Radiology-Artificial Intelligence最新文献

{"title":"Context Is Everything: Understanding Variable LLM Performance for Radiology Retrieval-Augmented Generation.","authors":"Aawez Mansuri, Judy W Gichoya","doi":"10.1148/ryai.250187","DOIUrl":"10.1148/ryai.250187","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250187"},"PeriodicalIF":13.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Performance of a Deep Learning Multilabel Segmentation Model to Quantify Acute and Chronic Brain Lesions at MRI after Stroke and Predict Prognosis.","authors":"Tianyu Tang, Ying Cui, Chunqiang Lu, Huiming Li, Jiaying Zhou, Xiaoyu Zhang, Yujie Zhou, Ying Zhang, Yi Zhang, Yuhao Xu, Yuefeng Li, Shenghong Ju","doi":"10.1148/ryai.240072","DOIUrl":"10.1148/ryai.240072","url":null,"abstract":"<p><p>Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after acute ischemic stroke (AIS) and assess relationships between clinical and model-extracted radiologic features of the lesions and patient prognosis. Materials and Methods This retrospective study included patients with AIS from multiple centers, who experienced stroke onset between September 2008 and October 2022 and underwent MRI as well as thrombolytic therapy and/or treatment with antiplatelets or anticoagulants. A SegResNet-based deep learning model was developed to segment core infarcts and white matter hyperintensity (WMH) burdens on diffusion-weighted and fluid-attenuated inversion recovery images. The model was trained, validated, and tested with manual labels (260, 60, and 40 patients in each dataset, respectively). Radiologic features extracted from the model, including regional infarct size and periventricular and deep WMH volumes and cluster numbers, combined with clinical variables, were used to predict favorable versus unfavorable patient outcomes at 7 days (modified Rankin Scale [mRS] score). Mediation analyses explored associations between radiologic features and AIS outcomes within different treatment groups. Results A total of 1008 patients (mean age, 67.0 years ± 11.8 [SD]; 686 male, 322 female) were included. The training and validation dataset comprised 702 patients with AIS, and the two external testing datasets included 206 and 100 patients, respectively. The prognostic model combining clinical and radiologic features achieved areas under the receiver operating characteristic curve of 0.81 (95% CI: 0.74, 0.88) and 0.77 (95% CI: 0.68, 0.86) for predicting 7-day outcomes in the two external testing datasets, respectively. Mediation analyses revealed that deep WMH in patients treated with thrombolysis had a significant direct effect (17.7%, <i>P</i> = .01) and indirect effect (10.7%, <i>P</i> = .01) on unfavorable outcomes, as indicated by higher mRS scores, which was not observed in patients treated with antiplatelets and/or anticoagulants. Conclusion The proposed deep learning model quantitatively analyzed radiologic features of acute and chronic brain lesions, and the extracted radiologic features combined with clinical variables predicted short-term AIS outcomes. WMH burden, particularly deep WMH, emerged as a risk factor for poor outcomes in patients treated with thrombolysis. <b>Keywords:</b> MR-Diffusion Weighted Imaging, Thrombolysis, Head/Neck, Brain/Brain Stem, Stroke, Outcomes Analysis, Segmentation, Prognosis, Supervised Learning, Convolutional Neural Network (CNN), Support Vector Machines <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240072"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishing a Chain of Evidence for AI in Radiology: Sham AI and Randomized Controlled Trials.","authors":"John D Mayfield, Javier Romero","doi":"10.1148/ryai.250334","DOIUrl":"10.1148/ryai.250334","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250334"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144162292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning and Deep Learning Models for Automated Protocoling of Emergency Brain MRI Using Text from Clinical Referrals.","authors":"Heidi J Huhtanen, Mikko J Nyman, Antti Karlsson, Jussi Hirvonen","doi":"10.1148/ryai.230620","DOIUrl":"10.1148/ryai.230620","url":null,"abstract":"<p><p>Purpose To develop and evaluate machine learning and deep learning-based models for automated