Acta radiologica最新文献

BackgroundIschemic cardiovascular diseases are leading global causes of death, largely driven by atherosclerosis.PurposeTo develop a simplified approach to enhance the predictive accuracy of the revised Framingham Stroke Risk Profile (rFSRP) by integrating ultrasound-derived plaque characteristics.Material and MethodsThe study population consisted of 1782 asymptomatic patients with carotid plaques, prospectively enrolled from three hospitals. The patients were stratified into high-risk and low-risk groups using both the conventional rFSRP and a novel approach incorporating ultrasonic plaque features. Kaplan-Meier survival analysis and log-rank tests were utilized to evaluate stroke-free survival rates.ResultsOver a mean follow-up of 37 ± 15 months, 420 (23.5%) patients experienced strokes. Both univariate and multivariate analyses demonstrated a significant association between strokes and various parameters: an rFSRP score ≥10, plaque length ≥10 mm, plaque thickness ≥2 mm, and the presence of type 1 and type 2 plaque according to the Geroulakos classification. A notable disparity in stroke-free survival rate was observed between high-risk and low-risk groups when classified using the combined criteria of rFSRP and ultrasonic features (P <0.001). The net reclassification improvement formula, accounting for reclassification accuracy, indicated that 11.2% of patients were more precisely classified under the combined criteria. In addition, patients initially deemed low-risk based solely on rFSRP, when reclassified as high-risk per the combined criteria, showed a substantial difference in stroke-free survival rate from those remaining in the low-risk category (P <0.001).ConclusionIntegrating ultrasound-derived plaque characteristics with rFSRP improves stroke risk prediction, offering a more effective clinical tool for asymptomatic carotid atherosclerosis.

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disease marked by a variety of repetitive behaviors and social communication difficulties.PurposeTo develop a generalizable machine learning (ML) classifier that can accurately and effectively predict ASD in children.Material and MethodsThis paper makes use of neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE I and II) datasets through a combination of structural and functional magnetic resonance imaging data. Several ML models, such as Support Vector Machines (SVM), CatBoost, random forest (RF), and stack classifiers, were tested to demonstrate which model performs the best in ASD classification when used alongside a deep convolutional neural network.ResultsResults showed that stack classifier performed the best among the models, with the highest accuracy of 81.68%, sensitivity of 85.08%, and specificity of 79.13% for ABIDE I, and 81.34%, 83.61%, and 82.21% for ABIDE II, showing its superior ability to identify complex patterns in neuroimaging data. SVM performed poorly across all metrics, showing its limitations in dealing with high-dimensional neuroimaging data.ConclusionThe results show that the application of ML models, especially ensemble approaches like stack classifier, holds significant promise in improving the accuracy with which ASD is detected using neuroimaging and thus shows their potential for use in clinical applications and early intervention strategies.

BackgroundAmyloid deposition manifests as thickening and calcification of the joints on computed tomography (CT) images.PurposeTo investigate the diagnostic potential of thickening and calcification of the shoulder and hip joints for the detection of transthyretin amyloid cardiomyopathy (ATTR-CM).Material and MethodsWe included 19 patients who had been assessed using ^99mTc-pyrophosphate scintigraphy between January 2019 and December 2022 and diagnosed with ATTR-CM. The incidence of calcification and synovial thickening in the hip and shoulder joints of the patients and controls was evaluated. Two radiologists determined differences in joint calcification and thickness on CT images using Pearson chi-square tests and unpaired t-tests, respectively.ResultsShoulder and hip joint thickness (both P < 0.01) and calcifications (P < 0.05) significantly differed between the groups. The area under the receiver operating characteristic curve (AUC) was 0.74 for the shoulder joint, and the cut-off Youden index was 16.1 mm, with a sensitivity and specificity of 63.2% and 78.9%, respectively. The AUC was 0.844 for the hip joint, with an optimal cutoff of 11.8 mm, with a sensitivity and specificity of 71.4% and 89.5%, respectively. Inter-observer agreement was substantial between the radiologists for detecting hip and/or shoulder joint calcification (κ = 0.712). The interclass correlation coefficients (2, 1) were 0.65 and 0.71 for measurements of shoulder and hip joint thickness, respectively.ConclusionThickened and calcified shoulder and hip joints are more likely to be found in patients with clinically diagnosed ATTR-CM than those without.

BackgroundThe integration of artificial intelligence (AI) in radiology has the potential to improve diagnostic accuracy and efficiency. Medical students and junior doctors will likely use AI more frequently in the future, making their perceptions essential for identifying educational gaps.PurposeTo explore the perceptions of UK medical students and junior doctors regarding AI in radiology.Material and MethodsA cross-sectional survey was distributed across UK medical schools and foundation programs. A total of 250 responses were analyzed using descriptive statistics and non-parametric tests, focusing on career impact, clinical effectiveness, educational development, and ethical concerns.ResultsMost respondents (55.2%) were undeterred by career uncertainties related to AI, with 64% confident that AI would not replace radiologists. Up to 80.6% supported AI's clinical benefits, and 63.2% endorsed its educational integration. However, there were concerns about job displacement and insufficient AI training. Medical students were more worried about job security than junior doctors, while those committed to radiology were less apprehensive and viewed AI as complementary.ConclusionEducational programs and regulatory frameworks are essential to facilitate AI integration in radiology. Addressing concerns about job displacement and improving AI education will be key to preparing future radiologists for technological advancements.

