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A 12-year-old adolescent was diagnosed with a right-sided solid mass in the round ligament of the uterus. The chief complaints were abdominal pain and pelvic discomfort. She underwent laparoscopic tumor resection. Histological examination demonstrated a trabecular growth pattern of epithelioid cells with mitotic activity (3 per 50 HPF), which expressed melanocytic and myoid markers. Due to aforementioned findings, a final diagnosis of perivascular epithelioid cell tumor (PEComa) with uncertain malignant potential was made. To the best of our knowledge, this localization of PEComa is considered to be infrequent with only occasionally reported cases.

H-score is a semi-quantitative method used to assess the presence and distribution of proteins in tissue samples by combining the intensity of staining and the percentage of stained nuclei. It is widely used but time-consuming and can be limited in terms of accuracy and precision. Computer-aided methods may help overcome these limitations and improve the efficiency of pathologists’ workflows. In this work, we developed a model EndoNet for automatic H-score calculation on histological slides. Our proposed method uses neural networks and consists of two main parts. The first is a detection model which predicts the keypoints of centers of nuclei. The second is an H-score module that calculates the value of the H-score using mean pixel values of predicted keypoints. Our model was trained and validated on 1780 annotated tiles with a shape of 100 × 100 µm and we achieved 0.77 mAP on a test dataset. We obtained our best results in H-score calculation; these results proved superior to QuPath predictions. Moreover, the model can be adjusted to a specific specialist or whole laboratory to reproduce the manner of calculating the H-score. Thus, EndoNet is effective and robust in the analysis of histology slides, which can improve and significantly accelerate the work of pathologists.