protocoling of emergency brain MRI scans based on clinical referral text. Materials and Methods In this single-institution, retrospective study of 1953 emergency brain MRI referrals from January 2016 to January 2019, two neuroradiologists labeled the imaging protocol and use of contrast agent as the reference standard. Three machine learning algorithms (naive Bayes, support vector machine, and XGBoost) and two pretrained deep learning models (Finnish bidirectional encoder representations from transformers [BERT] and generative pretrained transformer [GPT]-3.5 [GPT-3.5 Turbo; Open AI]) were developed to predict the MRI protocol and need for a contrast agent. Each model was trained with three datasets (100% of training data, 50% of training data, and 50% plus augmented training data). Prediction accuracy was assessed with a test set. Results The GPT-3.5 models trained with 100% of the training data performed best in both tasks, achieving an accuracy of 84% (95% CI: 80, 88) for the correct protocol and 91% (95% CI: 88, 94) for the contrast agent. BERT had an accuracy of 78% (95% CI: 74, 82) for the protocol and 89% (95% CI: 86, 92) for the contrast agent. The best machine learning model in the protocol task was XGBoost (accuracy, 78%; 95% CI: 73, 82), and the best machine learning models in the contrast agent task were support vector machine and XGBoost (accuracy, 88%; 95% CI: 84, 91 for both). The accuracies of two nonneuroradiologists were 80%-83% in the protocol task and 89%-91% in the contrast medium task. Conclusion Machine learning and deep learning models demonstrated high performance in automatic protocoling of emergency brain MRI scans based on text from clinical referrals. <b>Keywords:</b> Natural Language Processing, Automatic Protocoling, Deep Learning, Machine Learning, Emergency Brain MRI <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license. See also commentary by Strotzer in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230620"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"External Testing of a Commercial AI Algorithm for Breast Cancer Detection at Screening Mammography.","authors":"John Brandon Graham-Knight, Pengkun Liang, Wenna Lin, Quinn Wright, Hua Shen, Colin Mar, Janette Sam, Rasika Rajapakshe","doi":"10.1148/ryai.240287","DOIUrl":"10.1148/ryai.240287","url":null,"abstract":"<p><p>Purpose To test a commercial artificial intelligence (AI) system for breast cancer detection at the BC Cancer Breast Screening Program. Materials and Methods In this retrospective study of 136 700 female individuals (mean age, 58.8 years ± 9.4 [SD]; median, 59.0 years; IQR = 14.0) who underwent digital mammography screening in British Columbia, Canada, between February 2019 and January 2020, breast cancer detection performance of a commercial AI algorithm was stratified by demographic, clinical, and imaging features and evaluated using the area under the receiver operating characteristic curve (AUC), and AI performance was compared with radiologists, using sensitivity and specificity. Results At 1-year follow-up, the AUC of the AI algorithm was 0.93 (95% CI: 0.92, 0.94) for breast cancer detection. Statistically significant differences were found for mammograms across radiologist-assigned Breast Imaging Reporting and Data System breast densities: category A, AUC of 0.96 (95% CI: 0.94, 0.99); category B, AUC of 0.94 (95% CI: 0.92, 0.95); category C, AUC of 0.93 (95% CI: 0.91, 0.95), and category D, AUC of 0.84 (95% CI: 0.76, 0.91) (A_AUC > D_AUC, <i>P</i> = .002; B_AUC > D_AUC, <i>P</i> = .009; C_AUC > D_AUC, <i>P</i> = .02). The AI showed higher performance for mammograms with architectural distortion (0.96 [95% CI: 0.94, 0.98]) versus without (0.92 [95% CI: 0.90, 0.93], <i>P</i> = .003) and lower performance for mammograms with calcification (0.87 [95% CI: 0.85, 0.90]) versus without (0.92 [95% CI: 0.91, 0.94], <i>P</i> < .001). Sensitivity of radiologists (92.6% ± 1.0) exceeded the AI algorithm (89.4% ± 1.1, <i>P</i> = .01), but there was no evidence of difference at 2-year follow-up (83.5% ± 1.2 vs 84.3% ± 1.2, <i>P</i> = .69). Conclusion The tested commercial AI algorithm is generalizable for a large external breast cancer screening cohort from Canada but showed