Faculty Bibliography

PURPOSE/OBJECTIVE:To determine the association between labral width as measured on preoperative MRI with hip-specific validated patient self-reported outcomes at a minimum of 2 years follow-up. METHODS:An IRB-approved retrospective review of prospectively gathered hip arthroscopy patients from 2010 to 2017 was performed. Inclusion criteria was defined as patients >18 years old with radiographic evidence of femoroacetabular impingement who underwent a primary labral repair and had a minimum of 2 years clinical follow-up. Exclusion criteria was defined as inadequate preoperative imaging, prior hip surgery, Tonnis grade ≥2 or lateral central edge angle <25 degrees. An a-priori power analysis was performed. MRI measurements of labral width were conducted by two blinded, musculoskeletal fellowship-trained radiologists at standardized "clockface" locations using a previously validated technique. Outcomes were assessed using the Harris Hip Score (HHS), Modified HHS (mHSS), and NonArthritic Hip Score (NAHS). For mHHS, a minimal clinically important difference (MCID) and Patient Acceptable Symptomatic State (PASS) of 8 and 74 were used, respectively. Patients were divided into groups by labral width of < (hypoplastic) and ≥ 1 standard deviation below the mean. Statistical analysis was performed using linear and polynomial regression, Mann-Whitney U, chi-square, Fischer exact, and interclass-correlation coefficients (ICC) testing. RESULTS:=0.26, p<0.001). CONCLUSION/CONCLUSIONS:Hip labral width < 1 standard deviation below the mean measured via preoperative MRI was associated with significantly worse functional outcomes following arthroscopic labral repair and treatment of FAI. The negative relationship between labral width and outcomes may be non-linear.

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.

Background/UNASSIGNED:Orthopedic surgeons use radiographs to determine degrees of fracture healing, guide progression of clinical care, and assist in determining weight bearing and removal of immobilization. However, no gold standard exists to determine the progression of healing of humeral shaft fractures treated non-operatively. Purpose/UNASSIGNED:The purpose of this study was to determine whether a scale comparable to the modified Radiographic Union Score for Tibial (RUST) fractures applied to non-operatively treated humeral shaft fractures can increase interobserver reliability in determining fracture healing. Methods/UNASSIGNED:A retrospective review was undertaken by three orthopedic traumatologists and one musculoskeletal radiologist, who evaluated 50 sets of anteroposterior and lateral radiographs, presented at random, of non-operatively treated humeral shaft fractures at various stages of healing from 17 patients. The radiographs were scored using a modified RUST scale called the Radiographic Humerus Union Measurement (RHUM). Observers were blinded to the time from injury. After a 4-week washout period, observers again scored the same radiographs. Observers classified each fracture as either healed or not healed based on the combination of radiographs. Inter- and intraobserver reliability of the RHUM were determined using an intraclass correlation coefficient (ICC). Interobserver reliability of determining a healed fracture was calculated using Cohen's kappa (κ) statistics. A receiver operator characteristic curve was conducted to determine the RHUM score predictive of a fracture being considered healed. Results/UNASSIGNED:ICC demonstrated almost perfect interobserver reliability (ICC, 0.838; ICC 95% CI, 0.765 to 0.896) and intraobserver reliability (ICC range, 0.822 to 0.948) of the RHUM. κ demonstrated substantial agreement between observers in considering a fracture healed (κ = 0.647). Receiver operating characteristic (ROC) curve demonstrated that a RHUM of 10 or higher is an excellent predictor of the observer considering the fracture healed (area under the ROC curve = 0.946, specificity = 0.957, 95% CI specificity, 0.916 to 0.979). Conclusions/UNASSIGNED:This cortical scoring system has excellent interobserver reliability in humeral shaft fractures treated non-operatively. Consistent with previous cortical scoring systems, a RHUM score of 10 or above can be considered radiographically healed.

