Acta radiologica最新文献

Breast cancer screening (BCS) is a prevention strategy for breast cancer (BC) facilitating early detection and treatment. BCS has been shown to be effective in reducing BC mortality and minimizing the need for more extensive invasive treatments. Immigrant women's participation rate to BSC is lower than that of native women. Interventions to support their informed decision-making could make a difference to this. The aim of this review was to investigate the current evidence of interventions for supporting informed decision-making on attendance to BCS by immigrant women. An integrative review was conducted by searching online databases (Medline, PubMed, PsycInfo, CINAHL, Scopus). A quality appraisal of the articles was performed using Joanna Briggs Institute quality appraisal checklists and mixed method appraisal tool. Data were extracted and synthesized using narrative analysis. A total of 25 articles were included in the study. Interventional strategies for supporting informed decision-making on attendance to BCS by immigrants were home visits, personal navigation service support, education sessions, and media-led information. In summary, to ensure the effective adoption of interventions for immigrants, it is imperative to consider cultural and linguistic tailored interventions, involve family members, especially husbands, offer free BCS and navigation services to those with limited financial resources, and, most importantly, uphold women's autonomy in their decision to participate in BCS.

BackgroundAcute findings of patellar dislocation in magnetic resonance imaging (MRI) can diminish with time; therefore, additional findings may help diagnosis of chronic patellofemoral instability.PurposeTo define a previously undescribed MRI finding-medial parapatellar triangle (MPT)-and to examine the diagnostic value of the disruption of MPT in chronic patellofemoral instability.Material and MethodsOur study was performed on 24 knees that underwent medial patellofemoral ligament reconstruction for chronic patellofemoral instability and 48 knees of patients with similar age and sex as control group. MPT was defined as an acute-angled triangle delineated by patellar cartilage, medial retinaculum, and medial femoral condyle in the section passing through the upper third of the patella on axial proton-density weighted with fat saturation (PD fat-sat) MRI scans. Disruption of MPT was assessed by two authors blinded to the diagnosis. Inter- and intra-observer reliability was assessed using Cohen's κ test. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each observer and observation.ResultsIn the study group, the triangular structure was disrupted in 18 knees for the first observer's first observation, and 17 knees for the second observation; for the second observer, it was 15 knees and 17 knees, respectively. In the control group, the numbers were 2, 6, 1, and 3, respectively. Mean Cohen's κ statistic was 0.81 (range=0.73-0.87). Sensitivity, specificity, PPV, and NPV were 66.7%-75%, 87.5%-97.9%, 72.7%-94.4%, and 84%-88.5%. respectively.ConclusionDisruption of MPT is an additional finding that may help the diagnosis of chronic patellofemoral instability.

BackgroundThe application of quantitative magnetic resonance imaging (MRI) in skeletal muscle is crucial in rehabilitation medicine and competitive sports training.PurposeTo explore the feasibility of evaluating T2 value, proton density fat fraction (PDFF), and cross-sectional area (CSA) of the quadriceps femoris before and after countermovement jump (CMJ) based on T2 mapping and Fat Analysis and Calculation Technique (FACT).Material and MethodsA total of 32 healthy volunteers were recruited and underwent MRI examination of the thigh muscles, including axial T2 mapping and FACT sequence. The T2 value, PDFF, and CSA of the quadriceps femoris, adductor magnus, and gracilis were measured. The peak torque (PT) of the quadriceps femoris was measured using an isokinetic muscle strength system. The differences in MRI parameters before and after CMJ were compared, as well as the differences between sexes.ResultsThe T2 value and CSA of the quadriceps femoris and adductor magnus increased and PDFF decreased after CMJ (P <0.01). The PDFF of the gracilis was significantly higher than that of the vastus lateralis, and the vastus lateralis had a significantly higher PDFF than the other muscles (P <0.01). PT was highly correlated with the CSA of the quadriceps femoris (P <0.001, r = 0.906). CSA and PT of men were higher than those of women (P <0.001).ConclusionT2 mapping and FACT can quantitatively evaluate the differences of T2 value, PDFF, and CSA of different muscles before and after CMJ, which is an important evaluation method for competitive sports training and disease rehabilitation.

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.