allerdings üblicherweise eine geringere Spezifität, was im Screening zu unnötigen Exzisionen vieler gutartiger Läsionen führen kann. Auch sind einige der Verfahren zeitaufwendig und kostenintensiv, was die Anwendbarkeit im Screening ebenfalls einschränkt.In naher Zukunft wird insbesondere die Nutzung von künstlicher Intelligenz die Diagnosefindung in vielfältiger Weise verändern. Vielversprechend ist v. a. die Analyse der makroskopischen und dermatoskopischen Routine-Bilder durch künstliche Intelligenz. Für die Klassifizierung von pigmentierten Hautläsionen anhand makroskopischer und dermatoskopischer Bilder erzielte die künstliche Intelligenz v. a. in Form neuronaler Netze unter experimentellen Bedingungen in zahlreichen Studien bereits eine vergleichbare diagnostische Genauigkeit wie Dermatologen. Insbesondere bei der binären Klassifikationsaufgabe Melanom/Nävus erreichte sie hohe Genauigkeiten, doch auch in der Multiklassen-Differenzierung von verschiedenen Hauterkrankungen zeigt sie sich vergleichbar gut wie Dermatologen. Der Nachweis der grundsätzlichen Anwendbarkeit und des Nutzens solcher Systeme in der klinischen Praxis steht jedoch noch aus. Noch zu schaffende Grundvoraussetzungen für die Translation solcher Diagnosesysteme in die dermatologischen Routine sind Möglichkeiten für die Nutzer, die Entscheidungen des Systems nachzuvollziehen, sowie eine gleichbleibend gute Leistung der Algorithmen auf Bilddaten aus fremden Kliniken und Praxen.Derzeit zeichnet sich ab, dass computergestützte Diagnosesysteme als Assistenzsysteme den größten Nutzen bringen könnten, denn Studien deuten darauf hin, dass eine Kombination von Mensch und Maschine die besten Ergebnisse erzielt. Diagnosesysteme basierend auf künstlicher Intelligenz sind in der Lage, Merkmale schnell, quantitativ, objektiv und reproduzierbar zu erfassen, und könnten somit die Medizin auf eine mathematische Grundlage stellen – zusätzlich zur ärztlichen Erfahrung.

The European journal of cancer : EJC 173, 307-316 (2022). doi:10.1016/j.ejca.2022.07.002, Published by Elsevier, Amsterdam [u.a.]

Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence (XAI) is often suggested as a solution to this problem. We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic evaluations of XAI with dermatologists or dermatopathologists?Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used in various combinations. Only studies concerned with skin cancer were included.37 publications fulfilled our inclusion criteria. Most studies (19/37) simply applied existing XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using CAD systems with XAI.XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking.

European journal of cancer 160, 80-91 (2022). doi:10.1016/j.ejca.2021.10.007, Published by Elsevier, Amsterdam [u.a.]

European journal of cancer 154, 227-234 (2021). doi:10.1016/j.ejca.2021.05.026, Published by Elsevier, Amsterdam [u.a.]

Artificial intelligence (AI) has been successfully applied for automatic tumor detection and grading in histopathological image analysis in urologic oncology. The aim of this review was to assess the applicability of these approaches in image-based oncological outcome prediction.A systematic literature search was conducted using the databases MEDLINE through PubMed and Web of Science up to April 20, 2021. Studies investigating AI approaches to determine the risk of recurrence, metastasis, or survival directly from HE-stained tissue sections in prostate, renal cell or urothelial carcinoma were included. Characteristics of the AI approach and performance metrics were extracted and summarized. Risk of bias (RoB) was assessed using the PROBAST tool.16 studies yielding a total of 6658 patients and reporting on 17 outcome predictions were included. Six studies focused on renal cell, six on prostate and three on urothelial carcinoma while one study investigated renal cell and urothelial carcinoma. Handcrafted feature extraction was used in five, a convolutional neural network (CNN) in six and a deep feature extraction in four studies. One study compared a CNN with handcrafted feature extraction. In seven outcome predictions, a multivariable comparison with clinicopathological parameters was reported. Five of them showed statistically significant hazard ratios for the AI's model's-prediction. However, RoB was high in 15 outcome predictions and unclear in two.The included studies are promising but predominantly early pilot studies, therefore primarily highlighting the potential of AI approaches. Additional well-designed studies are needed to assess the actual clinical applicability.

To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors.Haematoxylin and eosin (HE) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status.With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678-0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05-62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77-56.41%) and 69.65% (95% CI 68.21-71.1%), respectively. These results were confirmed via cross-validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02-1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96-35.7; P0.001) proved to be independent predictors for LNM.In our present study, CNN-based image analyses showed promising results as a potential novel low-cost method to extract relevant prognostic information directly from HE histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.

