Brain metastasis (BM) frequently occurs in advanced cancer patients, and accurate identification of the primary origin is essential for personalized treatment. Conventional imaging often lacks sensitivity and specificity for BM source prediction. This study aimed to develop and validate a multiparametric MRI-based radiomics model emphasizing brain–tumor interface (BTI) features to improve noninvasive BM origin identification. The primary cohort included 429 patients (740 BMs) from two centers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), breast cancer (BC), and gastrointestinal cancer (GIC), and an independent validation cohort of 33 patients (84 BMs) from a third center. All underwent pre-treatment 3.0 T MRI with T1-weighted contrast-enhanced (T1CE) and T2-weighted (T2W) sequences. Radiomic features were extracted from tumor core and BTI regions (2, 4, 6, 8 mm). Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and radiomics signatures (RS) were evaluated using receiver operating characteristic (ROC) analysis. The constructed RS-BTI4 based on the 4-mm BTI region generated the best overall performance, with area under the ROC curve (AUCs) in the training cohort were 0.855 (LC/NLC), 0.910 (SCLC/NSCLC), and 0.890 (BC/GIC); in validation cohort, 0.726, 0.856, and 0.809; and in external validation cohort, 0.739, 0.750, and 0.792, respectively. RS-BTI4 consistently outperformed models based on other BTI radial widths and the whole tumor region in identifying the primary origin of BM. BTI-derived radiomic features demonstrate strong potential for noninvasive identification of BM origin, with the 4-mm BTI region showing optimal predictive performance. Question Conventional MRI cannot reliably identify the primary origin of brain metastases, particularly when the primary tumor remains unknown at diagnosis. Findings The 4-mm brain–tumor interface model showed the most favorable overall performance, measured by area under the curve, across three binary classification tasks and validation cohorts. Critical relevance statement Integrating brain–tumor interface radiomics with multiparametric MRI may improve noninvasive estimation of BM origin and support more targeted diagnostic evaluation in clinical radiology.
The 2025 update of the International Society for the Study of Vascular Anomalies (ISSVA) classification introduced several imaging-relevant changes, including the recognition of potentially unique vascular anomalies. Although the main framework for vascular tumors remains unchanged, with continued subdivision into benign, borderline, and malignant tumors, the most radiologically relevant structural changes concern vascular malformations, which were reorganized into fast-flow lesions, slow-flow lesions, and developmental anomalies of named vessels. These updates are important for radiologists because imaging plays a central role in assessing hemodynamic characteristics, defining lesion extent, and identifying findings relevant to procedural planning and multidisciplinary management. This educational review presents an author-derived four-step framework that organizes established MRI observations for peripheral and soft-tissue vascular malformations according to the structure and terminology of the 2025 ISSVA classification, including hemodynamic assessment, vascular components, tissue composition, lesion extent, and treatment-relevant anatomy. Question Imaging-relevant organizational and terminology changes in the 2025 ISSVA classification require practical guidance for consistent MRI assessment and reporting of peripheral and soft-tissue vascular malformations. Findings An author-derived four-step, flow-based framework organizes hemodynamics, dominant vascular components, tissue composition, lesion extent, and treatment-relevant anatomy into structured MRI assessment. Critical relevance statement The MRI-focused framework provides a practical way to apply the 2025 ISSVA classification consistently in image interpretation, terminology, and reporting of peripheral and soft-tissue vascular malformations.
