Korean Journal of Radiology最新文献

{"title":"Development of a Deep-Learning Model for Estimating Newborn Gestational Age via Lumbar Vertebral Segmentation on Plain Radiography.","authors":"Sungwon Ham, Gayoung Choi, Bo-Kyung Je, Saelin Oh","doi":"10.3348/kjr.2025.0172","DOIUrl":"10.3348/kjr.2025.0172","url":null,"abstract":"<p><strong>Objective: </strong>To develop a deep learning model for estimating newborn gestational age (GA) based on the shape of the lumbar vertebral bodies on cross-table lateral radiographs obtained on the first day after birth.</p><p><strong>Materials and methods: </strong>This retrospective study included 423 cross-table lateral radiographs of 423 newborns (242 boys and 181 girls) taken within 24 hours after birth at two hospitals. Of these, 256 radiographs (157 boys and 99 girls) obtained from one institution were used for model development, and 167 radiographs (85 boys and 82 girls) from the other institution were used for model external testing. Clinical data, including medical history of underlying disorders, GA determined by ultrasound parameters, birth date, birth weight, sex, examination date, and reason for requesting radiographs, were obtained. The radiographs underwent manual labeling of the five lumbar vertebral bodies, followed by preprocessing steps such as normalization, resizing, denoising, cropping, and augmentation via horizontal flipping and rotation. Subsequently, we trained a deep learning model using a DeepLabv3+ network with a ResNet50 backbone for lumbar segmentation and used a customized AgeClassifier model with two parallel ResNet18 backbones for GA estimation. Model performance was evaluated using an external test dataset after image cropping.</p><p><strong>Results: </strong>Neither GA nor birth weight differed significantly between boys and girls. In the segmentation model, the mean dice similarity coefficient ± standard deviation (SD) was 0.801 ± 0.031. For GA estimation, the mean absolute error ± SD was 5.2 ± 0.5 days. The Bland-Altman bias (AI-estimated GA - ground truth GA) and 95% limits of agreement were -0.4 days and -13.0 to 12.3 days, respectively.</p><p><strong>Conclusion: </strong>Our deep learning model showed promising performance in lumbar vertebral body segmentation and GA estimation using plain radiographs, suggesting its potential utility as a supportive tool for neonatal maturity assessment in clinical practice.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"26 9","pages":"867-876"},"PeriodicalIF":5.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12394821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144959313","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":"Breast Cancer Screening in Asian Countries: Epidemiology, Screening Practices, Outcomes, Challenges, and Future Directions.","authors":"Niketa Chotai, Rupa Renganathan, Takayoshi Uematsu, Jane Wang, Qingli Zhu, Kartini Rahmat, Varanatjaa Pradaranon, Julian Cy Fong, Lina Choridah, Jung Min Chang","doi":"10.3348/kjr.2025.0338","DOIUrl":"10.3348/kjr.2025.0338","url":null,"abstract":"<p><p>In 2022, nearly 2.3 million new cases of breast cancer were reported globally, with less than half of these cases originating from Asia. Despite the relatively low incidence of breast cancer in most parts of Asia, the mortality-to-incidence ratio remains high. Low-income countries lack resources for breast cancer screening, whereas high-income countries fail to fully benefit from national breast screening programs because of the underutilization of preventive healthcare services. There is a notable difference in the age distribution of breast cancer cases between Asian and Western populations, with the prevalence peaking approximately a decade earlier in Asian women and most commonly affecting those aged 40-50 years. Existing literature on breast cancer trends, screening guidelines, and clinical practices in Asian countries, particularly regarding regional variations and healthcare system differences, is relatively sparse. Gaining a deeper understanding of how different Asian countries are implementing breast cancer screening in response to the rising incidence of the disease can help identify tailored strategies for early detection, ultimately contributing to a reduction in breast cancer-related mortality. This review explored the current breast cancer landscape, including breast cancer screening guidelines and outcomes of screening examinations in Asia, highlighting key challenges and future directions.