different performance for some subgroups, including those with architectural distortion or calcification in the image. <b>Keywords:</b> Mammography, QA/QC, Screening, Technology Assessment, Screening Mammography, Artificial Intelligence, Breast Cancer, Model Testing, Bias and Fairness <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license. See also commentary by Milch and Lee in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240287"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Major Adverse Cardiac Events Using Deep Learning-based Coronary Artery Disease Analysis at CT Angiography.","authors":"Jin Young Kim, Kye Ho Lee, Ji Won Lee, Jiyong Park, Jinho Park, Pan Ki Kim, Kyunghwa Han, Song-Ee Baek, Dong Jin Im, Byoung Wook Choi, Jin Hur","doi":"10.1148/ryai.240459","DOIUrl":"10.1148/ryai.240459","url":null,"abstract":"<p><p>Purpose To evaluate the predictive value of deep learning (DL)-based coronary artery disease (CAD) extent analysis for major adverse cardiac events (MACEs) in patients with acute chest pain presenting to the emergency department (ED). Materials and Methods This retrospective multicenter observational study included consecutive patients with acute chest pain who underwent coronary CT angiography (CCTA) at three institutional EDs from January 2018 to December 2022. Patients were classified as having no CAD, nonobstructive CAD, or obstructive CAD using a DL model. The primary outcome was MACEs during follow-up, defined as a composite of cardiac death, nonfatal myocardial infarction, and hospitalization for unstable angina. Cox proportional hazards regression models were used to evaluate the predictors of MACEs. Results The study included 408 patients (224 male; mean age, 59.4 years ± 14.6 [SD]). The DL model classified 162 (39.7%) patients as having no CAD, 94 (23%) as having nonobstructive CAD, and 152 (37.3%) as having obstructive CAD. Sixty-three (15.4%) patients experienced MACEs during follow-up. Patients with MACEs had a higher prevalence of obstructive CAD than those without (<i>P</i> < .001). In the multivariate analysis of model 1 (clinical risk factors), dyslipidemia (hazard ratio [HR], 2.15) and elevated troponin T levels (HR, 2.13) were predictive of MACEs (all <i>P</i> < .05). In model 2 (clinical risk factors plus DL-based CAD extent), obstructive CAD detected by the DL model was the most significant independent predictor of MACEs (HR, 88.07; <i>P</i> < .001). Harrell C statistic showed that DL-based CAD extent enhanced the risk stratification beyond clinical risk factors (Harrell C statistics: 0.94 vs 0.80, <i>P</i> < .001). Conclusion DL-based detection of obstructive CAD demonstrated stronger predictive value than clinical risk factors for MACEs in patients with acute chest pain presenting to the ED. <b>Keywords:</b> Cardiac, CT-Angiography, Outcomes Analysis © RSNA, 2025 See also commentary by Reddy in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240459"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishing the Evidence Needed for AI-driven Mammography Screening.","authors":"Hannah S Milch, Christoph I Lee","doi":"10.1148/ryai.250152","DOIUrl":"10.1148/ryai.250152","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250152"},"PeriodicalIF":13.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal Hindsight, Clinical Foresight: Longitudinal Lymphoma Analysis at PET/CT.","authors":"Bardia Khosravi, Judy W Gichoya","doi":"10.1148/ryai.250149","DOIUrl":"10.1148/ryai.250149","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250149"},"PeriodicalIF":13.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Double Reading: Multiple Deep Learning Models Enhancing Radiologist-led Breast Screening.","authors":"Alexandre Cadrin-Chênevert","doi":"10.1148/ryai.250125","DOIUrl":"10.1148/ryai.250125","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250125"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Mortality with Deep Learning: Are Metrics Alone Enough?","authors":"Eduardo Moreno Júdice de Mattos Farina, Paulo Eduardo de Aguiar Kuriki","doi":"10.1148/ryai.250224","DOIUrl":"10.1148/ryai.250224","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250224"},"PeriodicalIF":13.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}