ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation.

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance.

Predictive factors of radioiodine therapy failure in Graves' Disease: A meta-analysis.

BACKGROUND: I-131 therapy is a common treatment modality for adults with Graves' Disease (GD). Utilizing meta-analysis, we examined patient specific factors that predict I-131 therapy failure. METHODS: Literature search followed PRISMA. Comprehensive Meta-analysis (version 3.0) was used. Mantel-Haenszel test with accompanying risk ratio and confidence intervals evaluated categorical variables. Continuous data was analyzed using inverse variance testing yielding mean difference or standardized mean difference. Decision tree algorithms identified variables of high discriminative performance. RESULTS: 4822 collective patients across 18 studies were included. Male sex (RR = 1.23, 95%CI = 1.08-1.41, p = 0.002), I-131 therapy 6 months after GD diagnosis (RR = 2.10, 95%CI = 1.45-3.04, p < 0.001) and history of anti-thyroid drugs (RR = 2.05, 95%CI = 1.49-2.81, p < 0.001) increased the risk of I-131 therapy failure. Elevated free thyroxine, 24-h radioactive iodine uptake scan ≥60.26% and thyroid volume ≥35.77 mL were also associated with failure. CONCLUSION: Patient characteristics can predict the likelihood of I-131 therapy failure in GD. Definitive surgical treatment may be a reasonable option for those patients.