Minimally invasive autopsies for the investigation of pulmonary pathology of COVID-19-experiences of a longitudinal series of 92 patients.

Minimally invasive autopsies (MIAs) allow the collection of tissue samples for diagnostic and research purposes in special situations, e.g., when there is a high risk of infection which is the case in the context of COVID-19 or restrictions due to legal or personal reasons. We performed MIA to analyze lung tissue from 92 COVID-19 patients (mean age 78 years; range 48-98; 35 women, 57 men), representing 44% of all patients who died from the disease between October 2020 and April 2021. An intercostal approach was used with removal of a 5-cm rib section followed by manual collection of four lung tissue samples (5-8 cm in size). Diffuse alveolar damage (DAD) was found in 89 (97%) patients at various stages. Exudative DAD (eDAD) predominated in 18 (20%) patients, proliferative DAD (pDAD) in 43 (47%) patients, and mixed DAD (mDAD) in 31 (34%) patients. There were no significant differences in the predominant DAD pattern between tissue samples from the same patient. Additional purulent components were present in 46 (50%) cases. Fungi were detected in 11 (12%) patients. The pDAD pattern was associated with longer hospital stay including intensive care unit (p=0.026 and p<0.001) and younger age (p=0.019). Positive bronchoalveolar lavage and blood cultures were observed more frequently in pDAD patterns (p<0.001; p=0.018). In contrast, there was no significant association between intravital positive microbiological results and superimposed bronchopneumonia or fungal infection at autopsy. Having demonstrated the characteristic lung changes in a large longitudinal autopsy series, we conclude that the presented MIA approach can be considered a reliable and safe method for performing post mortem lung diagnostics in COVID-19 and other high-risk situations. The lack of correlation between histological changes indicative of bacterial or fungal superinfection and microbiology could have clinical implications for disease and treatment surveillance.

Bilateral Proximal Tibiofibular Synostosis Caused by Osteochondroma in a 21-Year-Old Highly Active Male-First in Literature.

Background and Objectives: Up until now, only one case of unilateral proximal tibiofibular synostosis caused by osteochondroma has been reported. This report is the first well-documented bilateral case of proximal tibiofibular synostosis caused by an osteochondroma. Case Report: A 21-year-old, highly active male patient with bilateral proximal tibiofibular synostosis caused by an osteochondroma suffering from persistent knee pain is presented. As conservative methods had failed, the patient was treated by bilateral open resection of the connecting bone. Histopathological findings confirmed the preoperative diagnosis. The patient returned to sports three weeks after surgery and continued soccer training six weeks after surgery. Discussion: The case report presents the successful treatment of a bilateral proximal tibiofibular synostosis caused by an osteochondroma by bilateral open resection of the connecting bone.

Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns.

Recent advances in artificial intelligence, particularly in the field of deep learning, have enabled researchers to create compelling algorithms for medical image analysis. Histological slides of basal cell carcinomas (BCCs), the most frequent skin tumor, are accessed by pathologists on a daily basis and are therefore well suited for automated prescreening by neural networks for the identification of cancerous regions and swift tumor classification.In this proof-of-concept study, we implemented an accurate and intuitively interpretable artificial neural network (ANN) for the detection of BCCs in histological whole-slide images (WSIs). Furthermore, we identified and compared differences in the diagnostic histological features and recognition patterns relevant for machine learning algorithms vs. expert pathologists.An attention-ANN was trained with WSIs of BCCs to identify tumor regions (n = 820). The diagnosis-relevant regions used by the ANN were compared to regions of interest for pathologists, detected by eye-tracking techniques.This ANN accurately identified BCC tumor regions on images of histologic slides (area under the ROC curve: 0.993, 95% CI: 0.990-0.995; sensitivity: 0.965, 95% CI: 0.951-0.979; specificity: 0.910, 95% CI: 0.859-0.960). The ANN implicitly calculated a weight matrix, indicating the regions of a histological image that are important for the prediction of the network. Interestingly, compared to pathologists' eye-tracking results, machine learning algorithms rely on significantly different recognition patterns for tumor identification (p < 10-4).To conclude, we found on the example of BCC WSIs, that histopathological images can be efficiently and interpretably analyzed by state-of-the-art machine learning techniques. Neural networks and machine learning algorithms can potentially enhance diagnostic precision in digital pathology and uncover hitherto unused classification patterns.

Accuracy of grading pancreatic neuroendocrine neoplasms with Ki-67 index in fine-needle aspiration cellblock material.

OBJECTIVE: The aim of this study was to assess the preoperative tumour grade of pancreatic neuroendocrine neoplasms (panNENs) by determining the Ki-67 index in endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) material and to correlate the preoperative tumour grade with the postoperative tumour grade in surgical specimens. METHODS: We performed a retrospective review of the institutional pathology database over a 10-year period (2007-2017) to identify all cases of panNENs with corresponding preoperative EUS-FNA cytological material and surgical specimens. Fifteen cases with adequate EUS-FNA material (more than 400 tumour cells on cellblock) were identified. The cytological and histological samples were graded based on the mitotic rate and the Ki-67 index in accordance with the 2017 World Health Organisation grading system for panNENs. The tumour grades determined on EUS-FNA cellblock material were compared with the histological tumour grades. RESULTS: Mean age at diagnosis was 64.8 ± 12.7 years (range, 38-85 years). The grading scores assigned to the cytological and histological samples were concordant in all 15 (100%) cases. Of those, two (13%) cases were scored as grade 1, nine (60%) cases as grade 2 and four (27%) cases as grade 3 tumours. CONCLUSION: Our study shows that tumour grade in patients with PanNENs can be reliably determined by assessing the Ki-67 index in EUS-FNA specimens based on the 2017 World Health Organisation classification and grading system.