Academic Radiology最新文献

{"title":"T2WI and ADC radiomics combined with a nomogram based on clinicopathologic features to quantitatively predict microsatellite instability in colorectal cancer","authors":"Leping Peng ,&nbsp;Xiuling Zhang ,&nbsp;Yuanhui Zhu ,&nbsp;Liuyan Shi ,&nbsp;Kai Ai ,&nbsp;Gang Huang ,&nbsp;Wenting Ma ,&nbsp;Zhaokun Wei ,&nbsp;Ling Wang ,&nbsp;Yaqiong Ma ,&nbsp;Lili Wang","doi":"10.1016/j.acra.2024.10.002","DOIUrl":"10.1016/j.acra.2024.10.002","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Microsatellite instability (MSI) stratification can guide the clinical management of patients with colorectal cancer (CRC). This study aimed to establish a radiomics model for predicting the MSI status of patients with CRC before treatment.</div></div><div><h3>Materials and Methods</h3><div>This retrospective study was performed on 366 patients diagnosed with CRC who underwent preoperative magnetic resonance imaging (MRI) and immunohistochemical staining between February 2016 and September 2023. The participants were divided randomly into training and testing cohorts in a 7:3 ratio. The tumor volume of interest (VOI) was manually delineated on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences using 3D Slicer software, and radiomics features were extracted. Feature selection was performed using the least absolute shrinkage and selection operator method. A radiomics nomogram was developed using multiple logistic regression, and the predictive performance of the models was evaluated and compared using receiver operating characteristic curves. The calibration curve, clinical decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical application value of the model.</div></div><div><h3>Results</h3><div>The radiomics normogram combined with history of chronic enteritis, tumor location, MR–reported inflammatory response, D2–40, carcinoembryonic antigen, tumor protein 53, and monocyte was an excellent predictive tool. The area under the curve for the training and testing cohorts were 0.927 and 0.984, respectively. The DCA and CIC demonstrated favorable clinical application and net benefit.</div></div><div><h3>Conclusions</h3><div>A radiomics nomogram based on T2WI and ADC sequences and clinicopathologic features can effectively and noninvasively predict the MSI status in CRC. This approach helps clinicians in stratifying CRC patients and making clinical decisions for personalized treatment.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1431-1450"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142569856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic value of extracellular volume fraction in myocardial infarction and myocardial infarction with nonobstructive coronary arteries: A multicenter study","authors":"Bowen Li MD ,&nbsp;Yan Gao MD ,&nbsp;Jian Wang MD ,&nbsp;Runze Zhu MD ,&nbsp;Shifeng Yang MD, PhD ,&nbsp;Congshan Ji MD, PhD ,&nbsp;Ying Wang MD ,&nbsp;Ximing Wang MD, PhD ,&nbsp;Hui Gu MD, PhD","doi":"10.1016/j.acra.2024.11.015","DOIUrl":"10.1016/j.acra.2024.11.015","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The aim of the present retrospective study was to evaluate the prognostic role of the extracellular volume fraction (ECV) in patients with myocardial infarction (MI) and myocardial infarction with nonobstructive coronary arteries (MINOCA). The present study hypothesized that ECV is associated with major adverse cardiovascular events (MACEs) in MI and MINOCA patients.</div></div><div><h3>Materials and Methods</h3><div>Cardiac magnetic resonance (CMR) imaging was performed on 351 consecutive patients (mean age: 58 ± 12 years; 252 [71.8%] males) who were diagnosed with MI between October 2015 and November 2023. From CMR imaging, the extent of late gadolinium enhancement (LGE), native T1 and ECV were derived. Patients were categorized into groups according to the degree of coronary artery stenosis, namely, patients with MINOCA and patients with obstructive MI. Follow-up was performed to assess MACEs.</div></div><div><h3>Results</h3><div>The final cohort consisted of 61 MINOCA patients and 290 obstructive MI patients. During a mean follow-up of 27 ± 16 months, there was no statistically significant difference in the incidence of MACEs between patients with MINOCA and those with obstructive MI, and the two groups of patients had similar ECVs (32.2 ± 3.6 vs. 32.3 ± 6.0, <em>p</em> = 0.864). According to the multivariate Cox regression, ECV was an independent predictor of MACEs (HR: 1.13; <em>p</em> &lt; 0.001) and significantly improved the prognostic value of the baseline multivariate models (C-statistic improvement: 0.816–0.864, <em>p</em> = 0.001). Similarly, ECV maintained an independent association with MACEs in the MINOCA (HR: 1.35; <em>p</em> &lt; 0.001) and obstructive MI (HR: 1.13; <em>p</em> &lt; 0.001) groups.