3-Dimensional Reconstruction From Histopathological Sections: A Systematic Review.

Although pathological tissue analysis is typically performed on single 2-dimensional (2D) histologic reference slides, 3-dimensional (3D) reconstruction from a sequence of histologic sections could provide novel opportunities for spatial analysis of the extracted tissue. In this review, we analyze recent works published after 2018 and report information on the extracted tissue types, the section thickness, and the number of sections used for reconstruction. By analyzing the technological requirements for 3D reconstruction, we observe that software tools exist, both free and commercial, which include the functionality to perform 3D reconstruction from a sequence of histologic images. Through the analysis of the most recent works, we provide an overview of the workflows and tools that are currently used for 3D reconstruction from histologic sections and address points for future work, such as a missing common file format or computer-aided analysis of the reconstructed model.

Automated curation of large-scale cancer histopathology image datasets using deep learning.

BACKGROUND: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient. METHODS: We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. RESULTS: In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. CONCLUSION: Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.

A photoaging smartphone application to promote sun safety behaviors among youth in late childhood and adolescence.

BACKGROUND: Ultraviolet (UV)-exposure behaviors can directly impact an individual's skin cancer risk, with many habits formed during childhood and adolescence. We explored the utility of a photoaging smartphone application to motivate youth to improve sun safety practices. METHODS: Participants completed a preintervention survey to gather baseline sun safety perceptions and behaviors. Participants then used a photoaging mobile application to view the projected effects of chronic UV exposure on participants' self-face image over time, followed by a postintervention survey to assess motivation to engage in future sun safety practices. RESULTS: The study sample included 87 participants (median [interquartile (IQR)] age, 14 [11-16] years). Most participants were White (50.6%) and reported skin type that burns a little and tans easily (42.5%). Preintervention sun exposure behaviors among participants revealed that 33 (37.9%) mostly or always used sunscreen on a sunny day, 48 (55.2%) experienced at least one sunburn over the past year, 26 (30.6%) engaged in outdoor sunbathing at least once during the past year, and zero (0%) used indoor tanning beds. Non-skin of color (18 [41.9%], p = .02) and older (24 [41.4%], p = .007) participants more often agreed they felt better with a tan. Most participants agreed the intervention increased their motivation to practice sun-protective behaviors (wear sunscreen, 74 [85.1%]; wear hats, 64 [74.4%]; avoid indoor tanning, 73 [83.9%]; avoid outdoor tanning, 68 [79%]). CONCLUSION: The findings of this cross-sectional study suggest that a photoaging smartphone application may serve as a useful tool to promote sun safety behaviors from a young age.

A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma.

PURPOSE: To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC). METHODS: Digitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS. Kaplan-Meier for univariable evaluation and Cox regression analyses for multivariable evaluation of the DINO-ViT risk groups were performed for prediction of OS and DSS. For validation, a cohort from a tertiary care centre was used. RESULTS: A significant risk stratification was achieved in univariable analysis for OS and DSS in the training (n = 443, log rank test, p < 0.01) and validation set (n = 266, p < 0.01). In multivariable analysis, including age, metastatic status, tumour size and grading, the DINO-ViT risk stratification was a significant predictor for OS (hazard ratio [HR] 3.03; 95%-confidence interval [95%-CI] 2.11-4.35; p < 0.01) and DSS (HR 4.90; 95%-CI 2.78-8.64; p < 0.01) in the training set but only for DSS in the validation set (HR 2.31; 95%-CI 1.15-4.65; p = 0.02). DINO-ViT visualisation showed that features were mainly extracted from nuclei, cytoplasm, and peritumoural stroma, demonstrating good interpretability. CONCLUSION: The DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future.

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology.

Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness. To address these issues, we present a new method for simultaneous tumor detection and prediction of genetic alterations: The Slide-Level Assessment Model (SLAM) uses a single off-the-shelf neural network to predict molecular alterations directly from routine pathology slides without any manual annotations, improving upon previous methods by automatically excluding normal and non-informative tissue regions. SLAM requires only standard programming libraries and is conceptually simpler than previous approaches. We have extensively validated SLAM for clinically relevant tasks using two large multicentric cohorts of colorectal cancer patients, Darmkrebs: Chancen der Verhütung durch Screening (DACHS) from Germany and Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR-BCIP) from the UK. We show that SLAM yields reliable slide-level classification of tumor presence with an area under the receiver operating curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; n = 2,297 tumor and n = 1,281 normal slides). In addition, SLAM can detect microsatellite instability (MSI)/mismatch repair deficiency (dMMR) or microsatellite stability/mismatch repair proficiency with an AUROC of 0.909 (0.888, 0.929; n = 2,039 patients) and BRAF mutational status with an AUROC of 0.821 (0.786, 0.852; n = 2,075 patients). The improvement with respect to previous methods was validated in a large external testing cohort in which MSI/dMMR status was detected with an AUROC of 0.900 (0.864, 0.931; n = 805 patients). In addition, SLAM provides human-interpretable visualization maps, enabling the analysis of multiplexed network predictions by human experts. In summary, SLAM is a new simple and powerful method for computational pathology that could be applied to multiple disease contexts. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer.

The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end-to-end deep learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population-based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67-0.758) and patients with any LNM with an AUROC of 0.711 (0.597-0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644-0.778) and 0.567 (0.542-0.597), respectively. Based on these findings, we used the deep learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and deep learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our deep learning algorithm is publicly available and can be used for biomarker discovery in any disease setting. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

[The Rise of Artificial Intelligence - High Prediction Accuracy in Early Detection of Pigmented Melanoma]. / Künstliche Intelligenz auf dem Vormarsch ­ Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome.

The incidence of malignant melanoma is increasing worldwide. If detected early, melanoma is highly treatable, so early detection is vital.Skin cancer early detection has improved significantly in recent decades, for example by the introduction of screening in 2008 and dermoscopy. Nevertheless, in particular visual detection of early melanomas remains challenging because they show many morphological overlaps with nevi. Hence, there continues to be a high medical need to further develop methods for early skin cancer detection in order to be able to reliably diagnosemelanomas at a very early stage.Routine diagnostics for melanoma detection include visual whole body inspection, often supplemented by dermoscopy, which can significantly increase the diagnostic accuracy of experienced dermatologists. A procedure that is additionally offered in some practices and clinics is wholebody photography combined with digital dermoscopy for the early detection of malignant melanoma, especially for monitoring high-risk patients.In recent decades, numerous noninvasive adjunctive diagnostic techniques were developed for the examination of suspicious pigmented moles, that may have the potential to allow improved and, in some cases, automated evaluation of these lesions. First, confocal laser microscopy should be mentioned here, as well as electrical impedance spectroscopy, multiphoton laser tomography, multispectral analysis, Raman spectroscopy or optical coherence tomography. These diagnostic techniques usually focus on high sensitivity to avoid malignant melanoma being overlooked. However, this usually implies lower specificity, which may lead to unnecessary excision of benign lesions in screening. Also, some of the procedures are time-consuming and costly, which also limits their applicability in skin cancer screening. In the near future, the use of artificial intelligence might change skin cancer diagnostics in many ways. The most promising approach may be the analysis of routine macroscopic and dermoscopic images by artificial intelligence.For the classification of pigmented skin lesions based on macroscopic and dermoscopic images, artificial intelligence, especially in form of neural networks, has achieved comparable diagnostic accuracies to dermatologists under experimental conditions in numerous studies. In particular, it achieved high accuracies in the binary melanoma/nevus classification task, but it also performed comparably well to dermatologists in multiclass differentiation of various skin diseases. However, proof of the basic applicability and utility of such systems in clinical practice is still pending. Prerequisites that remain to be established to enable translation of such diagnostic systems into dermatological routine are means that allow users to comprehend the system's decisions as well as a uniformly high performance of the algorithms on image data from other hospitals and practices.At present, hints are accumulating that computer-aided diagnosis systems could provide their greatest benefit as assistance systems, since studies indicate that a combination of human and machine achieves the best results. Diagnostic systems based on artificial intelligence are capable of detecting morphological characteristics quickly, quantitatively, objectively and reproducibly, and could thus provide a more objective analytical basis - in addition to medical experience.

Deep learning detects genetic alterations in cancer histology generated by adversarial networks.

Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barriers impede sharing of images and genetic data, hampering development of new algorithms for detection of MSI and other biomarkers. We hypothesized that histology images synthesized by conditional generative adversarial networks (CGANs) retain information about genetic alterations. To test this, we developed a 'histology CGAN' which was trained on 256 patients (training cohort 1) and 1457 patients (training cohort 2). The CGAN synthesized 10 000 synthetic MSI and non-MSI images which contained a range of tissue types and were deemed realistic by trained observers in a blinded study. Subsequently, we trained a deep learning detector of MSI on real or synthetic images and evaluated the performance of MSI detection in a held-out set of 142 patients. When trained on real images from training cohort 1, this system achieved an area under the receiver operating curve (AUROC) of 0.742 [0.681, 0.854]. Training on the larger cohort 2 only marginally improved the AUROC to 0.757 [0.707, 0.869]. Training on purely synthetic data resulted in an AUROC of 0.743 [0.658, 0.801]. Training on both real and synthetic data further increased AUROC to 0.777 [0.715, 0.821]. We conclude that synthetic histology images retain information reflecting underlying genetic alterations in colorectal cancer. Using synthetic instead of real images to train deep learning systems yields non-inferior classifiers. This approach can be used to create large shareable data sets or to augment small data sets with rare molecular features. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer.

OBJECTIVE: 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. PATIENTS AND METHODS: Haematoxylin and eosin (H&E) 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. RESULTS: 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; P < 0.001) proved to be independent predictors for LNM. CONCLUSION: 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 H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.

Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study.

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<.001). The overall balanced accuracy was numerically higher for the unsegmented ISIC classifiers (78.3% [SD 1.8%]) than for the segmented ISIC classifiers (77.4% [SD 1.5%]), which was significantly different in 1 out of 5 runs (P=.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.

Salivary cortisol levels and anxiety in melanoma patients undergoing sentinel lymph node excision under local anesthesia versus general anesthesia: a prospective study.

BACKGROUND: Sentinel lymph node excision (SLNE) can be performed in tumescent local anesthesia (TLA) or general anesthesia (GA). Perioperative cortisol level changes and anxiety are common in surgical interventions and might be influenced by the type of anesthesia. In this study, we intended to determine whether the type of anesthesia impacts the patients' perioperative levels of salivary cortisol (primary outcome) and the feeling of anxiety evaluated by psychological questionnaires (secondary outcome). METHODS: All melanoma patients of age undergoing SLNE at the University Hospital Essen, Germany, could be included in the study. Exclusion criteria were patients' intake of glucocorticoids or psychotropic medication during the former 6 months, pregnancy, age under 18 years, and BMI ≥ 30 as salivary cortisol levels were reported to be significantly impacted by obesity and might confound results. RESULTS: In total, 111 melanoma patients undergoing SLNE were included in our prospective study between May 2011 and April 2017 and could choose between TLA or GA. Salivary cortisol levels were measured three times intraoperatively, twice on the third and second preoperative day and twice on the second postoperative day. To assess anxiety, patients completed questionnaires (Hospital Anxiety and Depression Scale (HADS), State-Trait Anxiety Inventory (STAI)) perioperatively. Patients of both groups exhibited comparable baseline levels of cortisol and perioperative anxiety levels. Independent of the type of anesthesia, all patients showed significantly increasing salivary cortisol level from baseline to 30 min before surgery (T3) (TLA: t = 5.07, p < 0.001; GA: t = 3.09, p = 0.006). Post hoc independent t tests showed that the TLA group exhibited significantly higher cortisol concentrations at the beginning of surgery (T4; t = 3.29, p = 0.002) as well as 20 min after incision (T5; t = 277, p = 0.008) compared to the GA group. CONCLUSIONS: The type of anesthesia chosen for SLNE surgery significantly affects intraoperative cortisol levels in melanoma patients. Further studies are mandatory to evaluate the relevance of endogenous perioperative cortisol levels on the postoperative clinical course. TRIAL REGISTRATION: German Clinical Trials Register DRKS00003076, registered 1 May 2011.

Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

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.

[External scientific evaluation of the first teledermatology app without direct patient contact in Germany (Online Dermatologist-AppDoc)]. / Externe wissenschaftliche Evaluation der ersten Teledermatologie-App ohne direkten Patientenkontakt in Deutschland ("Online Hautarzt ­ AppDoc").

