[Endometrial osteoid metaplasia]. / L'image du mois. Métaplasie ostéoïde de l'endomètre.

Endometrial osteoid metaplasia is a rare condition characterised by the presence of bone in the uterine cavity. The main symptoms are secondary infertility, cycle disorders or dysmenorrhea. The discovery can also be fortuitous. Ultrasound and diagnostic hysteroscopy are diagnostic tools while operative hysteroscopy provides treatment. In the vast majority of cases, it is discovered in the aftermath of pregnancy, especially after curettage or miscarriage. There are several theories as to its etiopathogenesis.

La métaplasie ostéoïde est définie par la présence de tissu osseux au niveau de l'endomètre. Il s'agit d'une affection rare. Les principaux symptômes sont l'infertilité secondaire, des troubles du cycle ou de la dysménorrhée. La découverte peut également être fortuite. L'échographie et l'hystéroscopie diagnostique sont des outils de diagnostic tandis que l'hystéroscopie opératoire assure le traitement. Dans la grande majorité des cas, elle est découverte dans les suites d'une grossesse, surtout après un curetage ou une fausse-couche. Il existe plusieurs théories quant à son étiopathogénie.

Osteoid Metaplasia in Femoral Artery Plaques Is Associated With the Clinical Severity of Lower Extremity Artery Disease in Men.

Lamellar metaplastic bone, osteoid metaplasia (OM), is found in atherosclerotic plaques, especially in the femoral arteries. In the carotid arteries, OM has been documented to be associated with plaque stability. This study investigated the clinical impact of OM load in femoral artery plaques of patients with lower extremity artery disease (LEAD) by using a deep learning-based image analysis algorithm. Plaques from 90 patients undergoing endarterectomy of the common femoral artery were collected and analyzed. After decalcification and fixation, 4-µm-thick longitudinal sections were stained with hematoxylin and eosin, digitized, and uploaded as whole-slide images on a cloud-based platform. A deep learning-based image analysis algorithm was trained to analyze the area percentage of OM in whole-slide images. Clinical data were extracted from electronic patient records, and the association with OM was analyzed. Fifty-one (56.7%) sections had OM. Females with diabetes had a higher area percentage of OM than females without diabetes. In male patients, the area percentage of OM inversely correlated with toe pressure and was significantly associated with severe symptoms of LEAD including rest pain, ulcer, or gangrene. According to our results, OM is a typical feature of femoral artery plaques and can be quantified using a deep learning-based image analysis method. The association of OM load with clinical features of LEAD appears to differ between male and female patients, highlighting the need for a gender-specific approach in the study of the mechanisms of atherosclerotic disease. In addition, the role of plaque characteristics in the treatment of atherosclerotic lesions warrants further consideration in the future.