Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study.

Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.

Heparin-resistance in AL amyloidosis: a case report.

BACKGROUND: Non-AT-III mediated heparin-resistance during CPB occurs by complex-forming with heparin-binding proteins. Currently, there are no specific recommendations for non-AT-III mediated heparin-resistance. CASE PRESENTATION: We present a fatal case of a 70-yr-old male-patient undergoing cardiac-surgery in which refractory heparin-resistance was observed. The massive AL amyloidosis found at autopsy is thought to be responsible and illustrates that awareness and knowledge of the etiology and perioperative strategies of non-AT-III mediated heparin-resistance is important. CONCLUSION: For anticoagulation during cardiopulmonary bypass surgery in case of a non-AT-III medicated heparin resistance, we refer to the decision tree added to this manuscript and if necessary to consider direct thrombin inhibitors, such as bivalirudin or argatroban, as it bypasses the complexing pathway.