Cooled Radiofrequency Ablation for Chronic Joint Pain Secondary to Hip and Shoulder Osteoarthritis.

Osteoarthritis (OA) of the shoulder and hip is a leading cause of physical disability and mental distress. Traditional nonsurgical management alone is often unable to completely address the associated chronic joint pain. Moreover, a large number of patients are not eligible for joint replacement surgery owing to comorbidities or cost. Radiofrequency ablation (RFA) of articular sensory nerve fibers can disrupt the transmission of nociceptive signals by neurolysis, thereby providing long-term pain relief. A subtype of RFA, cooled RFA (CRFA), utilizes internally cooled electrodes to generate larger ablative zones compared with standard RFA techniques. Given the complex variable innervation of large joints such as the glenohumeral and hip joints, a larger ablative treatment zone, such as that provided by CRFA, is desired to capture a greater number of afferent nociceptive fibers. The suprascapular, axillary, and lateral pectoral nerve articular sensory branches are targeted during CRFA of the glenohumeral joint. The obturator and femoral nerve articular sensory branches are targeted during CRFA of the hip. CRFA is a promising tool in the interventionalist's arsenal for management of OA-related pain and symptoms, particularly in patients who cannot undergo, have long wait times until, or have persistent pain following joint replacement surgery. An invited commentary by Tomasian is available online. ©RSNA, 2022.

Ultrasound Evaluation for Extremity Deep Vein Thrombus in COVID-19-Positive Patients: Exam Positivity Rate and Association With D-Dimer Level.

OBJECTIVES: Determine the rate of positive extremity ultrasound exams for DVT in patients with COVID-19 and assess for differences in laboratory values in patients with and without DVT, which could be used as a surrogate to decide the need for further evaluation with ultrasound. METHODS: Retrospective case control study with 1:2 matching of cases (COVID-19+ patients) to controls (COVID-19- patients) based on age, gender, and race. Laboratory values assessed were serum D-dimer, fibrinogen, prothrombin time, international normalized ratio, and C-reactive protein. Demographic variables, comorbidities, and clinical variables including final disposition were also evaluated. P-values for categorical variables were calculated with the chi-square test or Fisher's exact test. P-values for continuous variables were compared with the use of a two-tailed unpaired t-test. RESULTS: The rate of extremity ultrasound exams positive for DVT were similar in patients with (14.7%) and without (19.3%) COVID-19 (P = .423). No significant difference was observed in laboratory values including the D-dimer level in COVID-19 patients without (mean 9523.9 ng/mL (range 339 to >60,000)) or with DVT (mean 13,663.7 ng/mL (range 1193->60,000)) (P = .475). No differences were found in demographic variabilities or co-morbidities among COVID-19 patients with and without extremity DVT. CONCLUSIONS: We found no statistically significant difference in rate of positive DVT studies between COVID-19+ and COVID-19- patients. D-dimer levels are elevated, in some cases markedly, in COVID-19 patients with and without DVTs and therefore these data do not support their use as a surrogate when assessing the need for ultrasound evaluation.

A Fusion NLP Model for the Inference of Standardized Thyroid Nodule Malignancy Scores from Radiology Report Text.

Radiology reports are a rich resource for advancing deep learning applications for medical images, facilitating the generation of large-scale annotated image databases. Although the ambiguity and subtlety of natural language poses a significant challenge to information extraction from radiology reports. Thyroid Imaging Reporting and Data Systems (TI-RADS) has been proposed as a system to standardize ultrasound imaging reports for thyroid cancer screening and diagnosis, through the implementation of structured templates and a standardized thyroid nodule malignancy risk scoring system; however there remains significant variation in radiologist practice when it comes to diagnostic thyroid ultrasound interpretation and reporting. In this work, we propose a computerized approach using a contextual embedding and fusion strategy for the large-scale inference of TI-RADS final assessment categories from narrative ultrasound (US) reports. The proposed model has achieved high accuracy on an internal data set, and high performance scores on an external validation dataset.