Academic Radiology最新文献

{"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":"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":"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":"The Thickness of the Hypoechoic Halo of Thyroid Nodules May Help to Recognize Thyroid Cancer.","authors":"Jiaqi Ji, Xinlong Shi, Lei Liang","doi":"10.1016/j.acra.2025.02.049","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.049","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"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","doi":"10.1016/j.acra.2025.03.030","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.030","url":null,"abstract":"","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":"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":"https://doi.org/10.1016/j.acra.2025.03.027","url":null,"abstract":"","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":"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}

{"title":"Using Optimal Feature Selection and Continuous Learning to Implement Efficient Model Arrays for Predicting Daily Clinical Radiology Workload.","authors":"Leslie K Lee, Melissa Viator, Catherine S Giess, Michael Gee, Ray Huang, Fionnuala McPeake, Oleg S Pianykh","doi":"10.1016/j.acra.2025.02.051","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.051","url":null,"abstract":"<p><strong>Rationale and objective: </strong>Clinical workload can fluctuate daily in radiology practice. We sought to design, validate, and implement an efficient and sustainable machine learning model to forecast daily clinical image interpretation workload.</p><p><strong>Materials and methods: </strong>A year of radiology exam volume data at two academic medical centers was analyzed with an optimal feature selection algorithm and several machine learning models, to produce the most accurate and explainable prediction of the next weekday's clinical workload. Continuous learning was used to maintain high model quality over time.</p><p><strong>Results: </strong>After evaluating several AI models of differing complexity on a large set of 707 workflow features, a continuously learning linear regression model array was selected based on three optimal features: the current number of unread exams, the number of exams scheduled to be performed after 5 pm, and the number of exams scheduled to be performed the next day. The model array had an average R² of 0.83 (IQR 0.13) across the tested radiology divisions; it significantly outperformed trivial estimates and provided an accurate daily prediction pattern. The solution was successfully implemented into an online dashboard, displaying the forecasted clinical volume as a percentile in reference to the past year's daily clinical volume. Retraining the model on a weekly basis using live data resulted in high, and sometimes increased, model quality.</p><p><strong>Conclusion: </strong>An AI model can be developed and implemented to forecast daily clinical radiology workload, as a practice management tool.</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":"143722581","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":"Knowledge Graph-Based Few-Shot Learning for Label of Medical Imaging Reports.","authors":"Tiancheng Li, Yuxuan Zhang, Deyu Su, Ming Liu, Mingxin Ge, Linyu Chen, Chuanfu Li, Jin Tang","doi":"10.1016/j.acra.2025.02.045","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.045","url":null,"abstract":"<p><strong>Background: </strong>The application of artificial intelligence (AI) in the field of automatic imaging report labeling faces the challenge of manually labeling large datasets.</p><p><strong>Purpose: </strong>To propose a data augmentation method by using knowledge graph (KG) and few-shot learning.</p><p><strong>Methods: </strong>A KG of lumbar spine X-ray images was constructed, and 2000 data were annotated based on the KG, which were divided into training, validation, and test sets in a ratio of 7:2:1. The training dataset was augmented based on the synonym/replacement attributes of the KG and was the augmented data was input into the BERT (Bidirectional Encoder Representations from Transformers) model for automatic annotation training. The performance of the model under different augmentation ratios (1:10, 1:100, 1:1000) and augmentation methods (synonyms only, replacements only, combination of synonyms and replacements) was evaluated using the precision and F1 scores. In addition, with the augmentation ratio was fixed, iterative experiments were performed by supplementing the data of nodes that perform poorly in the validation set to further improve model's performance.</p><p><strong>Results: </strong>Prior to data augmentation, the precision was 0.728 and the F1 score was 0.666. By adjusting the augmentation ratio, the precision increased from 0.912 at a 1:10 augmentation ratio to 0.932 at a 1:100 augmentation ratio (P<.05), while F1 score improved from 0.853 at a 1:10 augmentation ratio to 0.881 at a 1:100 augmentation ratio (P<.05). Additionally, the effectiveness of various augmentation methods was compared at a 1:100 augmentation ratio. The augmentation method that combined synonyms and replacements (F1=0.881) was superior to the methods that only used synonyms (F1=0.815) and only used replacements (F1=0.753) (P<.05). For nodes that exhibited suboptimal performance on the validation set, supplementing the training set with target data improved model performance, increasing the average F1 score to 0.979 (P<.05).</p><p><strong>Conclusion: </strong>Based on the KG, this study trained an automatic labeling model of radiology reports using a few-shot data set. This method effectively reduces the workload of manual labeling, improves the efficiency and accuracy of image data labeling, and provides an important research strategy for the application of AI in the domain of automatic labeling of image reports.</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":"143722578","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}