Using deep learning models to analyze the cerebral edema complication caused by radiotherapy in patients with intracranial tumor.

Using deep learning models to analyze patients with intracranial tumors, to study the image segmentation and standard results by clinical depiction complications of cerebral edema after receiving radiotherapy. In this study, patients with intracranial tumors receiving computer knife (CyberKnife M6) stereotactic radiosurgery were followed using the treatment planning system (MultiPlan 5.1.3) to obtain before-treatment and four-month follow-up images of patients. The TensorFlow platform was used as the core architecture for training neural networks. Supervised learning was used to build labels for the cerebral edema dataset by using Mask region-based convolutional neural networks (R-CNN), and region growing algorithms. The three evaluation coefficients DICE, Jaccard (intersection over union, IoU), and volumetric overlap error (VOE) were used to analyze and calculate the algorithms in the image collection for cerebral edema image segmentation and the standard as described by the oncologists. When DICE and IoU indices were 1, and the VOE index was 0, the results were identical to those described by the clinician.The study found using the Mask R-CNN model in the segmentation of cerebral edema, the DICE index was 0.88, the IoU index was 0.79, and the VOE index was 2.0. The DICE, IoU, and VOE indices using region growing were 0.77, 0.64, and 3.2, respectively. Using the evaluated index, the Mask R-CNN model had the best segmentation effect. This method can be implemented in the clinical workflow in the future to achieve good complication segmentation and provide clinical evaluation and guidance suggestions.

The development of Taiwan Fracture Liaison Service network.

Osteoporosis and its associated fragility fractures are becoming a severe burden in the healthcare system globally. In the Asian-Pacific (AP) region, the rapidly increasing in aging population is the main reason accounting for the burden. Moreover, the paucity of quality care for osteoporosis continues to be an ongoing challenge. The Fracture Liaison Service (FLS) is a program promoted by International Osteoporosis Foundation (IOF) with a goal to improve quality of postfracture care and prevention of secondary fractures. In this review article, we would like to introduce the Taiwan FLS network. The first 2 programs were initiated in 2014 at the National Taiwan University Hospital and its affiliated Bei-Hu branch. Since then, the Taiwan FLS program has continued to grow exponentially. Through FLS workshops promoted by the Taiwanese Osteoporosis Association (TOA), program mentors have been able to share their valuable knowledge and clinical experience in order to promote establishments of additional programs. With 22 FLS sites including 11 successfully accredited on the best practice map, Taiwan remains as one of the highest FLS coverage countries in the AP region, and was also granted the IOF Best Secondary Fracture Prevention Promotion award in 2017. Despite challenges faced by the TOA, we strive to promote more FLS sites in Taiwan with a main goal of ameliorating further health burden in managing osteoporotic patients.