BACKGROUND: Teledermatology addresses the problems associated with the lack of specialists and the often long waiting time for an appointment with a dermatologist. The research project Online Dermatologist-AppDoc enables a fast anonymous expert opinion and was approved on 22 October 2018 by the Landesärztekammer Baden-Württemberg for 2 years as a model project. OBJECTIVES: The aim of the present work is the presentation of the first real healthcare data for German teledematology within the framework of the external quality assurance of the model project Online Dermatologist-AppDoc. MATERIALS AND METHODS: Anonymous data records submitted to Online Dermatologist-AppDoc between 21 November 2018 and 1 August 2019 were analyzed qualitatively and quantitatively at the Department of Dermatology of the University Hospital Essen. In addition to the evaluation of the data records submitted so far, 100 cases submitted underwent a second assessment by a board-certified dermatologist to assess concordance. RESULTS: A total of 1364 cases (60.4% men, 39.6% women) were included in the current first external scientific evaluation. In 90.3% of the cases, remote diagnosis was possible. The two most frequent diagnoses were different forms of eczema (n = 270) and nevi (n = 163). Almost two thirds of the patients (64.3%) could be treated teledermatologically only. The random second examination of 100 cases resulted in an agreement of the diagnosis including the differential diagnosis/diagnoses in 97% of the cases. CONCLUSIONS: The first external scientific evaluation of the teledermatological model project Online Dermatologist-AppDoc indicates that the reduction of spatial and temporal barriers of a dermatological examination as well as the teledermatological triage have been so far successful.

Overdiagnosis of melanoma - causes, consequences and solutions.

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.

Loss of p53 compensates osteopenia in murine Mysm1 deficiency.

Histone modifications critically contribute to the epigenetic orchestration of bone homeostasis-in part, by modifying the access of transcription factors to specific genes involved in the osteogenic differentiation process of bone marrow mesenchymal stem cells (MSCs) and osteoblasts. Based on our previous finding that histone H2A deubiquitinase 2A-DUB/Mysm1 interacts with the p53 axis in hematopoiesis and tissue development, we analyzed the molecular basis of the skeletal phenotype of Mysm1-deficient mice and dissected the underlying p53-dependent and -independent mechanisms. Visible morphologic, skeletal deformations of young Mysm1-deficient mice-including a kinked and truncated tail and shortened long bones-were associated with osteopenia of long bones. On the cellular level, Mysm1-deficient primary osteoblasts displayed reduced potential to differentiate into mature osteoblasts, as indicated by decreased expression of osteogenic markers. Reduced osteogenic differentiation capacity of Mysm1-deficient osteoblasts was accompanied by an impaired induction of osteogenic transcription factor Runx2. Osteogenic differentiation of Mysm1-/- MSCs, however, was not compromised in vitro. In line with defective hematopoietic development of Mysm1-deficient mice, Mysm1-/- osteoclasts had reduced resorption activity and were more prone to apoptosis in TUNEL assays. Skeletal alterations and osteopenia of Mysm1-deficient mice were phenotypically completely rescued by simultaneous ablation of p53 in p53-/-Mysm1-/- double-deficient mice-although p53 deficiency did not restore Runx2 expression in Mysm1-/- osteoblasts on the molecular level but, instead, enhanced proliferation and osteogenic differentiation of MSCs. In summary, our results demonstrate novel roles for Mysm1 in osteoblast differentiation and osteoclast formation, resulting in osteopenia in Mysm1-deficient mice that could be abrogated by the loss of p53 from increased osteogenic differentiation of Mysm1-/-p53-/- MSCs.-Haffner-Luntzer, M., Kovtun, A., Fischer, V., Prystaz, K., Hainzl, A., Kroeger, C. M., Krikki, I., Brinker, T. J., Ignatius, A., Gatzka, M. Loss of p53 compensates osteopenia in murine Mysm1 deficiency.

Otto Mennicke (1876-) and the first description of skull base anomalies causing cerebellar tonsillar ectopia: one of the first mentions of the Chiari I malformation.

INTRODUCTION: Although Hans Chiari made significant and meaningful contributions to our understanding and classification of hindbrain herniations, others have also contributed to this knowledge. One figure who has been lost to history is Otto Mennicke. Herein, we discuss his role in our understanding of tonsillar ectopia and his life and connection to Hans Chiari. CONCLUSIONS: Our knowledge of what is now known as the Chiari malformations has been shaped by several clinicians including Otto Mennicke.