Radiology-Artificial Intelligence最新文献

Large language models (LLMs) hold substantial promise in addressing the growing workload in radiology, but recent studies also reveal limitations, such as hallucinations and opacity in sources for LLM responses. Retrieval-augmented generation (RAG)-based LLMs offer a promising approach to streamline radiology workflows by integrating reliable, verifiable, and customizable information. Ongoing refinement is critical in order to enable RAG models to manage large amounts of input data and to engage in complex multiagent dialogues. This report provides an overview of recent advances in LLM architecture, including few-shot and zero-shot learning, RAG integration, multistep reasoning, and agentic RAG, and identifies future research directions. Exemplary cases demonstrate the practical application of these techniques in radiology practice. Keywords: Artificial Intelligence, Deep Learning, Natural Language Processing, Tomography, x-Ray © RSNA, 2025.

Purpose To assess the effect of scanner manufacturer and scanning protocol on the performance of deep learning models to classify aggressiveness of prostate cancer (PCa) at biparametric MRI (bpMRI). Materials and Methods In this retrospective study, 5478 cases from ProstateNet, a PCa bpMRI dataset with examinations from 13 centers, were used to develop five deep learning (DL) models to predict PCa aggressiveness with minimal lesion information and test how using data from different subgroups-scanner manufacturers and endorectal coil (ERC) use (Siemens, Philips, GE with and without ERC, and the full dataset)-affects model performance. Performance was assessed using the area under the receiver operating characteristic curve (AUC). The effect of clinical features (age, prostate-specific antigen level, Prostate Imaging Reporting and Data System score) on model performance was also evaluated. Results DL models were trained on 4328 bpMRI cases, and the best model achieved an AUC of 0.73 when trained and tested using data from all manufacturers. Held-out test set performance was higher when models trained with data from a manufacturer were tested on the same manufacturer (within- and between-manufacturer AUC differences of 0.05 on average, P < .001). The addition of clinical features did not improve performance (P = .24). Learning curve analyses showed that performance remained stable as training data increased. Analysis of DL features showed that scanner manufacturer and scanning protocol heavily influenced feature distributions. Conclusion In automated classification of PCa aggressiveness using bpMRI data, scanner manufacturer and ERC use had a major effect on DL model performance and features. Keywords: Convolutional Neural Network (CNN), Computer-aided Diagnosis (CAD), Computer Applications-General (Informatics), Oncology Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Suri and Hsu in this issue.

Purpose To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these values to predict myocardial fibrosis in patients with Duchenne muscular dystrophy (DMD). Materials and Methods This retrospective study included 139 male patients with DMD who underwent cardiac MRI at a single center between February 2018 and April 2023. A DL pipeline was developed to detect five key frames throughout the cardiac cycle and respective dense deformation fields, allowing for phase-specific strain analysis across patients and from one key frame to the next. Effectiveness of these strain values in identifying abnormal deformations associated with fibrotic segments was evaluated in 57 patients (mean age [± SD], 15.2 years ± 3.1), and reproducibility was assessed in 82 patients by comparing the study method with existing feature-tracking and DL-based methods. Statistical analysis compared strain values using t tests, mixed models, and more than 2000 machine learning models; accuracy, F1 score, sensitivity, and specificity are reported. Results DL-based aligned strain identified five times more differences (29 vs five; P < .01) between fibrotic and nonfibrotic segments compared with traditional strain values and identified abnormal diastolic deformation patterns often missed with traditional methods. In addition, aligned strain values enhanced performance of predictive models for myocardial fibrosis detection, improving specificity by 40%, overall accuracy by 17%, and accuracy in patients with preserved ejection fraction by 61%. Conclusion The proposed aligned strain technique enables motion-based detection of myocardial dysfunction at noncontrast cardiac MRI, facilitating detailed interpatient strain analysis and allowing precise tracking of disease progression in DMD. Keywords: Pediatrics, Image Postprocessing, Heart, Cardiac, Convolutional Neural Network (CNN) Duchenne Muscular Dystrophy Supplemental material is available for this article. © RSNA, 2025.