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"26 8","pages":"743-758"},"PeriodicalIF":5.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12318657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144742423","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":"Effect of Deep Learning-Based Artificial Intelligence on Radiologists' Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging.","authors":"Jiyeon Park, Chae Young Lim, So Yeon Won, Han Kyu Na, Phil Hyu Lee, Sun-Young Baek, Yun Hwa Roh, Minjung Seong, Yongsik Sim, Eung Yeop Kim, Sung Tae Kim, Beomseok Sohn","doi":"10.3348/kjr.2025.0208","DOIUrl":"10.3348/kjr.2025.0208","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the effect of deep learning (DL)-based artificial intelligence (AI) software on the diagnostic performance of radiologists with different experience levels in detecting nigrosome 1 (N1) abnormalities on susceptibility map-weighted imaging (SMwI).</p><p><strong>Materials and methods: </strong>This retrospective diagnostic case-control study analyzed 139 SMwI scans of 59 patients with Parkinson's disease (PD) and 80 healthy participants. Participants were imaged using 3T MRI, and AI-generated assessments for N1 abnormalities were obtained using an AI model (version 1.0.1.0; Heuron Corporation, Seoul, Korea), which utilized YOLOX-based object detection and SparseInst segmentation models. Four radiologists (two experienced neuroradiologists and two less experienced residents) evaluated N1 abnormalities with and without AI in a crossover study design. Diagnostic performance metrics, inter-reader agreements, and reader responses to AI-generated assessments were evaluated.</p><p><strong>Results: </strong>Use of AI significantly improved diagnostic performance compared with interpretation without it across three readers, with significant increases in specificity (0.86 vs. 0.94, <i>P</i> = 0.004; 0.91 vs. 0.97, <i>P</i> = 0.024; and 0.90 vs. 0.97, <i>P</i> = 0.012). Inter-reader agreement also improved with AI, as Fleiss's kappa increased from 0.73 (95% confidence interval [CI]: 0.61-0.84) to 0.87 (95% CI: 0.76-0.99). The net reclassification index (NRI) demonstrated significant improvement in three of the four readers. When grouped by experience level, less experienced readers showed greater improvement (NRI = 12.8%, 95% CI: 0.067-0.190) than experienced readers (NRI = 0.8%, 95% CI: -0.037-0.051). In the less experienced group, reader-AI disagreement was significantly higher in the PD group than in the normal group (8.1% vs. 3.8%, <i>P</i> = 0.029).</p><p><strong>Conclusion: </strong>DL-based AI enhances the diagnostic performance in detecting N1 abnormalities on SMwI, particularly benefiting less experienced radiologists. These findings underscore the potential for improving diagnostic workflows for PD.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"26 8","pages":"771-781"},"PeriodicalIF":5.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12318656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144742425","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":"Association of Hypoxic-Ischemic Injury of the Brain With MRI-Derived Glymphatic Function Parameters in Neonates.","authors":"Arum Choi, Dayeon Bak, Jimin Kim, Se Won Oh, Yoonho Nam, Hyun Gi Kim","doi":"10.3348/kjr.2025.0300","DOIUrl":"10.3348/kjr.2025.0300","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the association between hypoxic-ischemic injury (HII) of the brain and glymphatic function using MRI-derived parameters in neonates.</p><p><strong>Materials and methods: </strong>This retrospective, single-institution study collected brain MRI scans of 127 neonates between July 2020 and July 2022. The volume and fraction of the basal ganglia perivascular space (BG-PVS) were automatically extracted using three-dimensional T2-weighted image processing. Diffusion-tensor imaging (DTI) along the PVS (DTI-ALPS) index values were derived from the DTI maps. BG-PVS and DTI-ALPS parameters were compared between neonates with and without HII. The correlations between MRI-derived glymphatic parameters and corrected gestational age (CGA), as well as between BG-PVS measurements and the DTI-ALPS index, were analyzed using Spearman coefficients. Multivariable logistic regression adjusted for age, sex, birth weight, and mode of delivery was performed to examine the associations between each glymphatic parameter and HII.