Preoperative identification of FIGO 2023 stage I endometrial cancer (EC) is important for treatment planning. We aimed to develop and validate a deep-learning radiomics nomogram (DLRN) based on multiparametric MRI to distinguish stage I EC from stage II–IV disease. This study retrospectively enrolled patients from three centers. Patients from Centers 1 and 2 were randomly split into a training set (n = 616), and an internal test set (n = 158), and Center 3 served as an external test set (n = 232). The DLRN integrated deep-learning features, intratumoral and peritumoral radiomics features, and clinical characteristics. The DLRN was compared with clinical, radiomics, and deep-learning models. Discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), with AUCs compared using DeLong’s test. A total of 1006 patients (mean age, 56.5 ± 9.7 years) were analyzed. In the internal test set, the DLRN achieved an AUC of 0.852, higher than the clinical (0.673; p = 0.001), original radiomics (0.765; p = 0.030), and combined radiomics (0.793; p = 0.050) models. In the external test set, the DLRN remained robust (AUC = 0.829) and outperformed the clinical, original radiomics, and combined radiomics models (AUC = 0.680, 0.681, and 0.743; all p < 0.005). With DLRN assistance, radiologists showed improved diagnostic performance. The MRI-based DLRN showed robust discrimination between FIGO 2023 stage I and stage II–IV EC across test sets, supporting its potential for preoperative risk stratification and decision support. Question The introduction of the FIGO 2023 staging system has made the differentiation between stages more challenging, highlighting the need for better tools. Findings A new MRI-based deep-learning radiomics nomogram (DLRN) accurately identifies high-risk patients, significantly outperforming traditional and individual clinical prediction methods. Critical relevance statement This tool helps doctors make better preoperative treatment plans, potentially improving patient outcomes in endometrial cancer.
CT and MRI utilization is increasing across high-income countries, partly because of population ageing. This study integrated cross-country trends in examination rates, demographic change, and scanner productivity across OECD countries to project utilization to 2050 and inform sustainable service planning. CT and MRI examination rates were obtained from the OECD Data Explorer and Japan’s National Database of Health Insurance Claims Open Data. Population data were derived from the United Nations World Population Prospects 2024. Linear regression projected examination rates to 2050. Pearson coefficients assessed associations between imaging rates and population shares aged 0–14, 15–64, and ≥ 65 years. Scanner productivity was calculated as annual examinations per device. CT utilization was strongly associated with the population aged ≥ 65 years in all countries (r = 0.95–1.00). MRI showed similarly strong associations in most countries (r = 0.95–0.99), with Italy as the main exception. Linear extrapolation projected CT rates of approximately 722 examinations per 1000 population in Korea and 520 in the United States by 2050, while MRI rates reached approximately 327 in France and 308 in Germany. Population-level utilization did not consistently correspond to device-level productivity; CT examinations per scanner ranged from 2153.3 in Japan to 11,526.5 in Canada. Population ageing was associated with increasing CT and MRI utilization. Future demand may also depend on health system organization and scanner productivity. Sustainable planning should integrate demographic forecasting, appropriateness stewardship, operational optimization, workforce development, and technological innovation rather than rely solely on proportional capacity expansion. Question Ageing drives CT/MRI demand (r = 0.95–1.00 for CT); projected to continue to 2050, offering a valuable planning window. Findings High utilization ≠ high scanner productivity; operational organization shapes imaging output independently of demand volume. Critical relevance statement Sustainable planning requires demographic forecasting, appropriateness management, and technological innovation, not just capacity expansion.
Airway lesions are common CT findings in interval and post-screen lung cancers; however, airway nodules are frequently overlooked. Robust artificial intelligence (AI) tools for characterizing airway nodules remain limited. This study aimed to develop and evaluate deep learning-based models for the detection and classification of airway nodules on low-dose CT(LDCT) as a second-reading tool. 1813 segmental and more proximal airway nodules were retrospectively identified from 1107 LDCT of 1043 participants. A sphere-based center-points matching network, first used to establish a lung nodule detection model, was transferred to airway nodules and enhanced by incorporating coordinate and multiscale attention. The model was trained on 907 proximal airway nodules and independently tested on 738 proximal nodules across four hospitals. The clinical utility of the AI tool was evaluated through human-AI interaction. For nodule classification, a 3D SEResNeXt101-based model was trained, validated and tested using 286 airway nodules (121 benign, 165 malignant) with a 6:2:2 split and evaluated in an independent testing dataset. On the independent testing set, the detection model achieved a high sensitivity of 73.7
