Ultrasonographic differential diagnosis of medial elbow pain.

Medial elbow pain is a common musculoskeletal problem among individuals engaging in repetitive activities. Medial epicondylitis is the predominant cause of this pain. However, other potential causes must be considered as part of the differential diagnosis. This article discusses several etiologies of medial elbow pain, including medial epicondylitis, ulnar neuropathy, snapping triceps syndrome, ulnar collateral ligament injury, medial antebrachial cutaneous neuropathy, and diseases of the elbow joint, with an emphasis on ultrasound (US) findings. Awareness of possible diagnoses and their US features can assist radiologists in establishing a comprehensive diagnosis for medial elbow pain.

Development of a deep learning-based fully automated segmentation of rotator cuff muscles from clinical MR scans.

BACKGROUND: The fatty infiltration and atrophy in the muscle after a rotator cuff (RC) tear are important in surgical decision-making and are linked to poor clinical outcomes after rotator cuff repair. An accurate and reliable quantitative method should be developed to assess the entire RC muscles. PURPOSE: To develop a fully automated approach based on a deep neural network to segment RC muscles from clinical magnetic resonance imaging (MRI) scans. MATERIAL AND METHODS: In total, 94 shoulder MRI scans (mean age = 62.3 years) were utilized for the training and internal validation datasets, while an additional 20 MRI scans (mean age = 62.6 years) were collected from another institution for external validation. An orthopedic surgeon and a radiologist manually segmented muscles and bones as reference masks. Segmentation performance was evaluated using the Dice score, sensitivities, precision, and percent difference in muscle volume (%). In addition, the segmentation performance was assessed based on sex, age, and the presence of a RC tendon tear. RESULTS: The average Dice score, sensitivities, precision, and percentage difference in muscle volume of the developed algorithm were 0.920, 0.933, 0.912, and 4.58%, respectively, in external validation. There was no difference in the prediction of shoulder muscles, with the exception of teres minor, where significant prediction errors were observed (0.831, 0.854, 0.835, and 10.88%, respectively). The segmentation performance of the algorithm was generally unaffected by age, sex, and the presence of RC tears. CONCLUSION: We developed a fully automated deep neural network for RC muscle and bone segmentation with excellent performance from clinical MRI scans.

Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study.

OBJECTIVE: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. MATERIALS AND METHODS: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. RESULTS: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. CONCLUSION: The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.

[Imaging Evaluation of Early and Long-Term Complications Associated with the Postoperative Spine]. / 척추 ìˆ˜ìˆ í›„ 영상 평가: 초기 및 중장기 합병증.

As the number of spinal surgeries being performed expands, the number of medical imaging procedures such as radiography, CT, and MRI is also increasing, and the importance of their interpretation is becoming more significant. Herein, we present the radiological findings of a variety of complications that can occur after spinal surgery and discuss how effectively and accurately they can be diagnosed through imaging. In particular, this study details the characteristic imaging findings specific to the early and long-term postoperative periods. Early complications of spinal surgery include improper placement of surgical instruments (instrument malpositioning), seromas, hematomas, pseudomeningoceles, and infections in the region surrounding the surgical site. Conversely, long-term complications may include osteolysis around surgical instruments, failure of fusion, adjacent segment disease, and the formation of epidural fibrosis or scar tissue. A precise understanding of the imaging assessments related to complications arising after spinal surgery is crucial to ensure timely and accurate diagnosis, which is necessary to achieve effective treatment.

Assessing visibility and bone changes of spinal metastases in CT scans: a comprehensive analysis across diverse cancer types.

OBJECTIVES: To analyze the characteristics of spinal metastasis in CT scans across diverse cancers for effective diagnosis and treatment, using MRI as the gold standard. METHODS: A retrospective study of 309 patients from four centers, who underwent concurrent CT and spinal MRI, revealing spinal metastasis, was conducted. Data on metastasis including total number, volume, visibility on CT (visible, indeterminate, or invisible), and type of bone change were collected. Through chi-square and Mann-Whitney U tests, we characterized the metastasis across diverse cancers and investigated the variation in the intra-individual ratio representing the percentage of lesions within each category for each patient. RESULTS: Out of 3333 spinal metastases from 309 patients, 55% were visible, 21% indeterminate, and 24% invisible. Sclerotic and lytic lesions made up 47% and 43% of the visible and indeterminate categories, respectively. Renal cell carcinoma (RCC), prostate cancer, and hepatocellular carcinoma (HCC) had the highest visibility at 86%, 73%, and 67% (p < 0.0001, p < 0.0001, and p = 0.003), while pancreatic cancer was lowest at 29% (p < 0.0001). RCC and HCC had significantly high lytic metastasis ratios (interquartile range (IQR) 0.96-1.0 and 0.31-1.0, p < 0.001 and p = 0.005). Prostate cancer exhibited a high sclerotic lesion ratio (IQR 0.52-0.97, p < 0.001). About 39% of individuals had invisible or indeterminate lesions, even with a single visible lesion on CT. The intra-individual ratio for indeterminate and invisible metastases surpassed 18%, regardless of the maximal size of the visible metastasis. CONCLUSIONS: This study highlights the variability in characteristics of spinal metastasis based on the primary cancer type through unique lesion-centric analysis.