Academic Radiology最新文献

{"title":"Missed Cancers at Prostate MRI: Can Growth Pattern Analysis on Digital Pathology Improve Detection?","authors":"Baris Turkbey MD , Stephanie A. Harmon PhD","doi":"10.1016/j.acra.2025.03.032","DOIUrl":"10.1016/j.acra.2025.03.032","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 5","pages":"Pages 2698-2699"},"PeriodicalIF":3.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755777","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":"Non-invasive Imaging Findings of Wild-type Transthyretin Amyloid Cardiomyopathy in Women: A Retrospective Study.","authors":"Fumihiro Yoshimura, Seitaro Oda, Masafumi Kidoh, Shinpei Yamaguchi, Seiji Takashio, Naoto Kuyama, Tetsuya Oguni, Hiroki Usuku, Yasuhiro Izumiya, Yasunori Nagayama, Takeshi Nakaura, Kenichi Tsujita, Toshinori Hirai","doi":"10.1016/j.acra.2025.03.009","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.009","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) predominantly affects males; however, female patients can also develop this condition. This study assessed the non-invasive imaging features of ATTRwt-CM in female patients.</p><p><strong>Materials and methods: </strong>In this study, 106 consecutive patients diagnosed with ATTRwt-CM were retrospectively analyzed, evaluating sex-related differences in imaging features, including echocardiography, cardiac magnetic resonance (CMR), and ^99mTc-labeled pyrophosphate (^99mTc-PYP) scintigraphy.</p><p><strong>Results: </strong>12 of 106 patients (11.3%) were female. They were significantly older at diagnosis (75 years [interquartile range, 71-79 years] vs. 79 years [77-83 years]; p<0.01). The proportion of female patients increased with age, from 7.5% (6/80) in those aged <80 years to 23.1% (6/26) in those aged ≥80 years. CMR-measured left ventricular ejection fraction (LVEF) was significantly higher in female patients (50.0% [42.0-61.0%] vs. 59.5% [44.3-72.3%]; p=0.04). No significant sex-related differences in LV mass and global longitudinal strain were observed. In T1 mapping, no significant difference in native T1 was observed; however, the extracellular volume fraction (ECV) was significantly lower in female patients (54.2% [46.5-66.0%] vs. 50.4% [42.0-55.0%]; p=0.04). Furthermore, no significant difference in myocardial T2 value was observed. Late gadolinium enhancement was more extensively observed in males than in females. The heart-to-contralateral ratio in ^99mTc-PYP scintigraphy was significantly lower among female patients (1.88 [1.70-2.04] vs. 1.64 [1.58-1.74]; p=0.02).</p><p><strong>Conclusion: </strong>CMR findings revealed that females exhibited higher LVEF, lower ECV, and weaker cardiac uptake on ^99mTc-PYP scintigraphy, indicating a milder myocardial amyloid burden. No significant sex-related differences in echocardiographic parameters or other CMR indices were observed.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755812","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":"Multivariate Classification of Adolescent Major Depressive Disorder Using Whole-brain Functional Connectivity.","authors":"Zhong Li, Yanrui Shen, Meng Zhang, Xuekun Li, Baolin Wu","doi":"10.1016/j.acra.2025.02.052","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.052","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional connectivity (FC) patterns within the brain. However, whether FC could be used as a potential biomarker for diagnosis of adolescent MDD is still unclear. The aim of our study was to investigate the potential diagnostic value of whole-brain FC in adolescent MDD.</p><p><strong>Methods: </strong>Resting-state functional magnetic resonance imaging data were obtained from 94 adolescents with MDD and 78 healthy adolescents. The whole brain was segmented into 90 regions of interest (ROIs) using the automated anatomical labeling atlas. FC was assessed by calculating the Pearson correlation coefficient of the average time series between each pair of ROIs. A multivariate pattern analysis was employed to classify patients from controls using the whole-brain FC as input features.</p><p><strong>Results: </strong>The linear support vector machine classifier achieved an accuracy of 69.18% using the optimal functional connection features. The consensus functional connections were mainly located within and between large-scale brain networks. The top 10 nodes with the highest weight in the classification model were mainly located in the default mode, salience, auditory, and sensorimotor networks.</p><p><strong>Conclusion: </strong>Our findings highlighted the importance of functional network connectivity in the neurobiology of adolescent MDD, and suggested the possibility of altered FC and high-weight regions as complementary diagnostic markers in adolescents with depression.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755810","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":"Exploring the Incremental Value of Aorta Enhancement Normalization Method in Evaluating Renal Cell Carcinoma Histological Subtypes: A Multi-center Large Cohort Study.","authors":"Zexin Huang, Lei Wang, Hangru Mei, Jiewen Liu, Haoyang Zeng, Weihao Liu, Haoyuan Yuan, Kai Wu, Hanlin Liu","doi":"10.1016/j.acra.2025.03.007","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.007","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>The classification of renal cell carcinoma (RCC) histological subtypes plays a crucial role in clinical diagnosis. However, traditional image normalization methods often struggle with discrepancies arising from differences in imaging parameters, scanning devices, and multi-center data, which can impact model robustness and generalizability.