• Korean Journal of Radiology
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• v.21 no.4
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• pp.431-441
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• 2020

Objective: To compare the diagnostic performance and interobserver variability of strain ratio obtained from one or two regions of interest (ROI) on breast elastography. Materials and Methods: From April to May 2016, 140 breast masses in 140 patients who underwent conventional ultrasonography (US) with strain elastography followed by US-guided biopsy were evaluated. Three experienced breast radiologists reviewed recorded US and elastography images, measured strain ratios, and categorized them according to the American College of Radiology breast imaging reporting and data system lexicon. Strain ratio was obtained using the 1-ROI method (one ROI drawn on the target mass), and the 2-ROI method (one ROI in the target mass and another in reference fat tissue). The diagnostic performance of the three radiologists among datasets and optimal cut-off values for strain ratios were evaluated. Interobserver variability of strain ratio for each ROI method was assessed using intraclass correlation coefficient values, Bland-Altman plots, and coefficients of variation. Results: Compared to US alone, US combined with the strain ratio measured using either ROI method significantly improved specificity, positive predictive value, accuracy, and area under the receiver operating characteristic curve (AUC) (all p values < 0.05). Strain ratio obtained using the 1-ROI method showed higher interobserver agreement between the three radiologists without a significant difference in AUC for differentiating breast cancer when the optimal strain ratio cut-off value was used, compared with the 2-ROI method (AUC: 0.788 vs. 0.783, 0.693 vs. 0.715, and 0.691 vs. 0.686, respectively, all p values > 0.05). Conclusion: Strain ratios obtained using the 1-ROI method showed higher interobserver agreement without a significant difference in AUC, compared to those obtained using the 2-ROI method. Considering that the 1-ROI method can reduce performers' efforts, it could have an important role in improving the diagnostic performance of breast US by enabling consistent management of breast lesions.

• Journal of the Korean Society of Radiology
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• v.82 no.5
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• pp.1186-1195
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• 2021

Purpose In the adult emergency department of a university hospital, we investigated the frequency of major discrepancies between the preliminary reports by radiology residents and the final reports by certified radiologists. Materials and Methods Based on CT and MRI scans obtained between December 2016 and November 2019, we selected cases with diagnoses or treatment plans that could be changed due to discrepancies between preliminary and final reports and classified them by the type of discrepancy. We also examined the distributions of the major discrepancies and stratified them by residents' working time zone, experience, and subspecialty. Results Based on the 72137 preliminary reports evaluated, 1348 tests (1.9%) showed major discrepancies. Most of the major discrepancies were false negatives (72.0%), followed by misdiagnosis (26.3%) and false positives (1.7%). Acute findings (87.2%) were more common than non-acute findings (12.8%). The major discrepancy rate increased toward the second half of the 24-hour shift, with the highest rate of 2.9% occurring between 2 am and 4 am. The major discrepancy rate did not vary with experience, and it varied from 0.6% to 4.5% for each subspecialty. Conclusion The major discrepancy rate was less than 2%, and it increased with longer working hours during a 24-hour shift.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.1009-1018
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• 2022

Objective: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. Materials and Methods: This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). Results: Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. Conclusion: This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

• Journal of Trauma and Injury
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• v.37 no.1
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• pp.28-36
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• 2024

Purpose: Recent advancements in interventional radiology have made angioembolization an invaluable modality in trauma care. Angioembolization is typically performed by interventional radiologists. In this study, we aimed to investigate the safety and efficacy of emergency angioembolization performed by trauma surgeons. Methods: We identified trauma patients who underwent emergency angiography due to significant trauma-related hemorrhage between January 2020 and June 2023 at Jeju Regional Trauma Center. Until May 2022, two dedicated interventional radiologists performed emergency angiography at our center. However, since June 2022, a trauma surgeon with a background and experience in vascular surgery has performed emergency angiography for trauma-related bleeding. The indications for trauma surgeon-performed angiography included significant hemorrhage from liver injury, pelvic injury, splenic injury, or kidney injury. We assessed the angiography results according to the operator of the initial angiographic procedure. The term "failure of the first angioembolization" was defined as rebleeding from any cause, encompassing patients who underwent either re-embolization due to rebleeding or surgery due to rebleeding. Results: No significant differences were found between the interventional radiologists and the trauma surgeon in terms of re-embolization due to rebleeding, surgery due to rebleeding, or the overall failure rate of the first angioembolization. Mortality and morbidity rates were also similar between the two groups. In a multivariable logistic regression analysis evaluating failure after the first angioembolization, pelvic embolization emerged as the sole significant risk factor (adjusted odds ratio, 3.29; 95% confidence interval, 1.05-10.33; P=0.041). Trauma surgeon-performed angioembolization was not deemed a significant risk factor in the multivariable logistic regression model. Conclusions: Trauma surgeons, when equipped with the necessary endovascular skills and experience, can safely perform angioembolization. To further improve quality control, an enhanced training curriculum for trauma surgeons is warranted.