</div></div><div><h3>Conclusion</h3><div>In MI and MINOCA patients, ECV is an independent predictor of MACEs. MINOCA is not a benign disease, and its long-term prognosis is as poor as that of patients with obstructive MI.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1313-1323"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Efficacy of Non-contrast vs. Contrast-enhanced CT Radiomics in Predicting Coronary Artery Plaques Among Patients with Low Agatston Scores","authors":"Jianhua Liang ,&nbsp;Congcong Lin ,&nbsp;Hongliang Qi ,&nbsp;Yongkai Lin ,&nbsp;Liwei Deng ,&nbsp;Jieyao Wu ,&nbsp;Chunyang Yang ,&nbsp;Zhiyuan He ,&nbsp;Jiaqing Li ,&nbsp;Hanwei Li ,&nbsp;Debin Hu ,&nbsp;Hongwen Chen ,&nbsp;Yuanzhang Li","doi":"10.1016/j.acra.2024.11.063","DOIUrl":"10.1016/j.acra.2024.11.063","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Patients with a low Agatston score often present with clinical signs and symptoms suggestive of coronary artery disease, despite having minimal calcium deposits. This study aimed to compare the efficacy of low-dose non-contrast cardiac CT with coronary computed tomography angiography (CCTA) in pericoronary adipose tissue (PCAT) radiomics for predicting coronary artery plaques, using CCTA as the reference standard.</div></div><div><h3>Materials and Methods</h3><div>This retrospective study analyzed 459 patients with suspected coronary artery disease and a coronary artery calcium score &lt; 100 Agatston units, who were treated between June 2021 and December 2023 at a tertiary hospital. Three predictive models for coronary artery plaques were developed: (1) a clinical factor model, (2) a hybrid model integrating clinical factors and CT PCAT radiomics, and (3) a hybrid model integrating clinical factors and CCTA PCAT radiomics. Multivariable logistic regression and receiver operating characteristic curve evaluations were performed to develop and validate predictive models.</div></div><div><h3>Results</h3><div>Both hybrid models showed significant correlations in the training set (r = 0.890, P &lt; 0.001) and the validation set (r = 0.920, P &lt; 0.001). The mean agreement in the training set is 0, with 3.42% (11/322) of the data points outside the 95% CI (−0.18–0.18, P &lt; 0.001). The mean agreement in the validation set is −0.244, with 6.57% (9/137) of the data points outside the 95% CI (−0.443–0.045, P &lt; 0.001).</div></div><div><h3>Conclusions</h3><div>Non-contract CT PCAT radiomics showed comparable efficacy to CCTA PCAT radiomics in predicting coronary artery plaques among patients with low Agatston scores.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1344-1352"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856160","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":"Radiomics Biomarkers to Predict Checkpoint Inhibitor Pneumonitis in Non-small Cell Lung Cancer","authors":"Yonghao Du ,&nbsp;Shuo Zhang ,&nbsp;Xiaohui Jia ,&nbsp;Xi Zhang ,&nbsp;Xuqi Li ,&nbsp;Libo Pan ,&nbsp;Zhihao Li ,&nbsp;Gang Niu ,&nbsp;Ting Liang ,&nbsp;Hui Guo MD, PhD","doi":"10.1016/j.acra.2024.09.053","DOIUrl":"10.1016/j.acra.2024.09.053","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC). However, immune-related adverse events still occur, of which checkpoint inhibitor pneumonitis (CIP) is the most common. We aimed to construct and validate a contrast-enhanced computed tomography-based radiomic nomogram to predict the probability of CIP before ICIs treatment in NSCLC.</div></div><div><h3>Materials and methods</h3><div>We retrospectively analyzed 685 patients with NSCLC who were initially treated with ICIs. A total of 186 patients were included in our study, and an additional 52 patients from another hospital were considered for external validation. After radiomics feature extraction and selection, we applied a support vector machine classification model to distinguish CIP and used the probability as a radiomics signature. A radiomics-clinical logistic regression model was built using the filtered clinical parameters and a radiomic signature. Receiver operating characteristic, area under the curve (AUC), calibration curve, and decision curve analysis was used for inter-model comparison.</div></div><div><h3>Results</h3><div>The combined radiomics-clinical model constructed using age, interstitial lung disease, emphysema at baseline, and radiomics signature showed an AUC of 0.935, 0.905, and 0.923 for the training, validation, and external validation cohorts, respectively. Compared with the clinical-only (AUC of 0.829, 0.826, and 0.809) and radiomics-only models (0.865, 0.847, and 0.841), the radiomics-clinical displayed better predictive power.