</p><p><strong>Materials and methods: </strong>This study included 1628 patients with pathologically confirmed RCC who underwent nephrectomy across eight cohorts. These were divided into a training set, a validation set, external test dataset 1, and external test dataset 2. We proposed an \"Aortic Enhancement Normalization\" (AEN) method based on the lesion-to-aorta enhancement ratio and developed an automated lesion segmentation model along with a multi-scale CT feature extractor. Several machine learning algorithms, including Random Forest, LightGBM, CatBoost, and XGBoost, were used to build classification models and compare the performance of the AEN and traditional approaches for evaluating histological subtypes (clear cell renal cell carcinoma [ccRCC] vs. non-ccRCC). Additionally, we employed SHAP analysis to further enhance the transparency and interpretability of the model's decisions.</p><p><strong>Results: </strong>The experimental results demonstrated that the AEN method outperformed the traditional normalization method across all four algorithms. Specifically, in the XGBoost model, the AEN method significantly improved performance in both internal and external validation sets, achieving AUROC values of 0.89, 0.81, and 0.80, highlighting its superior performance and strong generalizability. SHAP analysis revealed that multi-scale CT features played a critical role in the model's decision-making process.</p><p><strong>Conclusion: </strong>The proposed AEN method effectively reduces the impact of imaging parameter differences, significantly improving the robustness and generalizability of histological subtype (ccRCC vs. non-ccRCC) models. This approach provides new insights for multi-center data analysis and demonstrates promising clinical applicability.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744368","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":"Multimodal Deep Learning for Grading Carpal Tunnel Syndrome: A Multicenter Study in China.","authors":"Xiaochen Shi, Tianxiang Yu, Yu Yuan, Dan Wang, Jinhua Cui, Ling Bai, Fang Zheng, Xiaobin Dai, Zhuhuang Zhou","doi":"10.1016/j.acra.2025.02.043","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.043","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Ultrasound (US)-based deep learning (DL) models for grading the severity of carpal tunnel syndrome (CTS) are scarce. We aimed to advance CTS grading by developing a joint-DL model integrating clinical information and multimodal US features.</p><p><strong>Materials and methods: </strong>A retrospective dataset of CTS patients from three hospitals was randomly divided into the training (n=680) and internal validation (n=173) sets. An external validation set was prospectively recruited from another hospital (n=174). To further test the model's generalizability, cross-vendor testing was conducted at three additional hospitals utilizing different US systems in the external validation set 2 (n=224). An US-based model was developed to grade CTS severity utilizing multimodal sonographic features, including cross-sectional area [CSA], echogenicity, longitudinal nerve appearance, and intraneural vascularity. A joint-DL model (CTSGrader) was constructed integrating sonographic features and clinical information. Diagnostic performance of both models was verified based on electrophysiological results. In the validation sets, the better-performing model was compared to two junior and two senior radiologists. Additionally, the radiologists' diagnostic performance with artificial intelligence (AI) assistance was evaluated in external validation sets.</p><p><strong>Results: </strong>CTSGrader achieved areas under the curve (AUCs) of 0.951, 0.910, and 0.897 in the validation sets. The accuracies of CTSGrader were 0.849, 0.833, and 0.827, which were higher than those of US-based model (all p<.05). It outperformed two junior and one senior radiologists (all p<.05) and was equivalent to 1 senior radiologist (all p>.05). With its assistance, the accuracies of two junior and one senior radiologists were improved (all p<.05).</p><p><strong>Conclusion: </strong>The joint-DL model (CTSGrader) developed in our study outperformed the single-modality model. The AI-aided strategy suggested its potential to support clinical decision-making for grading CTS severity.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744370","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":"The Thickness of the Hypoechoic Halo of Thyroid Nodules May Help to Recognize Thyroid Cancer","authors":"Jiaqi Ji MM ,&nbsp;Xinlong Shi MM ,&nbsp;Lei Liang MD","doi":"10.1016/j.acra.2025.02.049","DOIUrl":"10.1016/j.acra.2025.02.049","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 5","pages":"Page 3133"},"PeriodicalIF":3.