Iranian Journal of Radiology
Volume:20 Issue: 2, Apr 2023

Osteoporosis is a common chronic disease that can cause fractures and other complications. Early diagnosis and prompt treatment of this disease can reduce the risk of fracture. The trabecular bone score (TBS) is a valuable tool for evaluating bone microarchitecture in patients with osteoporosis.

The present study aimed to evaluate the association of TBS score with bone mineral density (BMD) as the gold standard in patients with osteoporosis.

This cross-sectional study was performed on 254 patients admitted to Resalat Hospital in Tehran, Iran. The BMD and TBS were concurrently evaluated, and TBS validity for osteoporosis diagnosis was assessed based on BMD as the gold standard. The agreement between TBS and BMD results was measured using the kappa statistic. All analyses were performed in MedCalc version 18.11.3.8 and SPSS version 18, and the level of statistical significance was set at < 0.05.

In this study, the majority of the patients were female (88.2%) and younger than 65 years (63.0%). Osteopenia and osteoporosis were diagnosed in 42.1% and18.9% of the patients, respectively. The sensitivity of TBS for diagnosing osteoporosis versus osteopenia and osteopenia versus a normal status was higher in patients aged  65 years compared to those aged < 65 years (63.33% vs. 27.78% and 86.66% vs. 63.63%, respectively). Overall, TBS demonstrated a high diagnostic ability for differentiating osteoporosis versus normal BMD (AUC = 0.911, P < 0.001).

Based on the findings, TBS alone is not sufficient for diagnosing osteoporosis and cannot replace BMD. Therefore, a combination of BMD and TBS techniques can be the best approach for diagnosing osteoporosis, especially in patients aged  65 years.

Due to its non-specific symptoms, pulmonary arterial hypertension (PAH) is difficult to diagnose via non-invasive methods. Various diagnostic tests are required to evaluate PAH patients. The increased diameter of the main pulmonary artery in computed tomography (CT) imaging represents a high probability of PAH. Moreover, N-terminal pro B-type natriuretic peptide (NT-proBNP) and pro B-type natriuretic peptide (proBNP) can be considered as prognostic predictors in patients with PAH.

This study aimed to evaluate the correlation of CT-based main pulmonary artery diameter (MPAD) and the serum level of NT-proBNP (as a strong pro-inflammatory factor) with the severity of PAH in echocardiography among patients with PAH.

In this cross-sectional study, a total of 63 hospitalized patients with PAH due to chronic obstructive pulmonary disease were recruited from 2019 to 2020 after initial evaluations and collection of serum NT-proBNP measurements and echocardiographic findings. On the chest CT scans, the largest diameter of the pulmonary artery trunk was determined, and then, correlation of CT-based MPAD with both PAH severity on echocardiography and NT-proBNP level in patients with PAH were evaluated.

The results of the present study on 63 patients (70% male; mean age, 67.02 years) showed a significant positive correlation between the MPAD and NT-proBNP level (r = 0.444, P < 0.001). Moreover, a significant positive relationship was observed between the pulmonary artery pressure (PAP) and NT-proBNP (r = 0.353, P = 0.005) and also between MPAD and PAP (r = 0.306, P = 0.015). In PAH patients, the mean values of MPAD, PAP, and NT-proBNP were 32.58 mm, 47.9 mmHg, and 6563 pg/mL, respectively.

Considering the significant positive correlation between PAP, MPAD, and NT-proBNP level in subgroup comparisons based on MPAD and PAP, if the MPAD is abnormal on CT scan, additional echocardiographic assessments and serum NT-proBNP measurements can be helpful.

Dual-energy computed tomography (DECT) scan is a non-invasive method for the in vivo identification of renal stone composition. However, DECT scanners have several demerits, including high cost, low accessibility, and high radiation dose to patients.

The present study aimed to investigate the efficacy of deep neural networks in the classification of renal stone types using single-energy CT imaging. The Taguchi method was used for the optimization of hyperparameters.

A total of 146 pure renal stone samples were first surgically collected from the patients. The stones were then inserted into a Rando phantom and scanned using a DECT scanner. An ultra-low-dose CT scan was acquired to determine the stone position prior to the DECT scan. The result of chemical analysis was used as the gold standard for determining the stone composition throughout the study. Several neural networks, including ResNet-50, ResNet-18, GoogLeNet, and VGG-19, were used to classify the stone images into three composition groups, including uric acid, calcium oxalate, and cystine. Moreover, the Taguchi method was employed to optimize the network hyperparameters. The signal-to-noise ratio (SNR) was also analyzed to determine the optimal arrangement.

In this study, CT scans of 53 uric acid, 55 calcium oxalate, and 38 cystine stones, with diameters of 1 - 3 mm, were acquired. The deep learning findings showed that the ResNet-18 network had the highest accuracy for 120-kVp and 135-kVp CT scanning, while ResNet-50 performed better in 80-kVp CT scanning. The ResNet-50 network showed the best performance of all networks in predicting stone types in 80-kVp scanning, as indicated by its high sensitivity, specificity, and precision.

