Faculty Bibliography

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

Adult T cell leukemia/lymphoma (ATLL) is a mature T cell neoplasm caused by the human oncogenic retrovirus human T lymphotropic virus type-1 (HTLV-1). While several musculoskeletal manifestations have been described in ATLL, skeletal muscle involvement is unusual, with only four cases reported in the English-language literature. We present a rare case of ATLL manifesting as an intra-muscular calf mass in a 58-year-old man who immigrated to the USA from West Africa. While skeletal muscle involvement by lymphoma is uncommon, it remains important to consider within the differential diagnosis when there are suggestive imaging findings because it entails important technical biopsy considerations as well as treatment implications. This case report also raises awareness of ATLL presenting outside of typical HTLV-1 endemic areas, related to current population migration patterns. ATLL should therefore be considered in patients with appropriate risk factors.

OBJECTIVE:The purpose of this study was to determine whether SWE can detect biomechanical changes in the supraspinatus muscle that occur with increasing supraspinatus tendon abnormality prior to morphologic gray-scale changes. MATERIALS AND METHODS/METHODS:An IRB approved, HIPAA compliant retrospective study of shoulder ultrasounds from 2013-2018 was performed. The cohort consisted of 88 patients (mean age 55 ± 15 years old) with 110 ultrasounds. Images were acquired in longitudinal orientation to the supraspinatus muscle with shear wave velocity (SWV) point quantification. The tendon and muscle were graded in order of increasing tendinosis/tear (1-4 scale) and increasing fatty infiltration (0-3 scale). Mixed model analysis of variance, analysis of covariance, and Spearman rank correlation were used for statistical analysis. RESULTS:There was no statistically significant age or sex dependence for supraspinatus muscle SWV (p = 0.314, 0.118, respectively). There was no significant correlation between muscle SWV and muscle or tendon grade (p = 0.317, 0.691, respectively). In patients with morphologically normal muscle on gray-scale ultrasound, there were significant differences in muscle SWV when comparing tendon grade 3 with grades 1, 2, and 4 (p = 0.018, 0.025, 0.014, respectively), even when adjusting for gender and age (p = 0.044, 0.028, 0.018, respectively). Pairwise comparison of tendon grades other than those mentioned did not achieve statistical significance (p > 0.05). CONCLUSION/CONCLUSIONS:SWE can detect biomechanical differences within the supraspinatus muscle that are not morphologically evident on gray-scale ultrasound. Specifically, supraspinatus tendon partial tears with moderate to severe tendinosis may correspond to biomechanically distinct muscle properties compared to both lower grades of tendon abnormality and full-thickness tears.

RATIONALE AND OBJECTIVES/OBJECTIVE:The medial plantar proper digital nerve, also called Joplin's nerve, arises from the medial plantar nerve, courses along the medial hallux metatarsophalangeal joint, and can be a source of neuropathic pain due to various etiologies, following acute injury including bunion surgery and repetitive microtrauma. We describe our clinical experience with diagnostic ultrasound assessment of Joplin's neuropathy and technique for ultrasound-guided therapeutic intervention including both injection and cryoablation over a 6-year period. MATERIALS AND METHODS/METHODS:Retrospective review of all diagnostic studies performed for Joplin's neuropathy and therapeutic Joplin's nerve ultrasound-guided injections and cryoablations between 2012 and 2018 was performed. Indications for therapeutic injection and cryoablation, were recorded. Studies were assessed for sonographic abnormalities related to the nerve and perineural soft tissues. Post-treatment outcomes including immediate pain scores, clinical follow-up, and periprocedural complications were documented. RESULTS:Twenty-four ultrasound-guided procedures were performed, including 15 perineural injections and nine cryoablations. With respect to sonographic abnormalities, nerve thickening (33%) and perineural hypoechoic scar tissue (27%) were the most common findings. The mean pain severity score prior to the therapeutic injection was 6.4/10 (range 4-10) and 0.25/10 (range 0-2) following the procedure; mean follow-up was 26.2 months (range 3-63 months). All of the cryoablation patients experienced sustained pain relief with a mean length follow-up of 3.75 months (range 0.2-10 months). CONCLUSION/CONCLUSIONS:Therapeutic injection of Joplin's nerve is a safe and easily performed procedure under ultrasound guidance, with high rates of immediate symptom improvement. For those experiencing a relapse or recurrent symptoms, cryoablation offers an effective secondary potential treatment option.

Our aim was to test the effectiveness of ultrasound-guided intra-articular (IA) injection into the knee joint of rodents by an inexperienced operator compared with standard landmark-guided IA injections by a trained injector. Fifty landmark-guided and 46 ultrasound-guided IA injections in 49 rats were analyzed. Animal positioning and injection protocol were designed for use with the ultrasound system. Injection delivery was verified with a secondary imaging modality. We compared the success of IA injections by method (landmark and ultrasound-guided), adjusting for all other confounding factors (age, weight, experience, laterality and presence of surgery). Ultrasound-guided injections had higher success rates overall (89% vs. 58%) and helped to reduce the number of failed attempts per injection. None of the cofounding factors influenced the success of injection. In conclusion, we found higher accuracy for ultrasound-guided IA injection delivery than the traditional landmark-based injection method and also the technical feasibility for untrained personnel.