Journal of the Korean Society of Radiology最新文献

Kimura's disease (KD) is a rare, chronic inflammatory disorder characterized by angiolymphoid hyperplasia, peripheral eosinophilia, and elevated serum immunoglobulin E levels. It primarily affects young Asian males and typically involves the head and neck region, especially near the mandible and postauricular regions. Orbital involvement is unusual and extraocular muscle (EOM) involvement is exceedingly rare, with only a few cases reported in the literature. The present report describes a case of surgically confirmed KD in a 16-year-old male, involving the bilateral EOM, lacrimal gland, and left parotid gland.

Purpose: To assess the reliability and accuracy of an automated Cobb angle measurement (ACAM) using a convolutional neural network (CNN) for scoliosis evaluation and to compare measurement times.

Materials and methods: ACAM was applied to spine radiographs in 411 patients suspected of scoliosis. Observer 1 (consensus of two musculoskeletal radiologists) and observer 2 (a radiology resident) measured Cobb angle (CA). CA measurements were categorized using observer 1's measurements as the reference standard. Inter-observer reliability and correlation were assessed using intraclass correlation coefficient (ICC) and Spearman's rank correlation coefficient, respectively. Accuracy and measurement time of ACAM and observers were evaluated.

Results: ACAM demonstrated excellent reliability and very high correlation with observer 1 (ICC = 0.976, Spearman's rank correlation = 0.948), with a mean CA difference of 1.1. Overall accuracy was high (88.2%), particularly in mild (92.2%) and moderate (96%) scoliosis. Accuracy was lower in spinal asymmetry (77.1%) and higher in severe scoliosis (95%), although the CA was lower compared to the observers. ACAM significantly reduced measurement time by nearly half compared to the observers (p < 0.001).

Conclusion: ACAM using CNN enhances CA measurement for assessing mild or moderate scoliosis, despite limitations in spinal asymmetry or severe scoliosis. Nonetheless, it substantially decreases measurement time.

This report presents a unique case of Caplan syndrome that mimicked accelerated progressive massive fibrosis. The patient, a former coal miner, had been diagnosed with coal worker's pneumoconiosis 15 years prior and had been treated for rheumatoid arthritis for over 20 years. Accelerated progressive massive fibrosis and the development of multiple nodules with cavitation in the basal lungs were subsequently observed on serial CT scans. Here, the CT manifestations of Caplan syndrome are highlighted in a case in which Caplan syndrome mimicked accelerated progressive massive fibrosis.

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

Purpose: To identify clinical and MR predictors of retro-odontoid pseudotumor (ROP) regression after posterior fixation in patients with atlantoaxial instability.

Materials and methods: We included patients who had undergone posterior fixation for atlantoaxial instability and preoperative and postoperative MR imaging. Patients were classified into two groups according to the degree of ROP regression after posterior fixation: regression (≥ 10% reduction) and no regression (< 10% reduction). Mann-Whitney and Fisher's exact tests were performed to identify the clinical (age and sex) and MR predictors (preoperative ROP thickness, ROP type, MR signal homogeneity of the ROP, spinal cord signal change, spinal cord atrophy, ossified posterior longitudinal ligament, os odontoideum, and atlantodental interval) associated with ROP regression.

Results: We retrospectively assessed 11 consecutive patients (7 female; median age, 66 years [range, 31-84 years]). Posterior fixation induced ROP regression in eight (72.7%) patients. Older age and greater preoperative ROP thickness significantly correlated with ROP regression (p = 0.024 and 0.012, respectively). All patients with preoperative ROP thickness > 5 mm exhibited ROP regression. The other variables were not significantly associated with ROP regression.

Conclusion: Older age and thicker preoperative ROP are associated with ROP regression after posterior fixation in patients with atlantoaxial instability.

Primary rectal syphilis is a rare disease that can be misdiagnosed as lymphoma or other rectal cancers on sigmoidoscopy or CT. Here, we report a case of primary rectal syphilis mimicking rectal malignancy in a 23-year-old male who presented with a rectal mass and multiple lymphadenopathies. In this case report and literature review, we focused on the CT findings and endoscopic observations of primary rectal syphilis. Infectious diseases, such as rectal syphilis, should be considered in the differential diagnosis of young patients with unusual rectal lesions and disproportionately extensive lymphadenopathies.

Purpose: To determine the pros and cons of an artificial intelligence (AI) model developed to diagnose acute rib fractures in chest CT images of patients with chest trauma.

Materials and methods: A total of 1209 chest CT images (acute rib fracture [n = 1159], normal [n = 50]) were selected among patients with chest trauma. Among 1159 acute rib fracture CT images, 9 were randomly selected for AI model training. 150 acute rib fracture CT images and 50 normal ones were tested, and the remaining 1000 acute rib fracture CT images was internally verified. We investigated the diagnostic accuracy and errors of AI model for the presence and location of acute rib fractures.

Results: Sensitivity, specificity, positive and negative predictive values, and accuracy for diagnosing acute rib fractures in chest CT images were 93.3%, 94%, 97.9%, 82.5%, and 95.6% respectively. However, the accuracy of the location of acute rib fractures was low at 76% (760/1000). The cause of error in the diagnosis of acute rib fracture seemed to be a result of considering the scapula or clavicle that were in the same position (66%) or some ribs that were not recognized (34%).

Conclusion: The AI model for diagnosing acute rib fractures showed high accuracy in detecting the presence of acute rib fractures, but diagnosis of the exact location of rib fractures was limited.

Postoperative colorectal imaging studies play an important role in the detection of surgical complications and disease recurrence. In this pictorial essay, we briefly describe methods of surgery, imaging findings of their early and late complications, and postsurgical recurrence of cancer and inflammatory bowel disease.