The present results indicated that our deep learning approach could be used for the in vivo determination of renal stone types. Moreover, low-dose or ultra-low-dose single-energy CT scanning ismorewidely accessibleand safer in terms of radiation exposure.

Hepatocellular carcinoma (HCC) is characterized by high morbidity and mortality rates around the world, ranking the sixth most common malignant tumor and the second cause of cancer-related mortality. Most patients are diagnosed in the advanced stage, and therefore, there are limited therapeutic options. Transarterial chemoembolization (TACE), anti-angiogenic therapy, and immune checkpoint inhibitors (ICIs) are the research hotspots in HCC treatment.

This study aimed to explore the treatment efficacy and safety of TACE for refractory HCC patients after anti-angiogenic therapy combined with ICIs.

In this study, patients with HCC, who progressed after anti-angiogenic therapy combined with ICIs, were included from July 2019 to October 2022. The progression-free survival (PFS) was evaluated by the Kaplan-Meier method, and the tumor response was determined based on the modified immune response evaluation criteria in solid tumors (iRECIST). The Common Terminology Criteria for Adverse Events version 5.0 were also used to assess the adverse events.

A total of 34 patients were evaluated in this study, with a median PFS of five months (95% CI: 3.7 months, 6.3 months). The univariate analysis suggested that the level of aspartate aminotransferase was significantly associated with PFS (P < 0.05). The objective response rates within three and six months were 26.4% and 14.6%, and the disease control rates were 58.8% and 55.9%, respectively. During the follow-up, one or more types of adverse events were reported in 10 (58.8%) patients after treatment with atezolizumab and bevacizumab, while severe adverse events beyond grade III did not occur in any of the patients.

The TACE may improve the survival of HCC patients, whose disease progresses after anti-angiogenic therapy combined with ICIs. However, the lack of a control group is one of the limitations of this study.

With the development of computed tomography (CT) technology, the number of infants undergoing CT examination has increased. Recently, problems associated with radiation exposure have attracted the public attention, and low-dose CT examination has become the research focus.

This study aimed to explore the feasibility of a fixed ultralow tube current combined with iDose4 iterative reconstruction at a low tube voltage (80 kV) in low-dose CT scanning of infant lungs.

Ninety infants, aged 0 - 3 years, undergoing enhanced chest CT scan (including plain CT scan and arterial scan), were included in the experimental group. In the experimental group, plain CT scan was performed at 80 kV under a fixed tube current (30 mA), and arterial phase CT scan was carried out at 80 kV with automatic tube current modulation (30 - 150 mA). Meanwhile, 90 infants, aged 0 - 3 years, undergoing non-enhanced chest CT scan, were randomly recruited as the control group. For the control group, plain CT scan was performed at 80 kV with automatic tube current modulation (30-150 mA). In each examination, the iDose4 iterative reconstruction technique was used for reconstruction. The volume CT dose index (CTDIvol), dose-length product (DLP), effective dose (ED, measured using a 32-cm diameter phantom), signal-to-noise ratio (SNR), and overall subjective quality of plain CT scans were analyzed and compared between the experimental and control groups.

Regarding the radiation dose in different age groups, the ED (mSv) was significantly lower in the experimental group compared to the control group (0-1-year-old subgroup: 0.48 ± 0.05 mSv in the experimental group vs. 0.91 ± 0.19 mSv in the control group, P < 0.05; 1-2-year-old subgroup: 0.50 ± 0.01 mSv in the experimental group vs. 0.75 ± 0.10 mSv in the control group, P < 0.05; and 2-3-year-old subgroup: 0.46 ± 0.05 mSv in the experimental group vs. 0.79 ± 0.11 mSv in the control group, P < 0.05). The image SNR was higher in the control group compared to the experimental group (P < 0.05), while the difference in subjective image quality was not significant between the experimental and control groups (P < 0.05).

The chest CT examination of infants, aged 0 - 3 years, at a low tube voltage (80 kV) and a fixed tube current (30 mA), combined with the iDose4 iterative reconstruction technique, and the obtained images met the diagnostic criteria...

Pulmonary mass-like lesions are one of the common manifestations of respiratory disorders. Differential diagnosis of these lesions is a major challenge in imaging studies.

This study aimed to explore the efficacy of spectral computed tomography (CT) features combined with conventional CT features in the differential diagnosis of pulmonary mass-like lesions.

This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. The imaging characteristics and spectral CT parameters were evaluated in 77 patients who met the inclusion criteria. A multivariate logistic regression analysis was performed to determine independent predictors of malignant pulmonary lump-like lesions. Three models were established, including a radiomic feature model, a spectral CT model, and a combined model. A receiver operating characteristic (ROC) curve was also plotted to evaluate the diagnostic efficiency of the models.