BackgroundQuantitative analysis with habitat clustering represents an innovative, non-invasive approach to quantify tumor heterogeneity.PurposeTo characterize intratumoral spatial heterogeneity using dual-energy computed tomography (DECT) in breast cancer patients and investigate the performance of habitat imaging in predicting axillary lymph node (ALN) metastasis compared with radiomics.Material and MethodsA total of 135 patients were randomly assigned to a training group (n = 95) and a testing group (n = 40). An additional 50 patients served as the validation group. Four intratumoral subregions with different wash-in and wash-out enhancement modes were identified through cluster analysis of arterial and venous phase iodine concentration maps. The percentage of each subregion was quantified to construct habitat imaging. Radiomics features were extracted from iodine concentration maps, and Boruta was used for feature selection. Habitat imaging and radiomics model performance was compared by net reclassification improvement (NRI) and integrated discrimination improvement (IDI).ResultsHabitat imaging demonstrated areas under the receiver operating characteristic curve (AUCs) of 0.82, 0.80, and 0.78 in the training, testing, and validation groups, respectively. In addition, the AUCs of the radiomics models were 0.78, 0.70, and 0.65 in the training, testing, and validation groups, respectively. NRI and IDI demonstrated that habitat imaging was statistically superior to the radiomics model (P < 0.05).ConclusionsHabitat imaging based on intratumoral spatial heterogeneity can predict ALN metastasis in breast cancer and was superior to radiomics.

BackgroundRapid real-time magnetic resonance (MR) sequences enable dynamic articular kinematic assessment. The abduction-external rotation (ABER) position has long been used to characterize glenohumeral pathology.PurposeTo evaluate a dynamic gradient recall echo (GRE) sequence for ABER-positioned glenohumeral joint kinematic assessment correlating with subjective instability and clinical apprehension testing.Material and MethodsSymptomatic patients were scanned using a routine MR arthrogram protocol supplemented by an additional "dynamic ABER" GRE technique acquired with the arm abducted and then internally-externally rotated in real time. Dynamic motion of the humeral head between the extremes of motion in the abducted and externally rotated positions was evaluated. The cohort was followed for 3 years.ResultsA total of 15 dynamic ABER studies in 15 different patients were evaluated by three readers (right: n=9; left: n=6), with a mean age of 30 years (range=19-45 years). Good accuracy of the humeral head excursion between the abducted and externally-internally rotated positions (AUC=0.88) was observed as a test for positively detecting instability. An association was detected between clinical instability and mean humeral head excursion as measured by all three readers (P = 0.026), although no association between positive apprehension testing and mean humeral head excursion was detected. There was a trend towards surgery-naïve patients with higher mean humeral head excursion subsequently undergoing surgical management (P=0.088), although this did not reach statistical significance.ConclusionCorrelation between subjective instability and humeral head translation demonstrated on a dynamic ABER sequence added to MR shoulder arthrograms was observed but without association with clinical apprehension testing.

BackgroundMyxoid liposarcoma (MLS) is a subtype of liposarcoma characterized by its myxoid stroma and adipocyte differentiation. MLS is prone to recurrence and metastasis. Magnetic resonance imaging (MRI) plays a crucial role in evaluating tumor characteristics, enabling accurate diagnosis, and predicting patient prognosis.PurposeTo analyze the components of MLS by MRI features and assess their correlation with prognosis.Material and MethodsA total of 20 patients with MLS who underwent MRI were retrospectively included. Tumor components were analyzed by MRI features, and their prognostic correlation was assessed. Patients were divided into good and poor prognosis groups based on postoperative follow-up.ResultsThe proportions of non-fatty/non-myxoid components in the good and poor prognosis groups were 15.00% (range = 10.00%-20.00%) and 70.00% (range = 52.50%-77.50%), respectively (P < 0.001). The proportion of myxoid composition also differed significantly between the two groups (75.00%, [range = 65.00%-85.00%] vs. 25.00% [range = 17.50%-42.50%]; P < 0.001). The good prognosis group had a greater mean apparent diffusion coefficient (ADC) value (1.66 ± 0.23 × 10^-3 mm²/s) and a lower mean ADC low signal ratio (5.00% [range = 0%-10.00%]) in the non-fatty/non-myxoid areas than the poor group (1.21 ± 0.41 × 10^-3 mm²/s; 20.00% [range = 11.00%-39.00%]; P = 0.006 and P = 0.001). The differences in the percentages of patients with a component ratio <25% and >50% in both the non-fatty/non-myxoid and myxoid groups were significant (P < 0.001 and P = 0.005).ConclusionImaging features were closely associated with the histological components of MLS. The use of MRI features for assessing MLS components has important implications for prognostic prediction.

BackgroundDynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) protocol was included into the Ovarian-Adnexal Reporting & Data System (O-RADS) MRI scoring system. To avoid the administration of contrast medium, the non-contrast MRI scoring (NCMS) system was proposed.PurposeTo evaluate the contribution of detecting intra-tumoral hemorrhage in the solid tissue of adnexal masses to improve tumor characterization and enhance the risk stratification of adnexal lesions using the NCMS system.Material and MethodsMRI findings including susceptibility-weighted sequences (T2*-weighted MR angiography [SWAN]) were retrospectively analyzed in 126 surgically confirmed adnexal tumors with solid tissue components (20 benign, 106 malignant). Solid tissue was classified as malignant based on the NCMS criteria, defined by intermediate intensity on T2-weighted (T2W) imaging, and corresponding diffusion restriction. Hemorrhage was assessed based on high intensity on T1-weighted (T1W) imaging and susceptibility-related signal voids on SWAN.ResultsThe NCMS solid tissue criteria identified malignancy with a sensitivity of 94.3%, specificity of 60%, and accuracy of 88.9%. High intensity on T1W imaging and signal voids on SWAN were observed in 23.6% and 72.6% of malignant lesions, compared to 0% and 5% in benign lesions, respectively. Hemorrhage was frequently observed in high-grade malignant tumors, or hemorrhagic subtypes. The combination of NCMS criteria and/or presence of intra-tumoral hemorrhage was associated with malignancy, yielding a sensitivity of 98.1%, specificity of 60%, and accuracy of 92.1%.ConclusionThe inclusion of intra-tumoral hemorrhage enhances the diagnostic accuracy of the NCMS for characterizing adnexal lesions. SWAN may also aid in estimating tumor grade and identifying hemorrhagic subtypes.

BackgroundOsteosarcoma (OS) is the most common primary malignant bone tumor. Exploring quantitative parameters that reflect the outcome of neoadjuvant chemotherapy (NACT) in patients with OS can help advance the treatment of patients.PurposeTo explore the role of T2-weighted (T2W) magnetic resonance imaging (MRI) radiogenomic features in characterizing changes in patients with OS and on NACT.Material and MethodsA total of 21 patients with OS were examined retrospectively and divided into a poor-response group (n = 13) and a good-response group (n = 8). A total of 98 radiomic features and 31 gene expression profiles were analyzed for each patient. Age, sex, alkaline phosphatase, pathologic type, tumor size, and tumor location were also analyzed. Comparisons between the good- and poor-response groups were made using the t-test, Mann-Whitney U test, or Fisher's exact test. The relationships between radiomic features and gene expression profiles were conducted using Spearman's correlative analyses.ResultsStatistical differences in 19 radiomics features and glutathione-s-transferase 1 were found between the good- and poor-response groups (P < 0.05). The receiver operating characteristic curve showed that four NGTDM busyness features had the best performance in predicting the NACT of patients with OS, with an area under the curve of 0.788, sensitivity of 0.750, and specificity of 0.923. Correlation analysis showed that the HLA_I, CD274, GSTP1, and CCND3 were significantly correlated with one or more radiomics features (P < 0.05).ConclusionThe T2W MRI radiogenomic features can be used as biomarkers for the early response evaluation of NACT in OS. This is the first study to analyze the association of T2 radiogenomic features with NACT in patients with OS to assist in the assessment of NACT.

BackgroundMeniscal damage compromises its capacity to resist load transmission. However, little is known about the effects of different meniscal injuries on meniscal extrusion under femorotibial loading conditions.PurposeTo evaluate meniscal extrusion in the medial compartment using ultrasound (US), with and without axial loading, and correlate to individual factors (age, body mass index [BMI], osteoarthritis [OA], and type of meniscal tear with different degrees of extrusion).Material and MethodsThe study involved 104 volunteers (53 men, 51 women; mean age = 41.5 ± 1.8 years; age range = 18-70 years; mean BMI = 28.7 ± 5.8 kg/m²; range = 20-47 kg/m²). Meniscal extrusion was evaluated using US in the supine and standing positions, and tears were confirmed by magnetic resonance imaging (MRI).ResultsOur study shows significant variation in meniscus extrusion between supine and standing positions (P = 0.0002). In the supine position, mean values of medial meniscal extrusion within the meniscal tear group (2.281 ± 2.03 mm) were higher than the group without tears (0.55 ± 0.68 mm) (P < 0.0001). From a total of 104 knees studied, 57 (54.8%) demonstrated meniscal injuries. All menisci with ≥3 mm of extrusion presented tears confirmed on MRI. Painful medial compartment showed higher extrusion values (P < 0.0001). OA and age had a greater impact on extrusion (P = 0.001).ConclusionThe presence of extrusion ≥3 mm predicts meniscal tear. In addition, OA and age have a greater impact on increasing extrusion. This research provides valuable insights into the effects of axial body load and associated factors on meniscal extrusion.