Faculty Bibliography

Musculoskeletal injections serve a variety of diagnostic and therapeutic purposes, with ultrasonography (US) guidance having many advantages: no ionizing radiation, real-time guidance, high spatial resolution, excellent soft tissue contrast, and the ability to identify and avoid critical structures. Sonography can be cost effective and afford flexibility in resource-constrained settings. This article describes US-guided musculoskeletal injections relevant to many radiology practices and provides experience-based suggestions. Structures covered include multiple joints (shoulder, hip), bursae (iliopsoas, subacromial-subdeltoid, greater trochanteric), peripheral nerves (sciatic, radial), and tendon sheaths (posterior tibial, peroneal, flexor hallucis longus, Achilles, long head of the biceps). Trigger point and similar targeted steroid injections, as well as calcific tendinopathy barbotage, are also described.

PURPOSE/OBJECTIVE:To describe our experience using ultrasound (US) to evaluate postoperative complications in the presence of in situ shoulder arthroplasty. METHODS:Review of patients who underwent US evaluation following total shoulder arthroplasty (TSA), reverse total shoulder arthroplasty (RTSA) or hemiarthroplasty from 2007 to 2020. All studies were reviewed independently by two musculoskeletal radiologists to assess for joint effusion, periarticular collection, and characterization of associated rotator cuff tears. Tendon tears were assessed with respect to (1) thickness: low grade (<50% thickness), high grade (>50% thickness), full thickness; (2) morphology (focal vs. diffuse) and location (insertion vs. critical zone). Inter-reader agreements were determined using Cohen's kappa test. RESULTS:Ninety-seven studies were performed in 72 patients following TSA, RTSA, or hemiarthroplasty. Thirty-seven exams were solely for diagnostic purposes, and 59 were for guiding joint or periarticular collection aspiration. Twenty-eight studies assessed the cuff tendons post TSA. The mean time between surgery and US examination was 29.2 months. Complete or high-grade tears were identified in 8/28 (28.6%) diagnostic exams. The most commonly torn tendon among TSA patients was the subscapularis, with 13/28 (46.4%) demonstrating at least partial tearing. Inter-reader agreement was excellent for presence of effusion (k = 0.79, p < .001) and periarticular collection (k = 0.87, p < .001), and excellent agreement for presence of subscapularis tear (k = 0.78, p < .001), with fair agreement for assessment of supraspinatus (k = 0.66, p < .001) and infraspinatus (k = 0.60, p < .001) tears. CONCLUSION/CONCLUSIONS:The most commonly torn tendon following anatomic TSA identified by US was the subscapularis, which was torn or deficient in 46.4% of cases. The majority of studies were performed for the guidance of percutaneous aspiration.

OBJECTIVE:To determine whether the healing response in rotator cuff repairs can be quantitatively characterized using a multimodality imaging approach with MR signal intensity, power Doppler and shear wave elastography (SWE). MATERIALS AND METHODS/METHODS:Patients scheduled for rotator cuff repair were prospectively enrolled between September 2013 and June 2016. A 12 patient cohort with unilateral, full-thickness, supraspinatus tendon tears underwent MRI and ultrasound both preoperatively and postoperatively (at 3 and 6 months post-surgery). The MR signal intensity ratio of tendon-to-deltoid muscle (TMR), vascularity score by power Doppler (PD) and shear wave velocity (SWV) were measured. Repaired and asymptomatic control shoulders were compared over time and between modalities. RESULTS:TMR and vascularity of the tendon repair initially increased and then decreased postoperatively. Although not achieving statistical significance, postoperative SWV initially decreased and later increased, which negatively correlated with the TMR at 3 months (r = -0.73, p = 0.005). PD demonstrated a statistically significant change in tendon vascularity over time compared to the contralateral control (p = 0.009 at 3 months; p = 0.036 at 6 months). No significant correlation occurred between TMR and SWE at 6 months, or with PD at any time point. CONCLUSION/CONCLUSIONS:Despite a small patient cohort, this prospective pilot study suggests a temporal relationship of MRI and ultrasound parameters that parallels the expected phases of healing in the repaired rotator cuff.

