Iranian Journal of Radiology
Volume:20 Issue: 4, Oct 2023

Tomosynthesis, despite its potential to produce superior images compared to mammography for Breast Imaging- Reporting and Data System (BI-RADS) scoring, has not yet been widely adopted. This is likely due to a combination of insufficient data and a scarcity of expertise in this area.

In this study, we aimed to evaluate the potential benefits of tomosynthesis for patients with indeterminate BI-RADS results from full-field digital mammography (FFDM). Specifically, we sought to determine whether this method could clarify ambiguous BI-RADS scores.

This cross-sectional study involved 386 female patients, with an average age of 44.7 ± 7.9 years. The study included all patients who were admitted to a referral center for breast radiology during 2019 - 2020 and underwent both FFDM and tomosynthesis within a short interval. The purpose of performing tomosynthesis was to assess its additional benefits over FFDM. The collected data was input into SPSS version 20 for analysis. A P-value of less than 0.05 was deemed statistically significant.

Out of 152 patients who initially had indeterminate findings in mammography (BI-RADS score of 0), only one patient (0.7%) remained indeterminate after tomosynthesis. This implies that tomosynthesis was able to clarify the BI-RADS scores for 99.3% of patients with previously indeterminate scores. Specifically, out of these 151 patients, 81 were scored as BI-RADS 2, 45 as BIRADS 3, 19 as BI-RADS 4, and six as BI-RADS 5.

Tomosynthesis has the capability to clarify unclear FFDM BI-RADS scores in over 99% of cases. This suggests that it could serve as the primary supplementary imaging technique for cases with indeterminate BI-RADS scores.

During computed tomography (CT) examinations, the presence of radiation material undoubtedly has a certain impact on patients, particularly children, who are more susceptible to radiation. Consequently, finding ways to minimize radiation dosage and mitigate radiation-related risks while enhancing the quality and accuracy of CT scans has become a crucial concern for medical professionals in CT equipment manufacturing companies and radiology departments to contemplate.

This study aimed to explore the feasibility of low dose and low contrast medium combined with low flow rate scanning in CT angiography (CTA) of children’s liver.

A total of 59 children who visited our department for liver vascular CT scans between April 2021 and December 2022 were prospectively selected and randomly divided into 2 groups: the experimental group and the control group. The experimental group consisted of 28 children who received an injection of 80 kV, automatic tube current, low contrast dose (1.5 mL/kg), and low flow rate of 20 s. The control group included 31 children who were injected with 100 kV, automatic tube current, contrast agent dose (2 mL/kg), and flow rate of 16 s. The objective and subjective image quality, radiation dose, and iodine intake in the arterial, portal, and venous phases were compared between the 2 groups.

During the arterial, portal, and venous phases, the experimental group exhibited lower signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) compared to the control group (P < 0.05). However, there was no significant difference in subjective quality scores between the 2 groups (P> 0.05). The effective dose (ED),volumeCT dose index (CTDIvol), anddose-length product (DLP) of the experimental group were lower than those of the control group (P< 0.05). Additionally, the iodine intake in the experimental group was lower than that in the control group (P < 0.05).

In the examination of hepatic vascular CTA in children, the use of a 3-low scanning technique can effectively decrease radiation and contrast agents while ensuring sufficient image quality for accurate clinical diagnosis.

We report the first case demonstrating the viability of the trans-cell approach as a salvage strategy in situations where Flow Redirection Endoluminal Devices (FREDs) fail to open.

A 64-year-old woman was admitted to our facility after a motorcycle accident. During her examination, an arteriovenous malformation (AVM) in the right temporal lobe was incidentally discovered. The AVM was associated with large flow-related aneurysms in the bilateral supraclinoid internal carotid artery (ICA), as revealed by computed tomography and magnetic resonance imaging. The AVM was surgically removed, and the aneurysm was treated endovascularly using a FRED. However, the middle section of the FRED failed to open post-deployment. We successfully carried out a salvage therapy using the stent strut technique with a Headway Duo microcatheter for coiling.

The trans-cell technique utilizing aHeadwayDuo156 microcatheter can beaneffective salvage treatment forunopened FRED stents used in brain aneurysm cases.

Assessing the measurable chest computed tomography (CT) scan cardiac parameters can help us evaluate cardiovascular involvement and risk stratification in patients with coronavirus disease 2019 (COVID-19).

This study investigated the measurable chest cardiac CT indices and their association with predicting outcomes in patients with COVID-19.