Purpose To evaluate a sham-artificial intelligence (AI) model acting as a placebo control for a standard-AI model for diagnosis of intracranial aneurysm. Materials and Methods This retrospective crossover, blinded, multireader, multicase study was conducted from November 2022 to March 2023. A sham-AI model with near-zero sensitivity and similar specificity to a standard AI model was developed using 16 422 CT angiography examinations. Digital subtraction angiography-verified CT angiographic examinations from four hospitals were collected, half of which were processed by standard AI and the others by sham AI to generate sequence A; sequence B was generated in the reverse order. Twenty-eight radiologists from seven hospitals were randomly assigned to either sequence and then assigned to the other sequence after a washout period. The diagnostic performances of radiologists alone, radiologists with standard-AI assistance, and radiologists with sham-AI assistance were compared using sensitivity and specificity, and radiologists' susceptibility to sham AI suggestions was assessed. Results The testing dataset included 300 patients (median age, 61.0 years [IQR, 52.0-67.0]; 199 male), 50 of whom had aneurysms. Standard AI and sham AI performed as expected (sensitivity, 96.0% vs 0.0%; specificity, 82.0% vs 76.0%). The differences in sensitivity and specificity between standard AI-assisted and sham AI-assisted readings were 20.7% (95% CI: 15.8, 25.5 [superiority]) and 0.0% (95% CI: -2.0, 2.0 [noninferiority]), respectively. The difference between sham AI-assisted readings and radiologists alone was -2.6% (95% CI: -3.8, -1.4 [noninferiority]) for both sensitivity and specificity. After sham-AI suggestions, 5.3% (44 of 823) of true-positive and 1.2% (seven of 577) of false-negative results of radiologists alone were changed. Conclusion Radiologists' diagnostic performance was not compromised when aided by the proposed sham-AI model compared with their unassisted performance. Keywords: CT Angiography, Vascular, Intracranial Aneurysm, Sham AI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Mayfield and Romero in this issue.

Purpose To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weight language models (LMs) and retrieval-augmented generation (RAG) and to assess the effects of model configuration variables on extraction performance. Materials and Methods This retrospective study used two datasets: 7294 radiology reports annotated for Brain Tumor Reporting and Data System (BT-RADS) scores and 2154 pathology reports annotated for IDH mutation status (January 2017-July 2021). An automated pipeline was developed to benchmark the performance of various LMs and RAG configurations for accuracy of structured data extraction from reports. The effect of model size, quantization, prompting strategies, output formatting, and inference parameters on model accuracy was systematically evaluated. Results The best-performing models achieved up to 98% accuracy in extracting BT-RADS scores from radiology reports and greater than 90% accuracy for extraction of IDH mutation status from pathology reports. The best model was medical fine-tuned Llama 3. Larger, newer, and domain fine-tuned models consistently outperformed older and smaller models (mean accuracy, 86% vs 75%; P < .001). Model quantization had minimal effect on performance. Few-shot prompting significantly improved accuracy (mean [±SD] increase, 32% ± 32; P = .02). RAG improved performance for complex pathology reports by a mean of 48% ± 11 (P = .001) but not for shorter radiology reports (-8% ± 31; P = .39). Conclusion This study demonstrates the potential of open LMs in automated extraction of structured clinical data from unstructured clinical reports with local privacy-preserving application. Careful model selection, prompt engineering, and semiautomated optimization using annotated data are critical for optimal performance. Keywords: Large Language Models, Retrieval-Augmented Generation, Radiology, Pathology, Health Care Reports Supplemental material is available for this article. © RSNA, 2025 See also commentary by Tejani and Rauschecker in this issue.

Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier leakage detection using dynamic contrast-enhanced MRI, without requiring pharmacokinetic models and arterial input function estimation. Materials and Methods This retrospective study included data from patients who underwent dynamic contrast-enhanced MRI between April 2010 and December 2020. An autoencoder-based anomaly detection approach identified one-dimensional voxel-wise time-series abnormal signals through reconstruction residuals, separating them into residual leakage signals (RLSs) and residual vascular signals. The RLS maps were evaluated and compared with the volume transfer constant (K^trans) using the structural similarity index and correlation coefficient. Generalizability was tested on subsampled data, and isocitrate dehydrogenase (IDH) status classification performance was assessed using area under the receiver operating characteristic curve (AUC). Results A total of 274 patients (mean age, 54.4 years ± 14.6 [SD]; 164 male) were included in the study. RLS showed high structural similarity (structural similarity index, 0.91 ± 0.02) and correlation (r = 0.56; P < .001) with K^trans. On subsampled data, RLS maps showed better correlation with RLS values from the original data (0.89 vs 0.72; P < .001), higher peak signal-to-noise ratio (33.09 dB vs 28.94 dB; P < .001), and higher structural similarity index (0.92 vs 0.87; P < .001) compared with K^trans maps. RLS maps also outperformed K^trans maps in predicting IDH mutation status (AUC, 0.87 [95% CI: 0.83, 0.91] vs 0.81 [95% CI: 0.76, 0.85]; P = .02). Conclusion The unsupervised framework effectively detected blood-brain barrier leakage without pharmacokinetic models and arterial input function. Keywords: Dynamic Contrast-enhanced MRI, Unsupervised Learning, Feature Detection, Blood-Brain Barrier Leakage Detection Supplemental material is available for this article. © RSNA, 2025 See also commentary by Júdice de Mattos Farina and Kuriki in this issue.