Das maligne Melanom ist diejenige Form von Hautkrebs, an der die meisten Menschen sterben. Im Fruhstadium ist das Melanom gut behandelbar, eine Fruherkennung ist also lebenswichtig. Kritiker bemangeln, dass seit der bundesweiten Einfuhrung des Hautkrebs-Screenings haufiger Melanome diagnostiziert werden, die Sterblichkeit am malignen Melanom jedoch nicht zuruckgegangen ist. Sie fuhren dies vor allem auf Uberdiagnosen zuruck. Ein Grund ist die zum Teil komplexe Unterscheidung zwischen benignen und malignen Lasionen. Hinzu kommt, dass es auch Ubergangsformen zwischen eindeutig gut- oder bosartigen Lasionen geben kann, und dass einige bosartige Lasionen so wenig aggressiv wachsen, dass sie nie lebensbedrohlich geworden waren. Bisher kann mangels entsprechender Biomarker nicht festgestellt werden, bei welchen Melanomen dies der Fall ist. Auch die Progressionswahrscheinlichkeit eines In-situ-Melanoms zu einem invasiven Tumor kann bisher nicht sicher beurteilt werden. Die Konsequenzen fur uberdiagnostizierte benigne Lasionen sind unnotige psychische und korperliche Belastungen fur die Betroffenen und anfallende Therapiekosten. Umgekehrt konnen Unterdiagnosen zu gravierenden Einschrankungen der Prognose der Betroffenen und zur Notwendigkeit belastender(er) Therapien fuhren. Prazisere Diagnosemoglichkeiten konnten die Anzahl der korrekten Diagnosen erhohen. Hier haben sich in Studien mit auf kunstlicher Intelligenz basierenden Assistenzsystemen bereits erste Erfolge gezeigt, die allerdings noch in die klinische und pathologische Routine ubertragen werden mussen.

Malignant melanoma is the skin tumor that causes most deaths in Germany. At an early stage, melanoma is well treatable, so early detection is essential. However, the skin cancer screening program in Germany has been criticized because although melanomas have been diagnosed more frequently since introduction of the program, the mortality from malignant melanoma has not decreased. This indicates that the observed increase in melanoma diagnoses be due to overdiagnosis, i.e. to the detection of lesions that would never have created serious health problems for the patients. One of the reasons is the challenging distinction between some benign and malignant lesions. In addition, there may be lesions that are biologically equivocal, and other lesions that are classified as malignant according to current criteria, but that grow so slowly that they would never have posed a threat to patient's life. So far, these "indolent" melanomas cannot be identified reliably due to a lack of biomarkers. Moreover, the likelihood that an in-situ melanoma will progress to an invasive tumor still cannot be determined with any certainty. When benign lesions are diagnosed as melanoma, the consequences are unnecessary psychological and physical stress for the affected patients and incurred therapy costs. Vice versa, underdiagnoses in the sense of overlooked melanomas can adversely affect patients' prognoses and may necessitate more intense therapies. Novel diagnostic options could reduce the number of over- and underdiagnoses and contribute to more objective diagnoses in borderline cases. One strategy that has yielded promising results in pilot studies is the use of artificial intelligence-based diagnostic tools. However, these applications still await translation into clinical and pathological routine.

Background Artificial intelligence (AI) has shown potential to improve diagnostics of various diseases, especially for early detection of skin cancer. Studies have yet to investigate the clear application of AI technology in clinical practice or determine the added value for younger user groups. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation of AI into clinical practice, while at the same time, representing the future generation of skin cancer screening participants. Objective We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile apps for skin cancer diagnostics. We evaluated preferences and relative influences of concerns, with a focus on younger age groups. Methods We recruited participants below 35 years of age using three social media channels—Facebook, LinkedIn, and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes toward mobile apps for skin examination. We integrated an adaptive choice-based conjoint to assess participants’ preferences. We evaluated potential concerns using maximum difference scaling. Results We included 728 participants in the analysis. The majority of participants (66.5%, 484/728; 95% CI 0.631-0.699) expressed a positive attitude toward the use of AI-based apps. In particular, participants residing in big cities or small towns (P=.02) and individuals that were familiar with the use of health or fitness apps (P=.02) were significantly more open to mobile diagnostic systems. Hierarchical Bayes estimation of the preferences of participants with a positive attitude (n=484) revealed that the use of mobile apps as an assistance system was preferred. Participants ruled out app versions with an accuracy of ≤65%, apps using data storage without encryption, and systems that did not provide background information about the decision-making process. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information in the decision-making process. Maximum difference scaling analysis for the negative-minded participant group (n=244) showed that data security, insufficient trust in the app, and lack of personal interaction represented the dominant concerns with respect to app use. Conclusions The majority of potential future users below 35 years of age were ready to accept AI-based diagnostic solutions for early detection of skin cancer. However, for translation into clinical practice, the participants’ demands for increased transparency and explainability of AI-based tools seem to be critical. Altogether, digital natives between 18 and 24 years and between 25 and 34 years of age expressed similar preferences and concerns when compared both to each other and to results obtained by previous studies that included other age groups.