• Tuberculosis and Respiratory Diseases
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• v.57 no.6
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• pp.515-521
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• 2004

With the increasing resolution of modern CT scanners, analysis of the larger numbers of images acquired in a lung screening exam or diagnostic study is necessary, which also needs high accuracy and reproducibility. Recent developments in the computerized analysis of medical images are expected to aid radiologists and other healthcare professional in various diagnostic tasks of medical image interpretation. This article is to provide a brief overview of some of computer-aided diagnosis schemes in chest CT.

• Imaging Science in Dentistry
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• v.50 no.3
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• pp.269-272
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• 2020

• Journal of The Korean Radiological Technologist Association
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• v.28 no.1
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• pp.74-82
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• 2002

1. Purpose Presently, the service areas of those who we engaged in radiation works are being specialized. Thus, in this study, we propose a way for efficiently improving mutual relationship between the works of the radiation technicians(radiologists) and

• Journal of International Society for Simulation Surgery
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• v.1 no.1
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• pp.37-40
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• 2014

Purpose For living donor liver transplantation, liver segmentation is difficult due to the variability of its shape across patients and similarity of the density of neighbor organs such as heart, stomach, kidney, and spleen. In this paper, we propose an automatic segmentation of the liver using multi-planar anatomy and deformable surface model in portal phase of abdominal contrast-enhanced CT images. Method Our method is composed of four main steps. First, the optimal liver volume is extracted by positional information of pelvis and rib and by separating lungs and heart from CT images. Second, anisotropic diffusing filtering and adaptive thresholding are used to segment the initial liver volume. Third, morphological opening and connected component labeling are applied to multiple planes for removing neighbor organs. Finally, deformable surface model and probability summation map are performed to refine a posterior liver surface and missing left robe in previous step. Results All experimental datasets were acquired on ten living donors using a SIEMENS CT system. Each image had a matrix size of $512{\times}512$ pixels with in-plane resolutions ranging from 0.54 to 0.70 mm. The slice spacing was 2.0 mm and the number of images per scan ranged from 136 to 229. For accuracy evaluation, the average symmetric surface distance (ASD) and the volume overlap error (VE) between automatic segmentation and manual segmentation by two radiologists are calculated. The ASD was $0.26{\pm}0.12mm$ for manual1 versus automatic and $0.24{\pm}0.09mm$ for manual2 versus automatic while that of inter-radiologists was $0.23{\pm}0.05mm$. The VE was $0.86{\pm}0.45%$ for manual1 versus automatic and $0.73{\pm}0.33%$ for manaual2 versus automatic while that of inter-radiologist was $0.76{\pm}0.21%$. Conclusion Our method can be used for the liver volumetry for the pre-surgery planning of living donor liver transplantation.

• Journal of the Korea Computer Industry Society
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• v.3 no.9
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• pp.1279-1286
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• 2002

Generally, radiologists can fail to detect pulmonary nodules in up to 30%. If an automatic system can inform the radiologists of thelocations of the doubtful nodules in the X-ray chest images, the frequency of mistakenly observed numbers of the nodules can be potentially reduced. This software is using morphological filtering and two feature-extraction techniques. The morphological filtering is the first process, which subsequently adds the operations of erosion and dilation to the original images so that this process can transform the original X-ray chest images into manageable ones. The false-positives are frequently being mistaken as nodules but actually these are not real nodules. The second process is the two feature-extraction techniques which are used to reduce the false-positives. Therefore, this system will make more effective detection of pulmonary nodules by reducing the false-positives when applied to the X-ray chest images which is difficult to get accurate detection.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.27 no.1
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• pp.179-188
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• 1997

This study was intended to compare the diagnostic ability between Waters' radiograph and CT in the diagnosis of midfacial fracture. The study group included 44 patients(male:32, female :12, age range :16-74 years old) with facial injury who underwent surgery. Waters' radiograph and both axial and coronal scanning were performed before surgery. Three oral radiologists and three non-oral radiologist interpreted 44 Waters' radiographs and 88 CT in three ways; 1) interpretation using Waters' radiograph only, 2) interpretation using CT only, 3) interpretation using Waters' radiograph and CT. The interpretation sites were confined to the walls of maxillary sinus; anterior, posterior, medial, lateral and superior wall. ROC curves were made with the findings during surgery as a gold standard except the posterior wall, where the joint evaluation of Waters' radiograph and CT by other three oral radiologists was used. ROC areas were compared according to the interpreting methods, the interpretation sites, and groups (R group ; oral radiologist group, N group ; non-oral radiologist group). The obtained results were as followed : 1. The diagnostic ability of CT only and Waters' radiograph and CT was higher than Waters' radiograph only in both groups(P<0.05). But there was no difference between CT only and Waters' radiograph and CT. 2. Generally, the diagnostic ability for the lateral antral wall was the highest and that for the posterior antral wall was the lowest in both groups(P<0.05). 3. In R group, for the anterior antral wall the diagnostic ability using CT only was increased but for the medial, lateral and superior antral walls the diagnostic ability was increased in only using Waters' radiograph and CT. 4. In N group, for the anterior and medial walls the diagnostic ability using CT only was increased. But for the posterior, lateral and superior antral walls there were no difference among three interpreting methods. 5. The diagnostic ability of R group was higher than N group in all interpreting methods.