Feasibility of a standardized ultrasound examination in patients with rheumatoid arthritis: A quality improvement among rheumatologists cohort.

BACKGROUND: Quality improvement is important to facilitate valid patient outcomes. Standardized examination procedures may improve the validity of US. The aim of this study was to investigate the learning progress for rheumatologists during training of US examination of the hand in patients with rheumatoid arthritis (RA). METHODS: Rheumatologists with varying degrees of experience in US were instructed by skilled tutors. The program consisted of two days with hands-on training followed by personal US examinations performed in their individual clinics. Examinations were sent to the tutors for quality control. The US examinations were evaluated according to a scoring sheet containing 144 items. An acceptable examination was defined as > 80% correct scores. RESULTS: Thirteen rheumatologists participated in the study. They included a total of 104 patients with RA. Only few of the initial examinations were scored below 80%, and as experience increased, the scores improved (p = 0.0004). A few participants displayed decreasing scores. The mean time spent performing the standardized examination procedure decreased from 34 min to less than 10 minutes (p = 0.0001). CONCLUSION: With systematic hands-on training, a rheumatologist can achieve a high level of proficiency in the conduction of US examinations of the joints of the hand in patients with RA. With experience, examination time decreases, while the level of correctness is maintained. The results indicate that US may be applied as a valid measurement tool suitable for clinical practice and in both single- and multi-centre trials

An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy

BackgroundAdaptive MRI-guided radiotherapy (MRIgRT) requires accurate and efficient segmentation of organs and targets on MRI scans. Manual segmentation is time-consuming and variable, while deformable image registration (DIR)-based contour propagation may not account for large anatomical changes. Therefore, we developed and evaluated an automatic segmentation method using the nnU-net framework.MethodsThe network was trained on 38 patients (76 scans) with localized prostate cancer and tested on 30 patients (60 scans) with localized prostate, metastatic prostate, or bladder cancer treated at a 1.5 T MRI-linac at our institution. The performance of the network was compared with the current clinical workflow based on DIR. The segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) metrics.ResultsThe trained network successfully segmented all 600 structures in the test set. High similarity was obtained for most structures, with 90% of the contours having a DSC above 0.9 and 86% having an MSD below 1 mm. The largest discrepancies were found in the sigmoid and colon structures. Stratified analysis on cancer type showed that the best performance was seen in the same type of patients that the model was trained on (localized prostate). Especially in patients with bladder cancer, the performance was lower for the bladder and the surrounding organs. A complete automatic delineation workflow took approximately 1 minute. Compared with contour transfer based on the clinically used DIR algorithm, the nnU-net performed statistically better across all organs, with the most significant gain in using the nnU-net seen for organs subject to more considerable volumetric changes due to variation in the filling of the rectum, bladder, bowel, and sigmoid.ConclusionWe successfully trained and tested a network for automatically segmenting organs and targets for MRIgRT in the male pelvis region. Good test results were seen for the trained nnU-net, with test results outperforming the current clinical practice using DIR-based contour propagation at the 1.5 T MRI-linac. The trained network is sufficiently fast and accurate for clinical use in an online setting for MRIgRT. The model is provided as open-source