Background Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). Objectives The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM). Methods Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set. Results On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage. Conclusion Deep learning–based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.

Background Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence–based diagnostic support systems, in particular convolutional neural network (CNN)–based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology. Methods Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility. Results Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation. Conclusions Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices.

Background One prominent application for deep learning–based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data. Objective The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured. Methods Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it. Results The benchmark contains three data sets—Skin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)—and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations. Conclusions This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers.

Background Studies have shown that artificial intelligence achieves similar or better performance than dermatologists in specific dermoscopic image classification tasks. However, artificial intelligence is susceptible to the influence of confounding factors within images (eg, skin markings), which can lead to false diagnoses of cancerous skin lesions. Image segmentation can remove lesion-adjacent confounding factors but greatly change the image representation. Objective The aim of this study was to compare the performance of 2 image classification workflows where images were either segmented or left unprocessed before the subsequent training and evaluation of a binary skin lesion classifier. Methods Separate binary skin lesion classifiers (nevus vs melanoma) were trained and evaluated on segmented and unsegmented dermoscopic images. For a more informative result, separate classifiers were trained on 2 distinct training data sets (human against machine [HAM] and International Skin Imaging Collaboration [ISIC]). Each training run was repeated 5 times. The mean performance of the 5 runs was evaluated on a multi-source test set (n=688) consisting of a holdout and an external component. Results Our findings showed that when trained on HAM, the segmented classifiers showed a higher overall balanced accuracy (75.6% [SD 1.1%]) than the unsegmented classifiers (66.7% [SD 3.2%]), which was significant in 4 out of 5 runs (P Conclusions Image segmentation does not result in overall performance decrease but it causes the beneficial removal of lesion-adjacent confounding factors. Thus, it is a viable option to address the negative impact that confounding factors have on deep learning models in dermatology. However, the segmentation step might introduce new pitfalls, which require further investigations.

Journal of medical internet research 23(2), e23436 (2021). doi:10.2196/23436, Published by Healthcare World, Richmond, Va.

Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses. Objectives: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone. Methods: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as ‘uncertain’ (CNN output score

Background A basic requirement for artificial intelligence (AI)–based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin cancer screening, should not change the diagnosis of such assistance systems. Objective To quantify to what extent minor image perturbations affect the convolutional neural network (CNN)–mediated skin lesion classification and to evaluate three possible solutions for this problem (additional data augmentation, test-time augmentation, anti-aliasing). Methods We trained three commonly used CNN architectures to differentiate between dermoscopic melanoma and nevus images. Subsequently, their performance and susceptibility to minor changes (‘brittleness’) was tested on two distinct test sets with multiple images per lesion. For the first set, image changes, such as rotations or zooms, were generated artificially. The second set contained natural changes that stemmed from multiple photographs taken of the same lesions. Results All architectures exhibited brittleness on the artificial and natural test set. The three reviewed methods were able to decrease brittleness to varying degrees while still maintaining performance. The observed improvement was greater for the artificial than for the natural test set, where enhancements were minor. Conclusions Minor image changes, relatively inconspicuous for humans, can have an effect on the robustness of CNNs differentiating skin lesions. By the methods tested here, this effect can be reduced, but not fully eliminated. Thus, further research to sustain the performance of AI classifiers is needed to facilitate the translation of such systems into the clinic.

Background: Multiple studies have compared the performance of artificial intelligence (AI)–based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice. Objective: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians. Methods: PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included. Results: A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images. Conclusions: All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice. info:eu-repo/semantics/publishedVersion

Background Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist’s diagnoses. Objective The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image–based discrimination between melanoma and nevus. Methods Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. Results While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists’ average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. Conclusions The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image–based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.

BACKGROUND An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems. OBJECTIVE The objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects. METHODS We trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95% confidence interval of its mean balanced accuracy was above 50.0%. RESULTS A mean balanced accuracy above 50.0% was achieved for all four tasks, even when considering the lower bound of the 95% confidence interval. Performance between tasks showed wide variation, ranging from 56.1% (slide preparation date) to 100% (slide origin). CONCLUSIONS Because all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification.