PURPOSE/OBJECTIVE:To assess the utility of a 2D dynamic HASTE sequence in assessment of cervical spine flexion-extension, specifically (1) comparing dynamic spondylolisthesis to radiographs and (2) assessing dynamic contact upon or deformity of the cord. METHODS:Patients with a dynamic flexion-extension sagittal 2D HASTE sequence in addition to routine cervical spine sequences were identified. Static and dynamic listhesis was first determined on flexion-extension radiographs reviewed in consensus. Blinded assessment of the dynamic HASTE sequence was independently performed by 2 radiologists for (1) listhesis and translation during flexion-extension and (2) dynamic spinal cord impingement (cord contact or deformity between neutral, flexion and extension). RESULTS:32 scans in 32 patients (9 males, 23 females) met inclusion criteria acquired on 1.5 T (n = 15) and 3 T (n = 17) scanners. The mean acquisition time was 51.8 s (range 20-95 seconds). Dynamic translation was seen in 14 patients on flexion-extension radiographs compared to 12 (reader 1) and 13 (reader 2) patients on HASTE, with 90.6 % agreement (K = 0.83; p = 0.789). In all cases dynamic listhesis was ≤3 mm translation with one patient showing dynamic listhesis in the range 4-6 mm. Four cases (13 %) demonstrated deformity of the cord between flexion-extension, not present in the neutral position. For cord impingement there was strong inter-reader agreement (K = 0.93) and the paired sample Wilcoxon signed rank test found no significant difference between the impingement scores of the two readers (p = 0.787). CONCLUSIONS:A sagittal dynamic flexion-extension HASTE sequence provides a rapid addition to standard MRI cervical spine protocols, which may useful for assessment of dynamic spondylolisthesis and cord deformity.

OBJECTIVES/OBJECTIVE:CT-guided radiofrequency ablation (CT-RFA) is considered to be the gold standard for treatment of osteoid osteoma (OO) yet treatment failures (TFs) continue to be reported. This systematic review was conducted to evaluate factors associated with TF, such as ablation time, lesion location, and patient age as well as evaluating how TF has trended over time. METHODS:Original studies reporting on patients undergoing CT-RFA of OO published between 2002 and 2019 were identified. TF was defined as patients with (1) recurrent or persistent pain +/- (2) imaging evidence of persistent OO. TFs were subdivided into those occurring after the index procedure (primary TF) or those occurring after repeat RFA (secondary TF). Subgroup analysis was performed for TF based on the study date (2002-2010 or 2010-2019), time duration of ablation at 90 °C (6 min or > 6 min), patient age, and tumor location (spinal vs. appendicular). RESULTS:Sixty-nine studies were included for a total of 3023 patients. The global primary TF rate was 8.3% whereas the secondary TF rate was 3.1%. The TF rate reported in studies published after 2011(7%) was about half that during the earlier time period 2002-2010 (14%). There was no statistical difference in TF corrected for age, OO location, or duration of ablation (respectively p = 0.39, 0.13, and 0.23). The global complication rate was 3%, the most frequent being skin burns (n = 24; 0.7%). CONCLUSIONS:A decrease in TF observed between 2011-2019 compared to 2002-2010 may reflect improvements in operator technique or advancements in equipment. Duration of ablation, patient age, or location of OO failed to significantly correlate with TF. KEY POINTS/CONCLUSIONS:• CT-guided radiofrequency ablation of osteoid osteomas is a safe technique with a low rate of treatment failure (8.3% failure rate after the primary radiofrequency reducing to 3.1% following a secondary treatment). • The treatment failure rate has decreased over time, possibly due to an improved understanding of the disease process, better technique, and advances in equipment. • Duration of ablation, patient age, or lesion location did not significantly correlate with treatment failure.

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

Most first-time anterior glenohumeral dislocations occur as the result of trauma. Many patients suffer recurrent episodes of anterior shoulder instability (ASI). The anatomy and biomechanics of ASI is addressed, as is the pathophysiology of capsulolabral injury. The roles of imaging modalities are described, including computed tomography (CT) and MR imaging with the additional value of arthrography and specialized imaging positions. Advances in 3D CT and MR imaging particularly with respect to the quantification of humeral and glenoid bone loss is discussed. The concepts of engaging and nonengaging lesions as well as on-track and off-track lesions are examined.

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.