Some CT features were significantly different between the malignant and benign groups, including the long-axis diameter (44.86 ± 18.42 in the malignant group vs. 55.59 ± 22.57 in the benign group; P = 0.07), mediastinal lymphadenopathy (25.00% in the benign group vs. 62.26% in the malignant group; P = 0.02), and mediastinal lymph node confluence (4.17% in the benign group vs. 41.51% in the malignant group; P = 0.01). The CT values at 40 keV (157.25 ± 79.23 vs. 148.46 ± 25.36, P = 0.047) and K40 - 70 keV (2.76 ± 2.05 vs. 2.52 ± 0.60, P = 0.04) were significantly higher in the benign group compared to the malignant group in the arterial phase (AP). Besides, the iodine concentration (IC) (14.73 ± 10.65 vs. 13.44 ± 3.24, P = 0.039; 17.52 ± 5.29 vs. 13.87 ± 5.81, P = 0.035), normalized iodine concentration (NIC) (0.15 ± 0.06 vs. 0.11 ± 0.05, P = 0.015; 0.41 ± 0.11 vs. 0.35 ± 0.10, P = 0.017), and Zeff value (8.46 ± 0.63 vs. 8.43 ± 0.28, P = 0.034; 8.60 ± 0.29 vs. 8.39 ± 0.33, P = 0.035) were significantly higher in the benign group compared to the malignant group, both in the AP and venous phase (VP). The logistic regression model, integrating CT features and spectral CT parameters, showed the highest diagnostic efficacy (area under the curve [AUC], 0.956; sensitivity, 87.5%; specificity, 90.6%).

The quantitative spectral CT parameters, combined with conventional CT features, could help distinguish benign and malignant pulmonary mass-like lesions, providing an essential basis for developing treatment plans.

Deep learning methods used for bone age assessment (BAA) mostly employ the whole hand or regional convolutional neural networks without carpal bones; therefore, their application is insufficient in young children.

This study aimed to improve the accuracy of BAA in young children by integrating a carpal bone analysis and to achieve a similar BAA accuracy for all age groups.

A hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse deep learning model for BAA was trained by integrating an additional carpal bone analysis of an open dataset. A total of 453 hand radiographs from a single institution were selected for external validation. To create the reference standard, three human experts conducted a BAA, based on the GP Atlas, and then, interobserver agreement was evaluated. The model performance was estimated by comparing the mean absolute difference (MAD) and the root mean square error (RMSE) between the two BAA models, including one with a carpal bone analysis (M1) and one without a carpal bone analysis (M2), and the reference standard. TheMADof each model was compared between sex and age groups with respect to four major developmental stages, that is, pre-puberty, early and mid-puberty, late puberty, and post-puberty.

The M1 model showed a higher accuracy with a lower MAD (0.366; 95% confidence interval (CI): 0.337 - 0.395) compared to the M2 model (0.388; 95% CI: 0.358 - 0.418) for all age groups, with a significant difference (P < 0.001). The RMSE values versus the reference standard were 0.483 and 0.505 years for the M1 and M2 models, respectively. According to sex and developmental stage distributions, the M1 model had a greater predictive ability compared to the M2 model for pre-pubertal patients, regardless of sex (P = 0.008 for males and P = 0.022 for females).

Based on the present findings, the integration of a carpal bone analysis into the BAA model improved its accuracy, especially in young children.

Despite the important role of coronary computed tomography angiography (CCTA) in determining the features of coronary atherosclerotic plaques, very few studies have investigated the role of plaque dimensions in the process of coronary artery obstruction. Therefore, this study aimed to assess the relationship between the characteristics and dimensions of calcified plaques and coronary artery stenosis using CCTA.

This study aimed to evaluate the relationship between calcified plaque dimensions and coronary artery stenosis in patients undergoing CCTA. Patients and

This cross-sectional study was conducted on 211 plaques of candidate patients for prospective and retrospective electrocardiography (ECG)-gated CCTA, using spiral scanning at 120 kV. The length, width, thickness, and luminal areas of calcified plaques were assessed in all coronary arteries.

The mean rate of stenosis was estimated to be 56.1 ± 24.4%. The number of patients with stenosis < 50% was 100 (47.3%). Stenosishada significant relationship with the plaque luminal areaandthe luminal dimeter of thenormalcoronary artery proximal and distal to the plaque (P < 0.05). However, there was no significant correlation between stenosis and normal luminal area (P > 0.05). Meanwhile, a significant relationship was found between stenosis and the width and length of plaques (P < 0.05).

The present results indicated a significant association between stenosis and the width, length, and luminal area of plaques. It can be concluded that plaque dimensions are a predictive factor for stenosis.