Acta radiologica最新文献

Background: Breast cancer in pediatric patients is rare, but ultrasound (US) is widely utilized for symptomatic cases.

Purpose: To determine biopsy and cancer detection rates of pediatric patients and to assess if breast US can be omitted.

Material and methods: A retrospective review of a 5-year period was conducted of single-center breast US performed in patients aged <19 years. Data regarding presentation, clinical opinion (P1-5 score), and US (U1-5 score) were collected. If biopsy or surgery was performed, pathology was reviewed (B1-5 score).

Results: In total, 579 patients were included (19 boys, 560 girls; mean age=16.2±1.9 years; age range=0-18 years). Clinical examination was normal or benign (P1/P2) in all boys (100%) and 557/560 (99.5%) girls, and P3 in 3 (0.5%) girls. Of US, 52% demonstrated normal findings (U1) for both sexes (300/579); in the remaining cases, the most frequent findings were gynecomastia in 12/19 boys and well-defined breast masses in 208/560 girls. Of the 560 girls, 6 (1%) underwent US-guided biopsy, with final histology of fibroadenoma (B2) in all cases, while 27 (5%) had a surgical excision, with final histology of fibroadenoma (22/27, 81.5%), hamartoma (2/27, 7.4%), benign phyllodes tumor (2/27, 7.4%), and angiomyxoma skin lesion (1/27, 3.7%). No malignant lesions were diagnosed at the time of clinical referral or during the 18-month follow-up in patients with a well-defined mass on US.

Conclusion: Breast malignancy is extremely rare in pediatric population. US can be safely omitted if clinical examination is normal; this approach would have avoided breast US in 52% of patients in this study.

Background: Myocardial fibrosis is often detected in patients with hypertrophic cardiomyopathy (HCM), which causes left ventricular (LV) dysfunction and tachyarrhythmias.

Purpose: To evaluate the potential value of a machine learning (ML) approach that uses radiomic features from late gadolinium enhancement (LGE) and cine images for the prediction of ventricular tachyarrhythmia (VT) in patients with HCM.

Material and methods: Hyperenhancing areas of LV myocardium on LGE images were manually segmented, and the segmentation was propagated to corresponding areas on cine images. Radiomic features were extracted using the PyRadiomics library. The least absolute shrinkage and selection operator (LASSO) method was employed for radiomic feature selection. Our model development employed the TabPFN algorithm, an adapted Prior-Data Fitted Network design. Model performance was evaluated graphically and numerically over five-repeat fivefold cross-validation. SHapley Additive exPlanations (SHAP) were employed to determine the relative importance of selected radiomic features.

Results: Our cohort consisted of 60 patients with HCM (73.3% male; median age = 51.5 years), among whom 17 had documented VT during the follow-up. A total of 1612 radiomic features were extracted for each patient. The LASSO algorithm led to a final selection of 18 radiomic features. The model achieved a mean area under the receiver operating characteristic curve of 0.877, demonstrating good discrimination, and a mean Brier score of 0.119, demonstrating good calibration.

Conclusion: Radiomics-based ML models are promising for predicting VT in patients with HCM during the follow-up period. Developing predictive models as clinically useful decision-making tools may significantly improve risk assessment and prognosis.

Background: Sigmoid sinus wall reconstruction (SSWR) is an effective treatment for pulsatile tinnitus (PT). However, follow-up postoperative imaging manifestations have not been extensively reported.

Purpose: To evaluate the morphological changes in patients with PT after successful SSWR using ultra-high-resolution computed tomography (U-HRCT).

Material and methods: Data were retrospectively analyzed from 10 patients with PT who underwent successful SSWR primarily with autologous bone powder. U-HRCT scans were performed within 3 days of surgery and repeated 6 months later. The integrity, relative density, extent, and shape of the repaired wall were analyzed. The chi-square test was used to compare the categorical variables and the Phi (φ) coefficient was used to represent the magnitude of the correlation.

Results: Among the 10 patients with PT, 1 (10%) achieved complete coverage of the defect with the residual bone, 8 (80%) had partial coverage, and 1 (10%) showed complete separation. A gap between the repaired wall and residual bone in the initial U-HRCT was linked to incomplete defect coverage in the subsequent U-HRCT scan (P < 0.001, φ = 0.903). The repaired wall shrank from the periphery to the center and the density increased. The repaired wall compressed into the sigmoid sinus retracts over time, reshaping into a naturally curved sigmoid sinus sulcus.