</div></div><div><h3>Conclusion</h3><div>This combined radiomics-clinical model predicted the probability of CIP during ICIs treatment in patients with NSCLC with favorable accuracy and could therefore be used as an effective tool to guide clinical ICIs decisions.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1685-1695"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative Prediction of Occult Level V Lymph Node Metastasis in Papillary Thyroid Carcinoma: Development and Validation of a Radiomics-Driven Nomogram Model","authors":"Jia-Wei Feng ,&nbsp;Feng Zheng ,&nbsp;Shui-Qing Liu ,&nbsp;Gao-Feng Qi ,&nbsp;Xin Ye ,&nbsp;Jing Ye ,&nbsp;Yong Jiang","doi":"10.1016/j.acra.2024.10.001","DOIUrl":"10.1016/j.acra.2024.10.001","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The study aimed to analyze the patterns and frequency of Level V lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC), identify its risk factors, and construct predictive models for assessment.</div></div><div><h3>Methods</h3><div>We conducted a retrospective analysis of 325 PTC patients who underwent thyroidectomy and therapeutic unilateral bilateral modified radical neck dissection from October 2020 to January 2023. Patients were randomly allocated into a training cohort (70%) and a validation cohort (30%). The radiomics signature model was developed using ultrasound images, applying the minimum Redundancy-Maximum Relevance and Least Absolute Shrinkage and Selection Operator regression to extract high-throughput quantitative features. Concurrently, the clinic signature model was formulated based on significant clinical factors associated with Level V LNM. Both models were independently translated into nomograms for ease of clinical use.</div></div><div><h3>Results</h3><div>The radiomics signature model, without the inclusion of clinical factors, showed high discriminative power with an area under the curve (AUC) of 0.933 in the training cohort and 0.912 in the validation cohort. Conversely, the clinic signature model, composed of tumor margin, simultaneous metastasis, and high-volume lateral LNM, achieved an AUC of 0.749 in the training cohort. The radiomics signature model exhibited superior performance in sensitivity, specificity, positive predictive value, negative predictive value across both cohorts. Decision curve analysis demonstrated the clinical utility of the radiomics signature model, indicating its potential to guide more precise treatment decisions.</div></div><div><h3>Conclusion</h3><div>The radiomics signature model outperformed the clinic signature model in predicting Level V LNM in PTC patients. The radiomics signature model, available as a nomogram, offers a promising tool for preoperative assessment, with the potential to refine clinical decision-making and individualize treatment strategies for PTC patients with potential Level V LNM.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1360-1372"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploratory Development of a Prognostic Model for Coronary Artery Disease Utilizing CT-FFR Derived Functional Duke Jeopardy Score","authors":"Li-Na Ouyang,&nbsp;Rui Wang,&nbsp;Qian Wu,&nbsp;Pei Wang,&nbsp;Huai-Rong Zhang,&nbsp;Yuan Li,&nbsp;Li Zhu","doi":"10.1016/j.acra.2024.11.038","DOIUrl":"10.1016/j.acra.2024.11.038","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To explore the prognostic value of the functional Duke Jeopardy Score based on CT-FFR(fDJS_CTA) in assessing major adverse cardiovascular events (MACE) in patients with coronary artery disease (CAD).</div></div><div><h3>Materials and Methods</h3><div>A total of 894 patients with stable CAD with stenosis ranging from 30% to 90%, who underwent CCTA were included in the study. Follow-up was performed to record MACE. The patients were randomly divided into training and validation sets in a 7:3 ratio. In the training set, prognostic analysis was performed and predictive model was constructed using univariable and multivariable Cox regressions and compared the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) of different indicators. The receiver operating characteristic curve, calibration curve and clinical decision curve were used to evaluate the model's discrimination, calibration and clinical efficacy.