Korean Journal of Radiology最新文献

{"title":"Statistical Methods for the Analysis of Inter-Reader Agreement Among Three or More Readers.","authors":"Kyunghwa Han, Leeha Ryu","doi":"10.3348/kjr.2023.0965","DOIUrl":"10.3348/kjr.2023.0965","url":null,"abstract":"","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"325-327"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning-Based Image Reconstruction in Patients With Suspected Focal Epilepsy.","authors":"Pae Sun Suh, Ji Eun Park, Yun Hwa Roh, Seonok Kim, Mina Jung, Yong Seo Koo, Sang-Ahm Lee, Yangsean Choi, Ho Sung Kim","doi":"10.3348/kjr.2023.0842","DOIUrl":"10.3348/kjr.2023.0842","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learning-based image reconstruction (1.5-mm MRI + DLR) compared to routine 3-mm slice thickness MRI (routine MRI) and 1.5-mm slice thickness MRI without DLR (1.5-mm MRI without DLR) for evaluating temporal lobe epilepsy (TLE).</p><p><strong>Materials and methods: </strong>This retrospective study included 117 MR image sets comprising 1.5-mm MRI + DLR, 1.5-mm MRI without DLR, and routine MRI from 117 consecutive patients (mean age, 41 years; 61 female; 34 patients with TLE and 83 without TLE). Two neuroradiologists evaluated the presence of hippocampal or temporal lobe lesions, volume loss, signal abnormalities, loss of internal structure of the hippocampus, and lesion conspicuity in the temporal lobe. Reference standards for TLE were independently constructed by neurologists using clinical and radiological findings. Subjective image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed. Performance in diagnosing TLE, lesion findings, and image quality were compared among the three protocols.</p><p><strong>Results: </strong>The pooled sensitivity of 1.5-mm MRI + DLR (91.2%) for diagnosing TLE was higher than that of routine MRI (72.1%, <i>P</i> < 0.001). In the subgroup analysis, 1.5-mm MRI + DLR showed higher sensitivity for hippocampal lesions than routine MRI (92.7% vs. 75.0%, <i>P</i> = 0.001), with improved depiction of hippocampal T2 high signal intensity change (<i>P</i> = 0.016) and loss of internal structure (<i>P</i> < 0.001). However, the pooled specificity of 1.5-mm MRI + DLR (76.5%) was lower than that of routine MRI (89.2%, <i>P</i> = 0.004). Compared with 1.5-mm MRI without DLR, 1.5-mm MRI + DLR resulted in significantly improved pooled accuracy (91.2% vs. 73.1%, <i>P</i> = 0.010), image quality, SNR, and CNR (all, <i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>The use of 1.5-mm MRI + DLR enhanced the performance of MRI in diagnosing TLE, particularly in hippocampal evaluation, because of improved depiction of hippocampal abnormalities and enhanced image quality.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"374-383"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography.","authors":"Si Eun Lee, Hanpyo Hong, Eun-Kyung Kim","doi":"10.3348/kjr.2023.0907","DOIUrl":"10.3348/kjr.2023.0907","url":null,"abstract":"<p><strong>Objective: </strong>Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings.</p><p><strong>Materials and methods: </strong>From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups.</p><p><strong>Results: </strong>Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion).</p><p><strong>Conclusion: </strong>PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"343-350"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Caveats in Using Abnormality/Probability Scores from Artificial Intelligence Algorithms: Neither True Probability nor Level of Trustworthiness.","authors":"Seong Ho Park, Eui Jin Hwang","doi":"10.3348/kjr.2024.0144","DOIUrl":"10.3348/kjr.2024.0144","url":null,"abstract":"","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"328-330"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2024 Consensus Statement on Coronary Stenosis and Plaque Evaluation in CT Angiography From the Asian Society of Cardiovascular Imaging-Practical Tutorial (ASCI-PT).","authors":"Cherry Kim, Chul Hwan Park, Bae Young Lee, Chan Ho Park, Eun-Ju Kang, Hyun Jung Koo, Kakuya Kitagawa, Min Jae Cha, Rungroj Krittayaphong, Sang Il Choi, Hwan Seok Yong, Sung Min Ko, Sung Mok Kim, Sung Ho Hwang, Nguyen Ngoc Trang, Whal Lee, Young Jin Kim, Jongmin Lee, Dong Hyun Yang","doi":"10.3348/kjr.2024.0112","DOIUrl":"10.3348/kjr.2024.0112","url":null,"abstract":"<p><p>The Asian Society of Cardiovascular Imaging-Practical Tutorial (ASCI-PT) is an instructional initiative of the ASCI School designed to enhance educational standards. In 2021, the ASCI-PT was convened with the goal of formulating a consensus statement on the assessment of coronary stenosis and coronary plaque using coronary CT angiography (CCTA). Nineteen experts from four countries conducted thorough reviews of current guidelines and deliberated on eight key issues to refine the process and improve the clarity of reporting CCTA findings. The experts engaged in both online and on-site sessions to establish a unified agreement. This document presents a summary of the ASCI-PT 2021 deliberations and offers a comprehensive consensus statement on the evaluation of coronary stenosis and coronary plaque in CCTA.