OBJECTIVES/OBJECTIVE:To evaluate whether a follow-up magnetic resonance imaging (MRI) scan performed after initial ultrasound (US) to evaluate soft tissue mass (STM) lesions of the musculoskeletal system provides additional radiologic diagnostic information and alters clinical management. METHODS:A retrospective chart review was performed of patients undergoing initial US evaluations of STMs of the axial or appendicular skeleton between November 2012 and March 2019. Patients who underwent US examinations followed by MRI for the evaluation of STM lesions were identified. For inclusion, the subsequent pathologic correlation was required from either a surgical or image-guided biopsy. Imaging studies with pathologic correlations were then reviewed by 3 musculoskeletal radiologists, who were blinded to the pathologic diagnoses. The diagnostic utility of MRI was then assessed on the basis of a 5-point grading scale, and inter-reader agreements were determined by the Fleiss κ statistic. RESULTS:Ninety-two patients underwent MRI after US for STM evaluations. Final pathologic results were available in 42 cases. Samples were obtained by surgical excision or open biopsy (n = 34) or US-guided core biopsy (n = 8). The most common pathologic diagnoses were nerve sheath tumors (n = 9), lipomas (n = 5), and leiomyomas (n = 5). Imaging review showed that the subsequent MRI did not change the working diagnosis in 73% of cases, and the subsequent MRI was not considered to narrow the differential diagnosis in 68% of cases. There was slight inter-reader agreement for the diagnostic utility of MRI among individual cases (κ = 0.10) between the 3 readers. CONCLUSIONS:The recommendation of MRI to further evaluate STM lesions seen with US frequently fails to change the working diagnosis or provide significant diagnostic utility.

Purpose/UNASSIGNED:To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses. Materials and Methods/UNASSIGNED:= 227) were used to train and evaluate a CNN model to distinguish malignant and benign lesions. Twenty percent of cases were withheld as a test dataset, and the remaining cases were used to train the model with a 75%-25% training-validation split and fourfold cross-validation. Performance of the model was compared with retrospective interpretation of the same dataset by two experienced musculoskeletal radiologists, blinded to clinical history. A second group of US images from 275 of the 419 patients containing the three common benign masses was used to train and evaluate a separate model to differentiate between the masses. The models were trained on the Keras machine learning platform (version 2.3.1), with a modified pretrained VGG16 network. Performance metrics of the model and of the radiologists were compared by using the McNemar test, and 95% CIs for performance metrics were estimated by using the Clopper-Pearson method (accuracy, recall, specificity, and precision) and the DeLong method (area under the receiver operating characteristic curve). Results/UNASSIGNED:The model trained to classify malignant and benign masses demonstrated an accuracy of 79% (95% CI: 68, 88) on the test data, with an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.84, 0.98), matching the performance of two expert readers. Performance of the model distinguishing three benign masses was lower, with an accuracy of 71% (95% CI: 61, 80) on the test data. Conclusion/UNASSIGNED:The trained CNN was capable of differentiating between benign and malignant soft-tissue masses depicted on US images, with performance matching that of two experienced musculoskeletal radiologists.© RSNA, 2020.

Ultrasound-guided hookwire localization was initially introduced to facilitate the excision of nonpalpable breast lesions by guiding surgical exploration, thereby reducing operative time and morbidity. The same technique has since found utility in a range of other applications outside breast and can be useful within the musculoskeletal system. Despite this, there remains limited literature with respect to its technical aspects and practical utility. We describe our technique and a series of preoperative ultrasound-guided wire localizations in the musculoskeletal system to assist surgical excision of 4 soft tissue masses.

Purpose: Ultrasound is increasingly utilized as the first-line diagnostic evaluation of superficial soft tissue masses. With growing health care costs, there is increasing pressure to develop cost-effective methods to triage patients with palpable masses. Deep convolutional neural networks (CNNs) have demonstrated the ability to classify images with good accuracy. We hypothesize that using a limited dataset, a CNN can be trained to classify benign versus malignant soft tissue masses seen on ultrasound.
Material(s) and Method(s): Ultrasound exams from 227 patients were selected with up to two pairs of gray scale and Doppler images extracted per patient. Pairs of gray scale and Doppler images were concatenated to create a single image for a total of 344 combined images. Images from 49 patients (96 images) were withheld for a pathology enriched test set (56 benign and 40 malignant). The remaining 248 images were used to train a CNN using an 80/20 training-validation split with five-fold crossvalidation. The model was trained on Keras using a pretrained VGG-16 architecture on a Nvidia GTX 1070 GPU. The withheld test set was used for a reader study which consists of two experienced musculoskeletal radiologists to assess the performance of the model.
Result(s): The CNN achieved an average accuracy of 0.87+/-0.07 on fivefold cross validation. The best performing model in the five folds was selected for comparison against two musculoskeletal radiologists on the pathology enriched test data set. The model achieved an accuracy 0.73 on the test data set and an AUC of 0.78 which was comparable to the performance of the two musculoskeletal radiologists (0.76 and 0.65 accuracy).
Conclusion(s): Using a relatively small data set, a CNN can be trained to differentiate between benign and malignant soft tissue masses seen on ultrasound with its performance approaching that of two experienced musculoskeletal radiologists