</p><p><strong>Results: </strong>This study included 97 neonates without HII (median gestational age [GA]: 252 days) and 30 with HII (median GA: 252 days). Neonates with HII had smaller BG-PVS volumes (19 mm³ vs. 33 mm³, <i>P</i> = 0.001) and fractions (0.29% vs. 0.54%, <i>P</i> = 0.003) compared to neonates without HII. The DTI-ALPS index values did not differ significantly between neonates with and without HII (<i>P</i> = 0.54). CGA correlated negatively with BG-PVS measurements (ρ = -0.21 to -0.26, all <i>P</i> < 0.05) and positively with DTI-ALPS index values (ρ = 0.22, <i>P</i> = 0.014). BG-PVS measurements and DTI-ALPS index values were not significantly correlated (ρ = -0.28 to -0.08, all <i>P</i> > 0.05). Multivariable logistic regression revealed a negative association between BG-PVS volume (odds ratio [OR]: 0.96 per mm³ increase, 95% confidence interval [CI]: 0.93-0.99) and fraction (OR: 0.15 per % increase, 95% CI: 0.03-0.79) with HII, while DTI-ALPS index values were not significantly associated with HII (OR: 0.10, 95% CI: 0.00-25.41).</p><p><strong>Conclusion: </strong>Neonates with HII demonstrated smaller BG-PVS volume and fraction compared with those without HII, indicating potential alterations in glymphatic function among affected newborns.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"26 8","pages":"782-792"},"PeriodicalIF":5.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12318655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144742392","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":"Response to \"Determining Whether the Glymphatic System is Truly Impaired in Pediatric Patients With Refractory Epilepsy Requires Appropriately Designed Studies\".","authors":"Lu Qiu, Haoxiang Jiang","doi":"10.3348/kjr.2025.0708","DOIUrl":"10.3348/kjr.2025.0708","url":null,"abstract":"","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"26 8","pages":"799-800"},"PeriodicalIF":5.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12318653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144742426","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":"Impact of Deep Learning-Based Image Conversion on Fully Automated Coronary Artery Calcium Scoring Using Thin-Slice, Sharp-Kernel, Non-Gated, Low-Dose Chest CT Scans: A Multi-Center Study.","authors":"Cherry Kim, Sehyun Hong, Hangseok Choi, Won-Seok Yoo, Jin Young Kim, Suyon Chang, Chan Ho Park, Su Jin Hong, Dong Hyun Yang, Hwan Seok Yong, Marly van Assen, Carlo N De Cecco, Young Joo Suh","doi":"10.3348/kjr.2025.0177","DOIUrl":"10.3348/kjr.2025.0177","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the impact of deep learning-based image conversion on the accuracy of automated coronary artery calcium quantification using thin-slice, sharp-kernel, non-gated, low-dose chest computed tomography (LDCT) images collected from multiple institutions.</p><p><strong>Materials and methods: </strong>A total of 225 pairs of LDCT and calcium scoring CT (CSCT) images scanned at 120 kVp and acquired from the same patient within a 6-month interval were retrospectively collected from four institutions. Image conversion was performed for LDCT images using proprietary software programs to simulate conventional CSCT. This process included 1) deep learning-based kernel conversion of low-dose, high-frequency, sharp kernels to simulate standard-dose, low-frequency kernels, and 2) thickness conversion using the raysum method to convert 1-mm or 1.25-mm thickness images to 3-mm thickness. Automated Agaston scoring was conducted on the LDCT scans before (LDCT-Org_auto) and after the image conversion (LDCT-CONV_auto). Manual scoring was performed on the CSCT images (CSCT_manual) and used as a reference standard. The accuracy of automated Agaston scores and risk severity categorization based on the automated scoring on LDCT scans was analyzed compared to the reference standard, using the Bland-Altman analysis, concordance correlation coefficient (CCC), and weighted kappa (κ) statistic.</p><p><strong>Results: </strong>LDCT-CONV_auto demonstrated a reduced bias for Agaston score, compared with CSCT_manual, than LDCT-Org_auto did (-3.45 vs. 206.7). LDCT-CONV_auto showed a higher CCC than LDCT-Org_auto did (0.881 [95% confidence interval {CI}, 0.750-0.960] vs. 0.269 [95% CI, 0.129-0.430]). In terms of risk category assignment, LDCT-Org_auto exhibited poor agreement with CSCT_manual (weighted κ = 0.115 [95% CI, 0.082-0.154]), whereas LDCT-CONV_auto achieved good agreement (weighted κ = 0.792 [95% CI, 0.731-0.847]).