Conclusion: Morphological remodeling is a typical characteristic of the repaired sigmoid sinus wall in patients with PT. Short-term incomplete repair may imply incomplete coverage of the defect in the future, but this is not correlated with recurrence.

Background: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstruction (DLIR) effectively reduces noise without sacrificing image quality.

Purpose: To evaluate whether DLIR on low-keV VMIs of dual-energy CT scans improves the visualization of the AKA.

Material and methods: We enrolled 29 patients who underwent CT angiography before aortic repair. VMIs obtained at 70 and 40 keV were reconstructed using hybrid iterative reconstruction (HIR), and 40 keV VMIs were reconstructed using DLIR. The image noise of the spinal cord, the maximum CT values of the anterior spinal artery (ASA), and the contrast-to-noise ratio (CNR) of the ASA were compared. The overall image quality and the delineation of the AKA were evaluated on a 4-point score (1 = poor, 4 = excellent).

Results: The mean image noise of the spinal cord was significantly lower on 40-keV DLIR than on 40-keV HIR scans; they were significantly higher than on 70-keV HIR images. The CNR of the ASA was highest on the 40-keV DLIR images among the three reconstruction images. The mean image quality scores for 40-keV DLIR and 70-keV HIR scans were comparable, and higher than of 40-keV HIR images. The mean delineation scores for 40-keV HIR and 40-keV DLIR scans were significantly higher than for 70-keV HIR images.

Conclusion: Visualization of the AKA was significantly better on low-keV VMIs subjected to DLIR than conventional HIR images.

Background: Double-concentration magnetic resonance imaging (MRI) contrast agents are frequently used in contrast-enhanced MR angiography (CE-MRA) of the head and neck. To avoid mistiming the peak concentration of intraluminal contrast (due to shorter duration of peak), a second acquisition is sometimes performed.

Purpose: To evaluate additional information from the second acquisition of CE-MRA and compare the collateral scoring to the hypoperfusion index obtained on MR perfusion, and to investigate presence of pseudo-occlusion using the second phase of CE-MRA.

Material and methods: A retrospective study was conducted. CE-MRA of the brain/neck, dynamic susceptibility contrast (DSC) MR perfusion scan (in majority) and subsequent digital subtraction angiography (DSA) were evaluated in patients with previous acute internal carotid artery (ICA)/middle cerebral artery (MCA) occlusion. Evaluation of CE-MRA/MR perfusion and DSA was performed by three experienced neuroradiologists and one neurointerventionist, respectively.

Results: The site of ICA occlusion was seen to be distal to the site noted on early arterial phase (pseudo-occlusion of ICA) in 28.5% of patients. A significant negative correlation was seen between a higher HIR and collateral score.

Conclusion: Evaluation of second phase CE-MRA can provide valuable information that may be otherwise lost if only the early arterial phase is evaluated.

Background: Shoulder ultrasonography (US) is commonly employed to differentiate the causes of shoulder pain. However, the correlation between ultrasonographic findings and the individual's functional disability and daily activities remains unexplored.

Purpose: To investigate the relationship between shoulder US findings and physical function, disability, and pain.

Material and methods: The study investigated patients with shoulder pain. Tendons of supraspinatus, infraspinatus, subscapularis, and long head of biceps brachii, acromiohumeral distance, and acromioclavicular joint (ACJ) degeneration were evaluated using US. The QuickDASH questionnaire was employed to evaluate physical function, symptoms, and disability, while the visual analog scale (VAS) was utilized to assess pain levels.

Results: The study included 84 patients (69 [82.1%] women, mean age = 55.7 ± 11.9 years). Ultrasonographic examination revealed ACJ degeneration in 61 (21.4%) patients and biceps tendinitis in 21 (25%) patients. In addition, supraspinatus tendinosis was identified in 51 (61%) patients, while 28 (33%) exhibited a supraspinatus tear. The patients demonstrated a mean VAS score of 7.03 ± 1.49 and mean QuickDASH score of 45.6 ± 15.3. Individuals with a supraspinatus tear exhibited higher VAS (P = 0.008) and QuickDASH (P = 0.016) scores. Patients presenting with ACJ degeneration demonstrated increased VAS scores (P = 0.014), whereas those with biceps tendinopathy showed higher QuickDASH scores (P = 0.035).

Conclusion: The results obtained from our research demonstrate significant correlations between ultrasonographic data and pain, disability, and physical manifestations. The findings of this study indicate that shoulder US provides valuable information regarding the difficulties individuals face in their daily tasks and the degree of pain they endure.

