Acta radiologica最新文献

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.

BackgroundAccurate preoperative assessment of endometrial cancer (EC) is crucial in young women who may be eligible for fertility-preserving therapy, which is generally limited to patients with grade 1, endometrioid-type tumors without myometrial invasion (MI).PurposeTo evaluate the utility of quantitative parameters derived from intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) for improving the diagnostic performance of magnetic resonance imaging (MRI).Material and MethodsThis retrospective study included 107 patients diagnosed with EC (mean age = 59 years; age range = 25-89 years) who underwent preoperative MRI, including multiple b-value (0-2000 s/mm²) diffusion-weighted imaging, between January 2022 and March 2024. Quantitative parameters were extracted from the mono-exponential (ADC), IVIM (Di, D*, f), and DKI (Dk, K) models and compared across clinical and pathological features.ResultsADC, Di, and Dk values were significantly higher in patients without MI (P = 0.015, 0.035, and 0.005, respectively). Di and Dk were significantly higher (P = 0.003 and 0.016), and K was significantly lower (P = 0.013) in the G1 group. Patients eligible for fertility preservation had significantly higher ADC, Di, and Dk values (P = 0.002, 0.002, and 0.001) and significantly lower K values (P = 0.044). The overall diagnostic performance of these parameters was moderate (area under the curve < 0.70).ConclusionIVIM and DKI-derived metrics may enhance preoperative assessment of tumor grade and MI, supporting decisions regarding fertility-preserving treatment.

BackgroundThe diagnostic performance of ultrasound (US) is heavily reliant on the operator's expertise. Advances in artificial intelligence (AI) have introduced deep learning (DL) tools that detect morphology beyond human perception, providing automated interpretations.PurposeTo evaluate Smart-Detect (S-Detect), a DL tool, for its potential to enhance diagnostic precision and standardize US assessments among radiologists with varying levels of experience.Material and MethodsThis prospective observational study was conducted between May and November 2024. US and S-Detect analyses were performed by a breast imaging fellow. Images were independently analyzed by five radiologists with varying experience in breast imaging (<1 year-15 years). Each radiologist assessed the images twice: without and with S-Detect. ROC analyses compared the diagnostic performance. True downgrades and upgrades were calculated to determine the biopsy reduction with AI assistance. Kappa statistics assessed radiologist agreement before and after incorporating S-Detect.ResultsThis study analyzed 230 breast masses from 216 patients. S-Detect demonstrated high specificity (92.7%), PPV (92.9%), NPV (87.9%), and accuracy (90.4%). It enhanced less experienced radiologists' performance, increasing the sensitivity (85% to 93.33%), specificity (54.5% to 73.64%), and accuracy (70.43% to 83.91%; P <0.001). AUC significantly increased for the less experienced radiologists (0.698 to 0.835 P <0.001), with no significant gains for the expert radiologist. It also reduced variability in assessment between radiologists with an increase in kappa agreement (0.459-0.696) and enabled significant downgrades, reducing unnecessary biopsies.ConclusionThe DL tool improves diagnostic accuracy, bridges the expertise gap, reduces reliance on invasive procedures, and enhances consistency in clinical decisions among radiologists.

BackgroundCone-beam computed tomography (CBCT) can offer advantages over multidetector CT in dose efficiency and economic costs, but musculoskeletal applications were limited in gantry-free systems.PurposeTo assess the utility of novel multi-scan-body CBCT for osseous imaging, compared to clinically implemented photon-counting-detector (PCCT) and energy-integrating-detector (EICT) CT.Material and MethodsAn anthropomorphic hand wrist phantom underwent gantry-based CBCT (low-dose, regular, enhanced, and best settings), PCCT, and EICT. Quantitative metrics included dose values, noise, and noise power spectrum (NPS). Three radiologists with varying experience levels (10, 6, and 1 years) assessed depiction of cortical and trabecular bone, articular surfaces, intraosseous ganglion cyst, and overall image quality using 5-point Likert scales.ResultsLow-dose and regular CBCT (0.37 and 0.67 mGy) showed the lowest dose values (CTDI_vol), followed by EICT, enhanced and best CBCT, and then PCCT (0.76, 1.08, and 1.61, and 3.56 mGy, respectively). Absolute noise was lowest for PCCT (15.1), followed by best (23.2), regular (25.1), and enhanced (27.4) CBCT. Highest noise was measured for low-dose CBCT (35.1) and EICT (30.1). CBCT showed overall irregular and relatively high NPS, compared to regular and high NPS of EID, whereas PCCT showed a cleaner texture with the lowest NPS. Qualitatively, CBCT (enhanced, best) generally achieved the best scores, while the other scans scored equally well. Average interreader agreement ranged from moderate to near-perfect (k = 0.53-0.87).ConclusionNovel multi-scan-body CBCT with variable image quality settings can provide detailed depiction of fine osseous structures, demonstrating comparable or lower doses compared to clinically implemented PCCT and EICT.

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.