</p><p><strong>Conclusion: </strong>Deep learning-based conversion of LDCT images originally obtained with thin slices and a sharp kernel can enhance the accuracy of automated coronary artery calcium score measurement using the images.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":" ","pages":"759-770"},"PeriodicalIF":5.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12318652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317318","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: Application Programming Interface for Large Language Models.","authors":"Chae Ri Park, Hwon Heo, Chong Hyun Suh, Woo Hyun Shim","doi":"10.3348/kjr.2025.0360","DOIUrl":"10.3348/kjr.2025.0360","url":null,"abstract":"","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"26 8","pages":"793-796"},"PeriodicalIF":5.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12318651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144742427","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":"Determining Whether the Glymphatic System is Truly Impaired in Pediatric Patients With Refractory Epilepsy Requires Appropriately Designed Studies.","authors":"Josef Finsterer","doi":"10.3348/kjr.2025.0542","DOIUrl":"10.3348/kjr.2025.0542","url":null,"abstract":"","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":"26 8","pages":"797-798"},"PeriodicalIF":5.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12318654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144742424","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":"OK-432 (Picibanil) Sclerotherapy for the Treatment of Chyle Leakage After Thyroid Surgery.","authors":"Hunjong Lim, Sang Il Choi","doi":"10.3348/kjr.2025.0230","DOIUrl":"10.3348/kjr.2025.0230","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to evaluate the safety and efficacy of OK-432 (Picibanil) sclerotherapy for the management of chyle leakage following thyroid surgery.</p><p><strong>Materials and methods: </strong>We retrospectively reviewed the medical records of 12 consecutive patients with chyle leakage who underwent Picibanil sclerotherapy after thyroid surgery between January 2019 and December 2024. The collected data included patient demographics, clinical presentation, prior treatments, sclerotherapy details, treatment outcomes, and adverse effects. Clinical success was defined as fulfillment of all three criteria: 1) Improvement in clinical signs and symptoms, 2) Volume reduction of the chyloma without compressive effects on surrounding tissues on ultrasonography, and 3) Drainage amount of <10 mL/day (if applicable). Recurrence was defined as the reappearance of chyle leakage symptoms or re-accumulation of fluid at the previous chyloma site on follow-up imaging after initial clinical success.</p><p><strong>Results: </strong>Among the 12 patients (median age: 45.5 years), 11 (91.7%) achieved clinical success. One patient (8.3%) did not respond to treatment and subsequently required thoracic duct embolization. The median number of sclerotherapy sessions was two (range, 1-4). Minor adverse effects, such as fever (10/12) and localized pain (4/12), were observed but resolved without long-term complications. No recurrence was noted during the median post-sclerotherapy follow-up period of 35 months (range: 10-57 months).</p><p><strong>Conclusion: </strong>Picibanil sclerotherapy may be a safe and effective treatment for chyle leakage after thyroid surgery. It may offer a minimally invasive alternative with a high clinical success rate and minor, manageable adverse effects.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":" ","pages":"727-733"},"PeriodicalIF":4.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12235551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144016027","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":"Potential Obstacles and Strategic Interventions for Addressing Gaps in the Understanding of Human-Artificial Intelligence Interactions in Medical Practice.","authors":"Hongnan Ye","doi":"10.3348/kjr.2025.0361","DOIUrl":"10.3348/kjr.2025.0361","url":null,"abstract":"","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":" ","pages":"738-739"},"PeriodicalIF":4.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12235545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144027546","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}