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744371","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":"Y-90 Selective Internal Radiation Therapy for Inoperable, Chemotherapy-Resistant Liver Metastases: A Meta-analysis.","authors":"Shengxiang Hou, Manjun Deng, Zonghao Hou, Zhixin Wang, Haijiu Wang, Haining Fan","doi":"10.1016/j.acra.2025.01.044","DOIUrl":"https://doi.org/10.1016/j.acra.2025.01.044","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Yttrium-90 (Y-90) radioembolization has emerged as an effective therapeutic modality for patients with liver metastases, despite the absence of Level I evidence. The objective of this study is to evaluate the efficacy of this treatment approach through a meta-analysis of the available literature.</p><p><strong>Methods: </strong>A comprehensive review protocol was implemented to screen all relevant reports in the literature. Strict inclusion criteria were applied to ensure consistency among the following selected studies: individual and complete data on Y-90 treatment for liver metastases, even if the studies included various tumor types. The selected studies were rigorously assessed according to the Reporting Standards of Radioembolization, based on 28 study criteria. Response data were extracted and analyzed using both fixed-effect and random-effect meta-analysis models.</p><p><strong>Results: </strong>A total of 28 studies, involving 1662 patients, were included. Begg's test showed no significant evidence of publication bias. The random-effects weighted average overall response rate (complete response [CR] and partial response [PR]) was 34% (range: 26%-44%, I²=86%). The disease control rate (CR, PR, and stable disease [SD]) was 64% (range: 53%-75%, I²=91%). The progressive disease rate was 24% (range: 16%-33%, I²=87%), while the rate of adverse events was 57% (range: 16%-93%, I²=98%). The rate of grade 3-5 adverse events was 20% (range: 7%-36%, I²=9%).</p><p><strong>Conclusion: </strong>This meta-analysis confirms that Yttrium-90 radioembolization is an effective treatment option for patients with liver metastases, demonstrating a high disease control rate with a relatively low incidence of severe adverse reactions.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732483","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 Bone Marrow MRI Segmentation Using Deep Learning-Based Frameworks","authors":"Arosh Shavinda Perera Molligoda Arachchige MD","doi":"10.1016/j.acra.2025.03.030","DOIUrl":"10.1016/j.acra.2025.03.030","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 5","pages":"Pages 2836-2837"},"PeriodicalIF":3.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732244","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":"A Novel Visual Model for Predicting Prognosis of Resected Hepatoblastoma: A Multicenter Study.","authors":"Ying He, Chaohui An, Kuiran Dong, Zhibao Lyu, Shanlu Qin, Kezhe Tan, Xiwei Hao, Chengzhan Zhu, Wenli Xiu, Bin Hu, Nan Xia, Chaojin Wang, Qian Dong","doi":"10.1016/j.acra.2025.03.004","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.004","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study aimed to evaluate the application of a contrast-enhanced CT-based visual model in predicting postoperative prognosis in patients with hepatoblastoma (HB).</p><p><strong>Materials and methods: </strong>We analyzed data from 224 patients across three centers (178 in the training cohort, 46 in the validation cohort). Visual features were extracted from contrast-enhanced CT images, and key features, along with clinicopathological data, were identified using LASSO Cox regression. Visual (DINOv2_score) and clinical (Clinical_score) models were developed, and a combined model integrating DINOv2_score and clinical risk factors was constructed. Nomograms were created for personalized risk assessment, with calibration curves and decision curve analysis (DCA) used to evaluate model performance.</p><p><strong>Results: </strong>The DINOv2_score was recognized as a key prognostic indicator for HB. In both the training and validation cohorts, the combined model demonstrated superior performance in predicting disease-free survival (DFS) [C-index (95% CI): 0.886 (0.879-0.895) and 0.873 (0.837-0.909), respectively] and overall survival (OS) [C-index (95% CI): 0.887 (0.877-0.897) and 0.882 (0.858-0.906), respectively]. Calibration curves showed strong alignment between predicted and observed outcomes, while DCA demonstrated that the combined model provided greater clinical net benefit than the clinical or visual models alone across a range of threshold probabilities.</p><p><strong>Conclusion: </strong>The contrast-enhanced CT-based visual model serves as an effective tool for predicting postoperative prognosis in HB patients. The combined model, integrating the DINOv2_score and clinical risk factors, demonstrated superior performance in survival prediction, offering more precise guidance for personalized treatment strategies.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722575","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":"Large Language Models with Image Processing Capabilities: An Inevitable yet Undetermined Presence in Radiology Practice and Education","authors":"Erin Gomez","doi":"10.1016/j.acra.2025.03.027","DOIUrl":"10.1016/j.acra.2025.03.027","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 5","pages":"Pages 3103-3105"},"PeriodicalIF":3.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722579","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}