Patients with confirmed COVID-19 were included in the study from March up to June 2020 and underwent chest CT scans on admission. The following parameters were analyzed: The extent and pattern of lung involvement, cardiothoracic ratio (CTR), pulmonary artery to aorta ratio (PA/A), pericardial effusion, pleural effusion, and inferior vena cava (IVC) diameter through low-dose CT scan. The association between cardiac indices and patients and the extent of lung involvement outcome was evaluated by logistic regression, Cox regression, and linear regression, respectively.

A total of 176 COVID-19 patients (51.1% men and 48.9% women) were enrolled in the study. Of them, 55 patients (31.25%) died, and 121 patients were discharged (68.75%). Themeanage of participants was 64.9 ± 15.09 years. CTR > 0.49 wasmorefrequentamong deceased patients (50.9% vs. 31.4%, P = 0.018) and was associated with non-significant increases in odds ratio (OR) and hazard ratio (HR) (OR = 1.75, P = 0.11; HR= 1.43, P = 0.19). Moreover, PA/A> 1 was morecommonin deaths (52.7% vs. 32.2%; P = 0.012) and significantly increased OR but not HR (OR = 1.99; P = 0.04; HR = 1.36; P = 0.26). The moderate and severe pleural effusion was noticeably associated with prolonged hospitalization (HR = 4.09, P = 0.01; HR = 3.37, P = 0.02, respectively). The IVC diameter and pericardial effusion were not significantly correlated with outcomes.

Cardiac indices in chest CT of COVID-19 patients can be accounted for the prediction of patient outcomes in the clinic, such as PA/A > 1 ratio, which increases the likelihood of in-hospital deaths.

COVID-19 has emerged as the most significant health crisis in recent years, leading to over 6 million deaths globally due to the disease.

Given the prevalence of multisystem inflammatory syndrome in children (MISC) following the COVID-19 pandemic, this study aims to examine the imaging findings and prognoses of clinical and subclinical myocarditis in children with MISC through cardiac magnetic resonance (CMR) imaging.

This prospective cohort study carried out over eighteen months from May 2021 to November 2022, included 14 patients who underwent CMR imaging. A census of all eligible patients during the study period served as the sampling method. Inclusion criteria were patients with confirmed COVID-19 infection through serological tests, polymerase chain reaction (PCR), or recent exposure to COVID-19 patients. Exclusion criteria included patients with a history of congenital heart disease (CHD) or pulmonary disease. Additional diagnostic tests performed included blood sample tests, chest X-ray (CXR), electrocardiogram (ECG), and echocardiography. CMRimaging was conducted on patients with cardiac involvement. A diagnosis of myocardial inflammation was made if a patient met at least two of the Lake Louise Criteria. The Chi-square, Fisher’s exact, and Mann-Whitney tests were used to examine the relationship between quantitative variables and treatment outcomes. Additionally, the Wilcoxon signed rank, and McNemar’s tests assessed changes in echocardiography findings from admission to follow-up. A significance level of 0.05 was set.

Among the 14 patients studied, 8 (57.10%) were girls and 6 (42.90%) were boys. The average age was 6.03 ± 3.71 years. The median time toCMR imaging after symptom onset was 4 weeks (interquartile range (IQR): 2 - 12, range: 30). Global function assessment using left ventricular ejection fraction (LVEF) showed that 5 (35.70%), 3 (21.40%), and 6 (42.90%) patients had normal function, mild dysfunction, and significant LV dysfunction, respectively. 71.40% of patients who recovered had mild tricuspid regurgitation (TR) and no cardiomegaly. Significant differences in mean values of polymorphonuclear neutrophil (PMN) (37.71 ± 11.75 vs. 81.44 ± 13.06), lymphocytes (48.71 ± 20.08 vs. 12.51 ± 7.26), hemoglobin (Hb) (12.60 ± 1.55 vs. 10.10 ± 1.62), mean corpuscular volume (MCV) (85.90 ± 5.67 vs. 79.37 ± 5.23), erythrocyte sedimentation rate (ESR) (8.86 ± 13.60 vs. 30.29 ± 21.33), and C-reactive protein (CRP) (18.91 ± 27.25 vs. 100.57 ± 85.67) were observed between non-recovered and recovered patients, respectively (P < 0.05). However, no statistically significant association was found between other variables, including N-terminal pro–B-type natriuretic peptide (NT-proBNP), D-dimer, and Troponin I (TPI), with treatment outcomes (P > 0.05).

