An uncommon cause of primary hyperparathyroidism is a cystic parathyroid adenoma. This paper describes two patients with hypercalcemia and right knee disease. Their serum calcium concentration was high, phosphorus concentration was low, and parathyroid hormone (PTH) concentration was high. Ultrasound and computed tomography scans of the neck indicated a cystic mass near the thyroid. Parathyroid scintigraphy showed no focal uptake in one patient and low tracer concentration in the cystic mass in the other patient. Following resection of the cystic masses, both were pathologically confirmed to be a cystic parathyroid adenoma with predominantly cystic degeneration. The calcium and PTH concentrations gradually decreased to the reference range. Both patients were stable at their last follow-up. The diagnosis of a functional cystic parathyroid adenoma is highly challenging because of the different clinical manifestations and negative result on parathyroid tracer scintigraphy. For patients with high serum calcium and PTH concentrations and a cystic mass in the neck, resection of the mass and subsequent postoperative pathological diagnosis is necessary even if the clinical diagnosis of a parathyroid adenoma cannot be confirmed preoperatively. Decreases in the PTH and serum calcium concentrations indicate successful resection of a functional parathyroid adenoma.

Bone age assessment plays a critical role in the investigation of endocrine, genetic, and growth disorders in children. This process is usually conducted manually, with some drawbacks, such as reliance on the pediatrician’s experience and extensive labor, as well as high variations among methods. Most deep learning models use one neural network to extract the global information from the whole input image, ignoring the local details that doctors care about. In this paper, we propose a global-local feature fusion convolutional neural network, including a global pathway to capture the global contextual information and a local pathway to extract the fine-grained information from local patches. The fine-grained information is integrated into the global context information layer-by-layer to assist in predicting bone age. We evaluated the proposed method on a dataset with 11,209 X-ray images with an age range of 4–18 years. Compared with other state-of-the-art methods, the proposed global-local network reduces the mean absolute error of the estimated ages to 0.427 years for males and 0.455 years for females; the average accuracy rate is within 6 months and 12 months, reaching 70% and 91%, respectively. In addition, the effectiveness and rationality of the model were verified on a public dataset.