White matter changes in young and middle-aged males with chronic prostatitis/chronic pelvic pain syndrome: Tract-based spatial statistics analysis.

The supraspinal mechanism plays a key role in developing and maintaining chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). However, it is not clear how white matter changes in young and middle-aged males with CP/CPPS. In this cross-sectional study, 23 CP/CPPS patients and 22 healthy controls (HCs) were recruited. Tract-based spatial statistics was applied to investigate the differences in diffusion tensor imaging metrics, including fractional anisotropy (FA), mean diffusion (MD), radial diffusion (RD) and axial diffusion (AD), between CP/CPPS patients and HCs. The study also examined the association between white matter alterations and clinical variables in patients using correlation analysis. Compared with HCs, patients showed decreased FA, MD, RD and AD in the body and genu of the corpus callosum and right anterior corona radiata. In addition, they showed increased FA along with decreased MD, RD and AD in the left posterior limb of the internal capsule (PLIC-L), left external capsule and left cerebral peduncle. The FA of PLIC-L was negatively correlated with disease duration (r = -.54, corrected p = .017), while MD and RD were positively correlated (r = .45, corrected p = .042; r = .57, corrected p = .017). These results suggest that CP/CPPS is associated with extensive changes in white matter tracts, which are involved in pain processing. In particular, the FA, MD and RD values in the PLIC-L were correlated with the disease duration, indicating that the long-term course of CP/CPPS may have effects on the white matter microstructure of the pain perception pathways.

The functional connectivity of the basal ganglia subregions changed in mid-aged and young males with chronic prostatitis/chronic pelvic pain syndrome.

Background: The Basal ganglia (BG) played a crucial role in the brain-level mechanisms of chronic pain disorders. However, the functional changes of BG in chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) are still poorly understood. This study investigated the BG subregions' resting-state functional connectivity (rs-FC) in CP/CPPS patients compared with healthy controls. Methods: Twenty eight patients with CP/CPPS and 28 age- and education-matched healthy males underwent clinical measurements and 3T brain MR imaging, including T1-weighted structural images and resting-state functional imaging. The data were analyzed by the seeded-based rs-FC analysis. Then, a machine learning method was applied to assess the feasibility of detecting CP/CPPS patients through the changed rs-FC. Results: Compared with healthy males, patients presented decreased rs-FC between the BG subregions and right middle cingulate cortex, and correlated with pain (r = 0.51, p-uncorrected = 0.005) and urinary symptoms (r = -0.4, p-uncorrected = 0.034). The left superior temporal gyrus and right supramarginal gyrus showed decreased rs-FC with the BG subregions as well. The area under the receiver operating characteristic curve of 0.943 (accuracy = 80%, F1-score = 80.6%) was achieved for the classification of CP/CPPS patients and healthy males with support vector machine (SVM) based on the changed rs-FC. Conclusion: These findings provide evidence of altered BG subregions' rs-FC in CP/CPPS, which may contribute to our understanding of the BG's role in CP/CPPS.

Application value of a deep learning method based on a 3D V-Net convolutional neural network in the recognition and segmentation of the auditory ossicles.

Objective: To explore the feasibility of a deep learning three-dimensional (3D) V-Net convolutional neural network to construct high-resolution computed tomography (HRCT)-based auditory ossicle structure recognition and segmentation models. Methods: The temporal bone HRCT images of 158 patients were collected retrospectively, and the malleus, incus, and stapes were manually segmented. The 3D V-Net and U-Net convolutional neural networks were selected as the deep learning methods for segmenting the auditory ossicles. The temporal bone images were randomized into a training set (126 cases), a test set (16 cases), and a validation set (16 cases). Taking the results of manual segmentation as a control, the segmentation results of each model were compared. Results: The Dice similarity coefficients (DSCs) of the malleus, incus, and stapes, which were automatically segmented with a 3D V-Net convolutional neural network and manually segmented from the HRCT images, were 0.920 ± 0.014, 0.925 ± 0.014, and 0.835 ± 0.035, respectively. The average surface distance (ASD) was 0.257 ± 0.054, 0.236 ± 0.047, and 0.258 ± 0.077, respectively. The Hausdorff distance (HD) 95 was 1.016 ± 0.080, 1.000 ± 0.000, and 1.027 ± 0.102, respectively. The DSCs of the malleus, incus, and stapes, which were automatically segmented using the 3D U-Net convolutional neural network and manually segmented from the HRCT images, were 0.876 ± 0.025, 0.889 ± 0.023, and 0.758 ± 0.044, respectively. The ASD was 0.439 ± 0.208, 0.361 ± 0.077, and 0.433 ± 0.108, respectively. The HD 95 was 1.361 ± 0.872, 1.174 ± 0.350, and 1.455 ± 0.618, respectively. As these results demonstrated, there was a statistically significant difference between the two groups (P < 0.001). Conclusion: The 3D V-Net convolutional neural network yielded automatic recognition and segmentation of the auditory ossicles and produced similar accuracy to manual segmentation results.