Our findings indicate that a negative COVID-19 test does not exclude an established clinical COVID-19 infection in children with MISC. The results suggest that all children with MISC and a history of COVID-19 infection should undergo assessment for myocardial fibrosis, regardless of ejection fraction (EF) as determined by Echocardiography, laboratory tests, and COVID-19 test results. Strain analysis, conducted during both the acute phase and subsequent follow-ups through CMR imaging or Echocardiography, is recommended to enhance the understanding of the prognosis.

 The widespread use of high-resolution computed tomography (HRCT) in lung cancer screening has allowed for an increased detection rate of ground-glass nodules (GGNs) in the lung. Hence, obtaining the correct clinical diagnosis of benign and malignant GGNs has become crucial.

 Most artificial intelligence and computer-aided diagnosis (AI-CAD) systems for the classification of pulmonary GGNs fail to extract CT features. This study used HRCT and AI to analyze the CT features of GGNs to improve the prediction of benign and malignant pulmonary GGNs.

 This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. A total of 204 patients with GGNs were recruited and divided into 2 groups according to their pathological results. Group A consisted of 69 cases with precursor glandular lesions (atypical adenomatous hyperplasia [AAH] and adenocarcinoma in situ [AIS]), inflammatory nodules, and benign nodules. Group B consisted of 135 cases with invasive lesions (minimally invasive adenocarcinoma [MIA], invasive adenocarcinoma [IAC], and other malignant lesions). Various CT features were compared between the 2 groups. The diagnostic efficacy of an AI-CAD system and radiologists’ reports for benign and malignant nodules were analyzed. A multivariate logistic regression analysis was performed to determine independent predictors of malignant GGN. A model that combined the AI system and manual extraction of radiological features was constructed. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the model.

 Significant differences were found between malignant and benign groups according to the following 7 CT features: The GGN size (long and short diameters), vacuole sign, air bronchogram sign, vascular convergence sign, vascular perforator sign, interlobular septal obstruction sign, and spiculation (P < 0.05). The volume and mean CT values of precursor glandular lesions of the lungs were significantly different from those of invasive lesions (P < 0.05). The logistic regression model showed that the sensitivity and specificity of the AI system in diagnosing malignant groups were 0.756 and 0.696, respectively. The sensitivity and specificity of radiologists’ reports in diagnosing the malignant groups were 0.726 and 0.783, respectively. The combination of the 2 had a sensitivity of 0.768 and a specificity of 0.793.

 Prediction of the nature of GGNs based on CT features, including the vacuole sign, vascular perforator sign, and interlobular septal obstruction sign, were relatively accurate for a preliminary diagnosis. The AI system had a poorer diagnostic accuracy for GGNs than radiologists’ reports of CT images. The combination of AI and radiologists’ reports showed the highest diagnostic efficacy.

 Differentiating between pulmonary sarcomatoid carcinoma (PSC) and pulmonary inflammatory pseudotumor (PIP) is challenging using current conventional diagnostic methods. This lack of distinction significantly impacts subsequent clinical treatment decisions.

 This study was conducted to construct an effective method to distinguish between PSC and PIP based on commonly used computed tomography (CT) images.

 A total of 14 patients with PSC and 76 patients with PIP were retrospectively included in the study for CT imaging. Radiomics features were extracted from non-enhanced CT images, and canonical correlation analysis was performed to reduce redundancy. The final radiomics signature was then identified using the least absolute shrinkage and selection operator (LASSO). Logistic regression (LR), classification and regression trees (CART), support vector machine (SVM), k-nearest neighbors (KNN), and gradient boosting machine (GBM) were used to construct the radiomics models. The performance of these different radiomics models was evaluated using the receiver operating characteristic curve.

 A total of 1186 radiomics features were extracted from non-enhanced CT images. After dimensionality reduction and selection, 7 valuable features were identified. The performance of 5 machine learning models was evaluated to differentiate between PSC and PIP, and the GBM-based radiomics model demonstrated the best performance. The GBM-based radiomics model achieved an accuracy of 0.922, area under the curve (AUC) of 0.98, F1 score of 0.967, and log loss of 0.161. Compared to conventional clinical-radiological diagnosis, the GBM-based radiomics model showed a significant association (odds ratio [OR] = 8.119; P = 0.006).

 The implementation of the GBM-based radiomics model has the potential to improve the ability to differentiate between PSC and PIP, thereby influencing the timeliness of subsequent surgical interventions and even the prognosis of patients.