Radiology最新文献

Since the introduction of the American College of Radiology (ACR) MRI safety guidelines in 2002, the indications for use of MRI in clinical care and research have continued to expand. Similarly, MRI technologies have evolved, with multiple field strengths now available for human imaging. While several publications have updated the ACR recommendations since the first guidelines, a single source in a structured format was lacking. Accordingly, the ACR Committee on MR Safety recently updated the online ACR Manual on MR Safety that compiles ACR recommendations for safe use of MRI equipment in humans into a single document. This review describes the new structure of the ACR Manual on MR Safety, discusses new content, indicates gaps in knowledge that require further research, and explains the rationale for the Committee on MR Safety recommendations on certain topics, such as remote operation of MRI systems.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Background In patients who cannot undergo MRI, dual-energy CT (DECT) with bone marrow edema (BME) maps are used as an approach for diagnosing pedal osteomyelitis, but with lower accuracy. Purpose To compare the diagnostic accuracy of additional bone marrow fat maps with that of DECT with BME maps and MRI for pedal osteomyelitis detection. Materials and Methods In this prospective study, thirty-one participants with clinically suspected osteomyelitis of the mid- and forefoot underwent noncontrast DECT (80 kV/140 kV) and MRI between October 2020 and February 2022. With image postprocessing, DECT-derived BME and fat maps were generated. Four independent readers evaluated 3 different image sets for osteomyelitis: DECT and BME maps (set 1); DECT, BME maps and fat maps (set 2); and MRI (set 3). Sensitivity, specificity and accuracy were calculated for each image set, with clinical and microbiological data as the reference standards. In a subanalysis, the DECT BME map, DECT fat map and DECT erosion map were analyzed for their accuracy in predicting bone marrow fat loss at T1-weighted MRI. Results Of the 31 participants included in the study (mean age, 61.7 years ±14.6 [SD]; 21 males) 17 (55%) had osteomyelitis. Sensitivity, specificity and accuracy for detecting osteomyelitis were 47% (8/17), 79% (11/14), and 61% (19/31) (set 1); 77% (13/17), 86% (12/14) and 81% (25/31) (set 2); and 82% (14/17), 93% (13/14) and 87% (27/31) (set 3), respectively. Thirty-one of 661 individual bones (0.5%) showed bone marrow fat loss on T1-weighted MRI; in the subanalysis, DECT fat map specificity was higher than that of the DECT BME map for predicting bone marrow fat loss in individual bones (97% (612/630) vs. 89% (560/630)) (P<.001). Conclusion Pedal osteomyelitis detection with novel DECT-derived fat map imaging in addition to DECT and BME maps was accurate. See also the editorial by Khurana in this issue.

Background Ductal carcinoma in situ (DCIS) is a nonlethal, preinvasive breast cancer for which breast MRI is best suited for accurate disease extent characterization. DCIS is often overtreated, necessitating robust models for improved risk stratification. Purpose To develop logistic regression models using clinical and MRI-based radiomic features of DCIS and to evaluate the performance of such models in predicting disease upstaging at surgery and DCIS score. Materials and Methods This study is a secondary analysis of dynamic contrast-enhanced (DCE) MRI data from the Eastern Cooperative Oncology Group-American College of Radiology Imaging Network, or ECOG-ACRIN, E4112 trial. Primary analysis focused on predicting disease upstaging (n = 295), and secondary analysis focused on predicting DCIS score (n = 174). Radiologist-drawn lesion segmentations and publicly available Cancer Phenomics Toolkit, or CaPTk, software was used to compute 65 radiomic features. Participants were clustered into groups based on their radiomic features (ie, radiomic phenotypes), and principal component analysis was used to summarize the feature space. Clinical information and qualitative MRI features were available. Associations were tested using χ² and likelihood ratio tests. Data were split into training and test sets with a 3:2 ratio, and model performance was assessed on the test set using the area under the receiver operating characteristic curve (AUC). Results Data from 297 female participants with median age of 60 years (IQR, 51-67 years) were analyzed. Two radiomic phenotypes were identified that were associated with disease upstaging (P = .007). For predicting disease upstaging, the top three radiomic principal components combined with clinical and qualitative MRI predictors yielded the highest AUC of 0.77 (95% CI: 0.65, 0.88) among all tested models (P = .007), identifying 25% more true-negative (49 of 93 true-negative findings, 53% specificity) findings, compared with using clinical information alone (23 of 93 true-negative findings, 28% specificity). Radiomic models were not predictive of the DCIS score (P > .05). Conclusion In patients with DCIS, combining radiomic metrics with clinical information improved prediction of disease upstaging but not DCIS score. ClinicalTrials.gov Identifier: NCT02352883 Supplemental material is available for this article. ©RSNA, 2025 See also the editorial by Kim and Woo in this issue.