Background: Flow-related signal void artifacts can be visualized on the T2-weighted (T2W) three-dimensional sampling perfection with application-optimized contrast (3D-SPACE) sequence. Flow void artifacts in the cerebral aqueduct and the fourth ventricle can provide information about cerebrospinal fluid (CSF) flow dynamics.

Purpose: In this study, we aimed to test the performance of the T2W 3D-SPACE sequence in assessing the CSF flow in the aqueduct and/or fourth ventricle.

Material and methods: A total of 137 patients (age range = 3-89 years) who underwent CSF flow study were included. The amount of signal loss on T2W 3D-SPACE due to flow in the aqueduct and fourth ventricle was assessed and graded using a 4-point scale of 0 (absence of flow void) to 3 (signal void filling the aqueduct and entire fourth ventricle). A correlation was then sought between the quantitative values obtained by phase-contrast magnetic resonance imaging (PC-MRI) and the amount of signal void in the 3D-SPACE sequence.

Results: At the aqueduct level, there was a statistically significant difference in the forward flow velocity and the flow volume among different grades (all P < 0.001). In the grade 3 group, CSF peak systolic flow velocity and mean flow volume were found to be significantly higher than in the other grades (P < 0.001). The mean aqueduct area in the grade 0 group was found to be significantly different from that in the other classes (P < 0.001).

Conclusion: The amount of signal loss in the fourth ventricle observed on T2W 3D-SPACE is correlated with the peak systolic velocity and flow volume measured quantitatively in PC-MRI.

Background: Rapid and accurate measurement of computed tomography (CT) image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) is a clinical challenge.

Purpose: To explore the feasibility of intelligent measurement of chest CT image noise, SNR, and CNR.

Material and methods: A total of 300 chest CT scans were included in the study, which was divided into research dataset, internal test dataset, and external test dataset. Based on the research dataset, automatically segment and measure the average CT values and standard deviation (SD) of CT values for background air and lung field under different thresholds to obtain noise, SNR, and CNR results. Using the results of manual measurements as the reference standard, we determine the optimal threshold with the highest consistency. Using internal and external test datasets, validate the consistency of automated measurements of noise, SNR, and CNR at the optimal CT threshold with reference standards.

Results: With background air set at -900 HU and lung field at -800 HU as thresholds, the automated measurements of noise, SNR, and CNR demonstrate the highest consistency with the reference standards. At the optimal threshold, the noise, SNR, and CNR measured automatically on both the internal (intraclass correlation coefficient [ICC] = 0.85-0.96) and external (ICC = 0.75-0.85) test datasets exhibit high consistency with their respective reference standards.

Conclusion: The method we explored can intelligently measure the noise, SNR, and CNR of chest CT images, exhibits high consistency with radiologists, and offers a novel tool for image quality evaluation and analysis.

Background: Acute pulmonary embolism (PE) is a disease with a serious prognosis and a high probability of death in the emergency department.

Purpose: To investigate the prediction of PE-related mortality and intensive care admission (ICU) of Qanadli (Qscore), Bova, and simplified Pulmonary Embolism Severity Index (sPESI) scores.

Material and methods: This retrospective observational study consisted of all patients diagnosed with acute PE who were imaged under computed tomography pulmonary angiography (CTPA) for a total of 5 years between 1 June 2018 and 1 June 2023. The prediction of radiological and clinical scores for mortality and ICU admission was examined.

Results: A total of 95 patients were analyzed. Patients who died and those who were admitted to the ICU had a significantly higher frequency of being found to have a high-risk (≥1) sPESI score (P = 0.04 and P = 0.016, respectively). For mortality, the sPESI score was found to be significant; the sensitivity and specificity were observed as 54% and 66% (area under the curve [AUC]=0.670, 95% confidence interval [CI]=0.527-0.814; P = 0.020). For ICU admission, the sensitivity and specificity of the Qscore, sPESI, and Bova scores were 35%, 77%, and 58%, and 78%, 65%, and 84% respectively (AUC=0.626, 95% CI=0.511-0.740, P = 0.031; AUC=0.769, 95% CI=0.674-0.865, P < 0.001; and AUC=0.767, 95% CI=0.671-0.862, P < 0.001, respectively).

Conclusion: It was found that the sPESI score was effective at predicting mortality in patients with acute PE. Qscore, sPESI, and Bova scores have been shown to be useful in predicting ICU admission.