</div></div><div><h3>Results</h3><div>The median follow-up period was 33 (16–36) months, during which 167 cases (18.68%) of MACE occurred. Males accounted for 61.52% (550/894) of the cohort, with a median age of 61.92 years. The multivariate Cox regression analysis indicated that DJS_CTA (HR: 2.07, 95% CI: 1.17 ∼ 3.68) and fDJS_CTA (HR: 4.68, 95% CI: 2.97 ∼ 7.38) were independent predictors of MACE. Using MACE as a standard, fDJS_CTA improved the risk re-stratification ability of CT-FFR (NRI:0.993, <em>P</em> &lt; 0.001) and the predictive ability of CT-FFR (IDI:0.101, <em>P</em> &lt; 0.001) and DJS_CTA (IDI:0.079, <em>P</em> &lt; 0.001). The prediction model demonstrated high discrimination (training AUC: 0.84 [0.80–0.89]; validation AUC: 0.82 [0.75–0.89]), good calibration and clinical efficacy.</div></div><div><h3>Conclusion</h3><div>The fDJS_CTA was the strongest predictor of MACE. The model constructed based on fDJS_CTA has certain clinical utility in prognostic evaluation for CAD.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1324-1332"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the Procedure: The Hidden Crisis of Moral Injury in Interventional Radiology","authors":"Jose Nicolas Duarte Niño MD. Radiology Resident ,&nbsp;Gonzalo Andrés Montaño Rozo (Associated researcher), MD. Research assistant ,&nbsp;David Fernando Torres Cortes (Associated researcher), MD. Interventional Radiologist ,&nbsp;Alejandra Duarte (Associated researcher), MD. Musculoskeletal Radiologist","doi":"10.1016/j.acra.2024.10.016","DOIUrl":"10.1016/j.acra.2024.10.016","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Page 1779"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Yes, We Can! Ensuring That our Graduating Resident’s Procedural Skills Meet the Needs of Their Patients","authors":"Jessica Fried MD ,&nbsp;Kamran Ali MD ,&nbsp;Alex Podlaski MD ,&nbsp;Dan DePietro MD ,&nbsp;Jeffrey Weinstein MD ,&nbsp;Daniel Rodgers MD ,&nbsp;Bob Pyatt MD ,&nbsp;Victoria Marx MD ,&nbsp;Catherine Keller MD ,&nbsp;Anthony Mancuso MD ,&nbsp;Catherine Everett MD, MBA ,&nbsp;Meredith Englander MD ,&nbsp;Jim Anderson MD ,&nbsp;Anna Rozenshtein MD, MPH ,&nbsp;Mary Scanlon MD","doi":"10.1016/j.acra.2024.11.073","DOIUrl":"10.1016/j.acra.2024.11.073","url":null,"abstract":"<div><h3>Objectives</h3><div>There is a burgeoning discrepancy between the procedural competency of graduating diagnostic radiology residents and the needs of our patient population. The causes of this mismatch and opportunities for improvement are explored by the APDR Procedural Competency of Graduating DR Residents Task Force.</div></div><div><h3>Materials and Methods</h3><div>The APDR convened a task force consisting of diverse broad stakeholder viewpoints, drawing from organized radiology, academic and private practices. The task force conducted structured analyses of the drivers contributing to the current state and reviewed relevant resources, conducted membership surveys, and developed consensus statements regarding solutions to the identified problem.</div></div><div><h3>Results</h3><div>A defined list of procedures a graduating resident is expected to competently perform is established. Key domain-based drivers of the currents state were identified including the ABR initial certification exam structure and content, ACGME practices, creation of the IR-DR residency and ESIR tracks, residency and fellowship training paradigms, and secular trends. The task force offers several best practice recommendations for improving procedural training in DR residency to better meet the needs of the marketplace and our patients.</div></div><div><h3>Conclusion</h3><div>Armed with a defined list of procedures expected of a general radiologist and best practices for enhancing procedural training in diagnostic residencies, the task force presents a national game-plan for improving our ability to deliver high value diagnostic and interventional services to the communities that need it most.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1752-1756"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Neuro-oncology Diagnostics: A Prospective Evaluation of 68Ga-Pentixafor PET/CT Imaging for CXCR4 Overexpression in High-grade Gliomas and Its Implications for Personalized Treatment","authors":"Yanxia Jia ,&nbsp;Shuo Shi ,&nbsp;Chengqiang Jin","doi":"10.1016/j.acra.2024.12.066","DOIUrl":"10.1016/j.acra.2024.12.066","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Page 1773"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI Workflow Integration and Pneumothorax Detection—A Blueprint for Sustainable Implementation?","authors":"Lukas Müller MD/PhD, MBA ,&nbsp;Daniel Pinto dos Santos MD","doi":"10.1016/j.acra.2025.02.003","DOIUrl":"10.1016/j.acra.2025.02.003","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1175-1177"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}