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Accurate and fast assessment of resection margins is an essential part of a dermatopathologist’s clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have a high probability of exhibiting pathological features in whole slide images (WSI). We focus on detecting basal cell carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues’ exact location on 100 WSI. The rest of the data, with ground-truth sectionwise labels, are used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: (a) deep supervision, (b) linear combination of decoder outputs, and obtain some interpretations about what the network’s decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the Test set.

Background Chronic spontaneous urticaria (CSU) is a debilitating inflammatory skin disease with a prevalence of approximately 1% of the population. It is characterized by recurrent itchy wheals and/or angioedema for more than 6 weeks without known triggers leading to a high quality of life impairment. The pathogenesis of CSU remains not fully understood. Objective This study aimed to explore the pathomechanism of CSU beyond mast cells and IgE‐dependent histamine release and to identify possible biomarkers for the disease and its treatment. Methods We investigated a patient cohort in the first month of omalizumab treatment regarding the IgE levels and changes in gene and miRNA expression in peripheral blood. The cohort was divided into responders and nonresponders (depending on the score of the urticaria control test) and compared to a group of healthy controls. Results Our messenger RNA and microRNA microarray analyses revealed the greatest changes in expression levels on Day 2 after the first omalizumab dose. Conclusion We identified several genes and miRNAs of interest, most of which have not been described to be linked to CSU so far, underlining, for example, to T cell involvement or even suggesting platelet involvement. Chronic spontaneous urticaria is a debilitating inflammatory skin leading to a high quality of life impairment. We investigated a omalizumab treated cohort in regard to changes of gene and miRNA expression in peripheral blood. Our results point to T cell involvement and suggest platelet involvement. Created in BioRender. Hawerkamp, H. (2025). https://BioRender.com/v03d595

As in general pathology, digitalization is also inexorably making its way into dermatopathology. This article examines the current state of digitalization in German dermatopathology laboratories based on the authors' own experiences, the current study situation, and a survey of members of the Dermatological Histology Working Group (ADH). Experiences with the establishment of a digital laboratory workflow, artificial intelligence (AI)-based assistance systems, and whole slide images (WSI)-based training programs are discussed. Digitalization in dermatopathology is an opportunity to simplify and accelerate processes, but there are some hurdles to overcome.

Background: Some of the most common cutaneous neoplasms are Bowen’s disease and seborrheic keratosis, a malignant and a benign proliferation, respectively. These entities represent a significant fraction of a dermatopathologists’ workload, and in some cases, histological differentiation may be challenging. The potential of deep learning networks to distinguish these diseases is assessed. Methods: In total, 1935 whole-slide images from three institutions were scanned on two different slide scanners. A U-Net-based segmentation deep learning algorithm was trained on data from one of the centers to differentiate Bowen’s disease, seborrheic keratosis, and normal tissue, learning from annotations performed by dermatopathologists. Optimal thresholds for the class distinction of diagnoses were extracted and assessed on a test set with data from all three institutions. Results: We aimed to diagnose Bowen’s diseases with the highest sensitivity. A good performance was observed across all three centers, underlining the model’s robustness. In one of the centers, the distinction between Bowen’s disease and all other diagnoses was achieved with an AUC of 0.9858 and a sensitivity of 0.9511. Seborrheic keratosis was detected with an AUC of 0.9764 and a sensitivity of 0.9394. Nevertheless, distinguishing irritated seborrheic keratosis from Bowen’s disease remained challenging. Conclusions: Bowen’s disease and seborrheic keratosis could be correctly identified by the evaluated deep learning model on test sets from three different centers, two of which were not involved in training, and AUC scores > 0.97 were obtained. The method proved robust to changes in the staining solution and scanner model. We believe this demonstrates that deep learning algorithms can aid in clinical routine; however, the results should be confirmed by qualified histopathologists.

Zusammenfassung Wie in der allgemeinen Pathologie hält die Digitalisierung auch in der Dermatopathologie unaufhaltsam ihren Einzug. Dieser Artikel beleuchtet den aktuellen Digitalisierungsstand deutscher dermatopathologischer Labore auf Grundlage eigener Erfahrungen der Autoren, der aktuellen Studienlage sowie einer Umfrage bei den Mitgliedern der Arbeitsgemeinschaft Dermatologische Histologie (ADH). Thematisiert werden Erfahrungen bei der Etablierung eines digitalen Laborablaufs und von Assistenzsystemen, die auf Künstlicher Intelligenz (KI) basieren sowie bei Whole-Slide-Image(WSI)-basierten Fortbildungsprogrammen. Die Digitalisierung in der Dermatopathologie ist eine Chance, Prozesse zu vereinfachen und zu beschleunigen. Es gilt jedoch, einige Hürden zu überwinden.

Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine. In this work, we successfully develop a deep learning method to assist the pathologists by marking critical regions that have a high probability of exhibiting pathological features in Whole Slide Images (WSI). We focus on detecting Basal Cell Carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues' exact location on 100 WSI. The rest of the data, with ground-truth section-wise labels, is used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: a) deep supervision, b) linear combination of decoder outputs, and obtain some interpretations about what the network's decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the test set.

The cementless, hydroxyapatite-coated Anatomique Benoist Giraud-I (ABG-I) hip endoprosthesis represented a modern implant in the 1990s. The aim of the current retrospective study was to evaluate the clinical and radiological results of this prosthesis. In addition, an analysis of the complications and retrieved implants was conducted. The medium-term results (follow-up 5.23 years) of 193 hip joints are presented. Of 158 total cohorts, 81.9% was able to undergo follow-up performed with standardized clinical and radiological investigations. Physical characteristics of the patients and the underlying disease prompting the need for total hip arthroplasty, as well as a clinical score (Merle d'Aubigné) were recorded. At the time of follow-up, a radiologic examination of all patients with a standardized evaluation was performed. In addition, the migration of the acetabular cup and femoral head as well as polyethylene wear could be determined digitally in 118 cases (61.1%) using one-picture Roentgen analysis. Clinical results, as measured with a Merle d'Aubigné Score increase from 8.4 to 16.2, were very good. Radiographs demonstrated successful osseous integration of the anatomically molded shaft. Within the period of the investigation, no revision procedures of the femoral shaft were necessary. However, the rate of polyethylene abrasion of 0.23 mm/year was markedly high. 13.9% of hips (n = 27) required acetabular cup revision due to wear. This calculates to a prosthesis 7-year survival probability of 63%. Intraoperative findings during the revision cases showed extensive periacetabular osteolysis with foreign body granulation tissue. Analysis of data from the total patient cohort versus data from cases requiring revision showed a significantly increased frequency of high polyethylene wear in young active patients as well as in cases where an unfavorable acetabular cup to femoral head relation existed in correspondence with polyethylene thickness. There is evidence, however, that suggests that multifactorial causes for the increased wear are significant in regards to the principal material and technical features of the prosthesis. On the basis of these results, it is strongly recommended that all patients treated with an ABG-I hip endoprosthesis should receive close clinical and most importantly close radiologic follow-up.