Journal of the American Academy of Dermatology : JAAD 86(3), 640-642 (2022). doi:10.1016/j.jaad.2021.02.009, Published by Elsevier, Amsterdam [u.a.]

BACKGROUND Studies have shown that artificial intelligence achieves similar or better performance than dermatologists in specific dermoscopic image classification tasks. However, artificial intelligence is susceptible to the influence of confounding factors within images (eg, skin markings), which can lead to false diagnoses of cancerous skin lesions. Image segmentation can remove lesion-adjacent confounding factors but greatly change the image representation. OBJECTIVE The aim of this study was to compare the performance of 2 image classification workflows where images were either segmented or left unprocessed before the subsequent training and evaluation of a binary skin lesion classifier. METHODS Separate binary skin lesion classifiers (nevus vs melanoma) were trained and evaluated on segmented and unsegmented dermoscopic images. For a more informative result, separate classifiers were trained on 2 distinct training data sets (human against machine [HAM] and International Skin Imaging Collaboration [ISIC]). Each training run was repeated 5 times. The mean performance of the 5 runs was evaluated on a multi-source test set (n=688) consisting of a holdout and an external component. RESULTS Our findings showed that when trained on HAM, the segmented classifiers showed a higher overall balanced accuracy (75.6% [SD 1.1%]) than the unsegmented classifiers (66.7% [SD 3.2%]), which was significant in 4 out of 5 runs (PP=.004). CONCLUSIONS Image segmentation does not result in overall performance decrease but it causes the beneficial removal of lesion-adjacent confounding factors. Thus, it is a viable option to address the negative impact that confounding factors have on deep learning models in dermatology. However, the segmentation step might introduce new pitfalls, which require further investigations.

BACKGROUND Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist’s diagnoses. OBJECTIVE The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image–based discrimination between melanoma and nevus. METHODS Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. RESULTS While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists’ average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. CONCLUSIONS The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image–based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.

Warum ich? Fur viele Krebspatienten spielt die Suche nach der Ursache ihrer Krebserkrankung eine wichtige Rolle. Einige versprechen sich von der zukunftigen Risikovermeidung sogar einen Einfluss auf ihre Prognose.

This randomized clinical trial assesses the effect of the free face-aging mobile app Sunface on the skin cancer protection behavior of adolescents in Brazil., Key Points Question Can a face-aging mobile app improve the skin cancer protection behavior of secondary school students? Findings In this cluster-randomized clinical trial of 52 school classes with 1573 Brazilian pupils, meaningful improvements were observed in sunscreen use, tanning behavior, and skin self-examinations 3 to 6 months after an intervention using a face-aging app compared with the nonintervention group. Meaning Face-aging apps may be useful tools to increase skin cancer protection in adolescents and thereby decrease skin cancer risk., Importance Because exposure to UV radiation early in life is an important risk factor for melanoma development, reducing UV exposure in children and adolescents is of paramount importance. New interventions are urgently required. Objective To determine the effect of the free face-aging mobile app Sunface on the skin cancer protection behavior of adolescents. Design, Setting, and Participants This cluster-randomized clinical trial included a single intervention and a 6-month follow-up from February 1 to November 30, 2018. Randomization was performed on the class level in 52 school classes within 8 public secondary schools (grades 9-12) in Itauna, Southeast Brazil. Data were analyzed from May 1 to October 10, 2019. Interventions In a classroom seminar delivered by medical students, adolescents’ selfies were altered by the app to show UV effects on their future faces and were shown in front of their class, accompanied by information about UV protection. Information about relevant parameters was collected via anonymous questionnaires before and 3 and 6 months after the intervention. Main Outcomes and Measures The primary end point of the study was the difference in daily sunscreen use at 6 months of follow-up. Secondary end points included the difference in daily sunscreen use at 3 months of follow-up, at least 1 skin self-examination within 6 months, and at least 1 tanning session in the preceding 30 days. All analyses were predefined and based on intention to treat. Cluster effects were taken into account. Results Participants included 1573 pupils (812 girls [51.6%] and 761 boys [48.4%]; mean [SD] age, 15.9 [1.3] years) from 52 school classes. Daily sunscreen use increased from 110 of 734 pupils (15.0%) to 139 of 607 (22.9%; P

Fortgeschrittene Krebserkrankung, etablierte Behandlungen ausgeschopft – eine schwere Situation fur alle Beteiligten. Oft ist der Wunsch nach weiterer Therapie bei Patienten und Angehorigen gros. Dieses Anliegen nicht anzuerkennen und darauf einzugehen, ware Zuruckweisung. Zudem konnten neue Therapiekonzepte nach sorgfaltiger Nutzen-Risiko-Abwagung fur manche Patienten vielleicht noch eine Chance bieten. Falls die Teilnahme an einer klinischen Studie nicht in Frage kommt, gibt es grundsatzlich die Moglichkeiten Behandlung im Off-label-Use, Teilnahme an einem Hartefallprogramm und individueller Heilversuch.