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"331-342"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study.","authors":"Dong Hyun Kim, Jiwoon Seo, Ji Hyun Lee, Eun-Tae Jeon, DongYoung Jeong, Hee Dong Chae, Eugene Lee, Ji Hee Kang, Yoon-Hee Choi, Hyo Jin Kim, Jee Won Chai","doi":"10.3348/kjr.2023.0671","DOIUrl":"10.3348/kjr.2023.0671","url":null,"abstract":"<p><strong>Objective: </strong>To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"363-373"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChatGPT Vision for Radiological Interpretation: An Investigation Using Medical School Radiology Examinations.","authors":"Hyungjin Kim, Paul Kim, Ijin Joo, Jung Hoon Kim, Chang Min Park, Soon Ho Yoon","doi":"10.3348/kjr.2024.0017","DOIUrl":"10.3348/kjr.2024.0017","url":null,"abstract":"","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"403-406"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT Demonstration of Local Cytokine-Release Syndrome Involving the Head and Neck Following Chimeric Antigen Receptor T Cell Infusion Therapy.","authors":"Ji Su Ko, Jeong Hyun Lee, Dok Hyun Yoon, Chong Hyun Suh, Sae Rom Chung, Young Jun Choi, Jung Hwan Baek","doi":"10.3348/kjr.2023.1100","DOIUrl":"10.3348/kjr.2023.1100","url":null,"abstract":"","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"399-402"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dark-Blood Computed Tomography Angiography Combined With Deep Learning Reconstruction for Cervical Artery Wall Imaging in Takayasu Arteritis.","authors":"Tong Su, Zhe Zhang, Yu Chen, Yun Wang, Yumei Li, Min Xu, Jian Wang, Jing Li, Xinping Tian, Zhengyu Jin","doi":"10.3348/kjr.2023.1078","DOIUrl":"10.3348/kjr.2023.1078","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the image quality of novel dark-blood computed tomography angiography (CTA) imaging combined with deep learning reconstruction (DLR) compared to delayed-phase CTA images with hybrid iterative reconstruction (HIR), to visualize the cervical artery wall in patients with Takayasu arteritis (TAK).</p><p><strong>Materials and methods: </strong>This prospective study continuously recruited 53 patients with TAK (mean age: 33.8 ± 10.2 years; 49 females) between January and July 2022 who underwent head-neck CTA scans. The arterial- and delayed-phase images were reconstructed using HIR and DLR. Subtracted images of the arterial-phase from the delayed-phase were then added to the original delayed-phase using a denoising filter to generate the final-dark-blood images. Qualitative image quality scores and quantitative parameters were obtained and compared among the three groups of images: Delayed-HIR, Dark-blood-HIR, and Dark-blood-DLR.</p><p><strong>Results: </strong>Compared to Delayed-HIR, Dark-blood-HIR images demonstrated higher qualitative scores in terms of vascular wall visualization and diagnostic confidence index (all <i>P</i> < 0.001). These qualitative scores further improved after applying DLR (Dark-blood-DLR compared to Dark-blood-HIR, all <i>P</i> < 0.001). Dark-blood DLR also showed higher scores for overall image noise than Dark-blood-HIR (<i>P</i> < 0.001). In the quantitative analysis, the contrast-to-noise ratio (CNR) values between the vessel wall and lumen for the bilateral common carotid arteries and brachiocephalic trunk were significantly higher on Dark-blood-HIR images than on Delayed-HIR images (all <i>P</i> < 0.05). The CNR values were significantly higher for Dark-blood-DLR than for Dark-blood-HIR in all cervical arteries (all <i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>Compared with Delayed-HIR CTA, the dark-blood method combined with DLR improved CTA image quality and enhanced visualization of the cervical artery wall in patients with TAK.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"384-394"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncover This Tech Term: Uncertainty Quantification for Deep Learning.","authors":"Shahriar Faghani, Cooper Gamble, Bradley J Erickson","doi":"10.3348/kjr.2024.0108","DOIUrl":"10.3348/kjr.2024.0108","url":null,"abstract":"","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"25 4","pages":"395-398"},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}