Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91-0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85-0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35-0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92-0.97] to its use as assistance system for physicians. In subgroup analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient Jutzi et al. Patients' View on AI-Based Diagnosis data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.

Background: Multiple studies have compared the performance of artificial intelligence (AI)ebased models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.

Promyelocytic leukemia nuclear bodies (PML NBs) are dynamic macromolecular multiprotein complexes that recruit and release a plethora of proteins. A considerable number of PML NB components play vital roles in apoptosis, senescence regulation and tumour suppression. The molecular basis by which PML NBs control these cellular responses is still just beginning to be understood. In addition to PML itself, numerous further tumour suppressors including transcriptional regulator p53, acetyl transferase CBP (CREB binding protein) and protein kinase HIPK2 (homeodomain interacting protein kinase 2) are recruited to PML NBs in response to genotoxic stress or oncogenic transformation and drive the senescence and apoptosis response by regulating p53 activity. Moreover, in response to death-receptor activation, PML NBs may act as nuclear depots that release apoptotic factors, such as the FLASH (FLICE-associated huge) protein, to amplify the death signal. PML NBs are also associated with other nuclear domains including Cajal bodies and nucleoli and share apoptotic regulators with these domains, implying crosstalk between NBs in apoptosis regulation. In conclusion, PML NBs appear to regulate cell death decisions through different, pathway-specific molecular mechanisms.

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. Ó2011 AACR.

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.

Summary 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 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.

Background: Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence ebased diagnostic support systems, in particular convolutional neural network (CNN)ebased 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 CNNbased 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.