Viele Krebspatienten, aber auch Verwandte, Freunde und Menschen, die Patienten pflegen, stellen sich haufig die Frage, ob und gegebenenfalls wie man sich beim Umgang miteinander an Krebs anstecken kann. Die Folge kann sein: Korperlicher Kontakt zwischen Ehepartnern, zwischen Groseltern und Enkel, zwischen Freunden wird vermieden.

Krebspatienten wird haufig die Teilnahme an einer klinischen Studie angeboten und sie fragen nun nach den moglichen Vor- und Nachteilen oder Alternativen. Umgekehrt mochten viele Patienten an klinischen Studien teilnehmen, weil sie sich davon eine besser wirksame und/oder nebenwirkungsarmere Behandlung versprechen.

Cancer is characterized by proliferation of cells that have managed to evade central endogenous control mechanisms. Cancers are grouped according to their organ or tissue of origin, but increasingly also based on molecular characteristics of the respective cancer cells. Due to the rapid technological advances of the last years, it is now possible to analyze the molecular makeup of different cancer types in detail within short time periods. The accumulating knowledge about development and progression of cancer can be used to develop more precise diagnostics and more effective and/or less toxic cancer therapies. In the long run, the goal is to offer to every cancer patient a therapeutic regimen that is tailored to his individual disease and situation in an optimal way.

Upon severe DNA damage a cellular signalling network initiates a cell death response through activating tumour suppressor p53 in association with promyelocytic leukaemia (PML) nuclear bodies. The deacetylase Sirtuin 1 (SIRT1) suppresses cell death after DNA damage by antagonizing p53 acetylation. To facilitate efficient p53 acetylation, SIRT1 function needs to be restricted. How SIRT1 activity is regulated under these conditions remains largely unclear. Here we provide evidence that SIRT1 activity is limited upon severe DNA damage through phosphorylation by the DNA damage-responsive kinase HIPK2. We found that DNA damage provokes interaction of SIRT1 and HIPK2, which phosphorylates SIRT1 at Serine 682 upon lethal damage. Furthermore, upon DNA damage SIRT1 and HIPK2 colocalize at PML nuclear bodies, and PML depletion abrogates DNA damage-induced SIRT1 Ser682 phosphorylation. We show that Ser682 phosphorylation inhibits SIRT1 activity and impacts on p53 acetylation, apoptotic p53 target gene expression and cell death. Mechanistically, we found that DNA damage-induced SIRT1 Ser682 phosphorylation provokes disruption of the complex between SIRT1 and its activator AROS. Our findings indicate that phosphorylation-dependent restriction of SIRT1 activity by HIPK2 shapes the p53 response.

HIPK2 activates the apoptotic arm of the DNA damage response by phosphorylating tumor suppressor p53 at serine 46. Unstressed cells keep HIPK2 levels low through targeted polyubiquitination and subsequent proteasomal degradation. Here we identify the LIM domain protein Zyxin as a novel regulator of the HIPK2-p53 signaling axis in response to DNA damage. Remarkably, depletion of endogenous Zyxin, which colocalizes with HIPK2 at the cytoskeleton and in the cell nucleus, stimulates proteasome-dependent HIPK2 degradation. In contrast, ectopic expression of Zyxin stabilizes HIPK2, even upon enforced expression of its ubiquitin ligase Siah-1. Consistently, Zyxin physically interacts with Siah-1, and knock-down of Siah-1 rescues HIPK2 expression in Zyxin-depleted cancer cells. Mechanistically, our data suggest that Zyxin regulates Siah-1 activity through interference with Siah-1 dimerization. Furthermore, we show that endogenous Zyxin coaccumulates with HIPK2 in response to DNA damage in cancer cells, and that depletion of endogenous Zyxin results in reduced HIPK2 protein levels and compromises DNA damage-induced p53 Ser46 phosphorylation and caspase activation. These findings suggest an unforeseen role for Zyxin in DNA damage-induced cell fate control through modulating the HIPK2-p53 signaling axis. Cancer Res; 71(6); 2350–9. ©2011 AACR.