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• Modern CT provides excellent anatomical detail of congenital heart disease. • Dual source CT systems offer high-pitch spiral and sequential modes. • The high-pitch mode provides high accuracy for the assessment of CHD. • A few images using the high-pitch mode were occasionally slightly degraded. • But the high-pitch mode significantly lowers the radiation dose.

Background: Preoperative differentiation between malignant and benign tumors is important for treatment decisions. Purpose/Hypothesis: To investigate/validate a radiomics nomogram for preoperative differentiation between malignant and benign masses. Study Type: Retrospective. Population: Imaging data of 91 patients. Field Strength/Sequence: T 1 -weighted images (570 msec repetition time [TR]; 17.9 msec echo time [TE], 200-400 mm field of view [FOV], 208-512 × 208-512 matrix), fat-suppressed fast-spin-echo (FSE) T 2 -weighted images (T 2 WIs) (4331 msec TR; 87.9 msec TE, 200-400 mm FOV, 312 × 312 matrix), slice thickness 4 mm, and slice spacing 1 mm. Assessment: Fat-suppressed FSE T 2 WIs were selected for extraction of features. Radiomics features were extracted from fat-suppressed T 2 WIs. A radiomics signature was generated from the training dataset using least absolute shrinkage and selection operator algorithms. Independent risk factors were identified by multivariate logistic regression analysis and a radiomics nomogram was constructed. Nomogram capability was evaluated in the training dataset and validated in the validation dataset. Performance of the nomogram, radiomics signature, and clinical model were compared. Statistical Tests: 1) Independent t-test or Mann-Whitney U-test: for continuous variables. Fisher's exact test or χ 2 test: comparing categorical variables between two groups. Univariate analysis: evaluating associations between clinical/morphological characteristics and malignancy. 2) Least absolute shrinkage and selection operator (LASSO)-logistic regression model: selection of malignancy features. 3) Significant clinical/morphological characteristics and radiomics signature were input variables for multiple logistic regression analysis. Area under the curve (AUC): evaluation of ability of the nomogram to identify malignancy. Hosmer-Lemeshow test and decision curve: evaluation and validation of nomogram results. Results: The radiomics nomogram was able to differentiate malignancy from benignity in the training and validation datasets with an AUC of 0.94. The nomogram outperformed both the radiomics signature and clinical model alone. Data Conclusion: This radiomics nomogram is a noninvasive, low-cost preoperative prediction method combining the radiomics signature and clinical model. Level of Evidence: 3 Technical Efficacy: Stage 2

ObjectivesTo evaluate the feasibility of dose-modulated retrospective ECG-gated thoracoabdominal aorta CT angiography (CTA) assessing abdominal aortic intimal flap motion and investigate the motion characteristics of intimal flap in acute aortic dissection (AAD).Materials and Methods49 patients who had thoracoabdominal aorta retrospective ECG-gated CTA scan were enrolled. 20 datasets were reconstructed in 5% steps between 0 and 95% of the R-R interval in each case. The aortic intimal flap motion was assessed by measuring the short axis diameters of the true lumen and false lumen 2 cm above of celiac trunk ostium in different R-R intervals. Intimal flap motion and configuration was assessed by two independent observers.ResultsIn these 49 patients, 37 had AAD, 7 had intramural hematoma, and 5 had negative result for acute aortic disorder. 620 datasets of 31 patients who showed double lumens in abdominal aorta were enrolled in evaluating intimal flap motion. The maximum and minimum true lumen diameter were 12.2±4.1 mm (range 2.6∼17.4) and 6.7±4.1 mm (range 0∼15.3) respectively. The range of intimal flap motion in all patients was 5.5±2.6 mm (range 1.8∼10.2). The extent of maximum true lumen diameter decreased during a cardiac cycle was 49.5%±23.5% (range 12%∼100%). The maximum motion phase of true lumen diameter was in systolic phase (5%∼40% of R-R interval). Maximum and minimum intimal flap motion was at 15% and 75% of the R-R interval respectively. Intimal flap configuration had correlation with the phase of cardiac cycle.ConclusionsAbdominal intimal flap position and configuration varied greatly during a cardiac cycle. Retrospective ECG-gated thoracoabdominal aorta CTA can reflect the actual status of the true lumen and provide more information about true lumen collapse. This information may be helpful to diagnosis and differential diagnosis of dynamic abstraction.

Background: The purpose of this study was to develop and validate a radiomics nomogram for preoperative differentiating focal nodular hyperplasia (FNH) from hepatocellular carcinoma (HCC) in the non-cirrhotic liver. Methods: A total of 156 patients with FNH (n = 55) and HCC (n = 101) were divided into a training set (n = 119) and a validation set (n = 37). Radiomics features were extracted from triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm, and a radiomics score (Radscore) was calculated. Clinical data and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed by multivariate logistic regression analysis. Nomogram performance was assessed with respect to discrimination and clinical usefulness. Results: Four thousand two hundred twenty-seven features were extracted and reduced to 10 features as the most important discriminators to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.964; 95% confidence interval [CI], 0.934-0.995) and the validation set (AUC, 0.865; 95% CI, 0.725-1.000). Age, Hepatitis B virus infection, and enhancement pattern were the independent clinical factors. The radiomics nomogram, which incorporated the Rad-score and clinical factors, showed good discrimination in the training set (AUC, 0.979; 95% CI, 0.959-0.998) and the validation set (AUC, 0.917; 95% CI, 0.800-1.000), and showed better discrimination capability (P < 0.001) compared with the clinical factors model (AUC, 0.799; 95% CI, 0.719-0.879) in the training set. Decision curve analysis showed the nomogram outperformed the clinical factors model in terms of clinical usefulness. Conclusions: The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating FNH from HCC in the noncirrhotic liver, which might facilitate clinical decision-making process.

Background Pulmonary mucosa‐associated lymphoid tissue lymphoma (MALToma) is the most frequent subset of primary pulmonary lymphoma. This study aimed to identify radiologic characteristics of pulmonary MALToma based on computed tomography (CT) observations and pathologic features, and further investigate its prognosis. Methods Sixty‐six patients (55.4 ± 10.9 years; 51.5% male) diagnosed as pulmonary MALToma by pathology were retrospectively enrolled. According to distributions and features of lesions shown on CT, patients were divided into three patterns, including single nodular/mass, multiple nodular/mass, and pneumonia‐like consolidative. Results Variety of the location and extent of the lymphomatous infiltration accounted for different characteristics demonstrated at CT. The pneumonia‐like consolidative pattern was the most frequent pattern observed in 42 patients (63.6%), followed by single nodular/mass (21.2%) and multiple nodular/mass (15.2%). CT features included air bronchogram (72.7%), well‐marginated halo sign (53.0%), coarse spiculate with different lengths (72.7%), angiogram sign (77.1% of 35 patients), peribronchovascular thickening (48.5%), irregular cavitation (16.7%) and pulmonary cyst (7.6%). The estimated 5‐year cumulative overall survival rate of pulmonary MALToma was 100.0%. Conclusions Pulmonary MALToma demonstrates several characteristics at CT. Identification of the significant pulmonary abnormalities of this indolent disease entity might be helpful for early diagnosis and optimal treatment.

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