Utilization trends for MR arthrography of the hip and shoulder: a retrospective cross-sectional analysis of 20-year data from a tertiary care academic medical center.

OBJECTIVE: The purpose of this study is to analyze changes in the utilization of MRA of the hip and shoulder at a large tertiary care academic medical center during a period of significant technological advancements over the last 20 years. MATERIALS AND METHODS: This retrospective cross-sectional analysis identified MRA of the hip and shoulder performed at our institution over a 20-year period (2/2003-2/2023) in relation to the total number of MR hip and shoulder examinations during the same period. Patient characteristics and referring provider demographic information were extracted. Descriptive statistics and trend analysis were performed. RESULTS: The total number of MRIs of the hip and shoulder increased overall, with small dips in 2020 and 2022. MRA of the hip increased significantly over the first 10 years of the study period (p = 0.0005), while MRA of the shoulder did not change significantly (p = 0.33). The proportion of both MRA of the hip and shoulder declined over the last 10 years (hip, p = 0.0056; shoulder, p = 0.0017). Over the same period, there was significant increase in the proportion of examinations performed at 3 Tesla versus 1.5 (p < 0.0001). CONCLUSION: Overall, there was a downward trend in MR shoulder and hip arthrogram utilization in the second half of this 20-year study period. However, utilization varied somewhat by referring specialties and credentials. These changes are likely reflective of both improvements in image quality and evolving practice recommendations. Awareness of such trends may be valuable in ensuring appropriate patient care, as well as for anticipating the needs of a musculoskeletal radiology practice.

A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT.

BACKGROUND: Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional deep learning) algorithm to detect lytic lesions on CTs could improve the value of these CTs for myeloma imaging. Our objectives were to develop a DL algorithm and determine its performance at detecting lytic lesions of multiple myeloma. METHODS: Axial slices (2-mm section thickness) from whole-body low-dose CT scans of subjects with biochemically confirmed plasma cell dyscrasias were included in the study. Data were split into train and test sets at the patient level targeting a 90%/10% split. Two musculoskeletal radiologists annotated lytic lesions on the images with bounding boxes. Subsequently, we developed a two-step deep learning model comprising bone segmentation followed by lesion detection. Unet and "You Look Only Once" (YOLO) models were used as bone segmentation and lesion detection algorithms, respectively. Diagnostic performance was determined using the area under the receiver operating characteristic curve (AUROC). RESULTS: Forty whole-body low-dose CTs from 40 subjects yielded 2193 image slices. A total of 5640 lytic lesions were annotated. The two-step model achieved a sensitivity of 91.6% and a specificity of 84.6%. Lesion detection AUROC was 90.4%. CONCLUSION: We developed a deep learning model that detects lytic bone lesions of multiple myeloma on whole-body low-dose CTs with high performance. External validation is required prior to widespread adoption in clinical practice.