Diagnostic Value of CT Window Technique for Primary Omentum Infarction.

Objective: The diagnostic value of CT window width technique in primary omentum infarction was evaluated by this study. Methods: The abdominal CT data of 32 patients with clinically diagnosed abdominal omentum infarction were retrospectively selected and analyzed. The fixed window position was 50 HU, and the window width was 135 HU, 250 HU (abdomen), 350 HU (mediastinum), and 500 HU, respectively. The detection rate of lesions was analyzed and compared. Results: Window widths of 135 HU, 250 HU (abdomen), 350 HU (mediastinum), and 500 HU have a detection rate of 12.5% (4 cases), 62.5% (20 cases), 100% (32 cases), 100% (32 cases) for abdominal omental lesions, respectively. However, 500 HU showed worse abdominal bowel and parenchymal organs than 350 HU. Conclusion: According to the comprehensive image quality, the ideal window width for diagnosis of primary omentum infarction is 350HU (mediastinal) window width.

Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading.

OBJECTIVES: To investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery. METHODS: The retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS: The SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively. CONCLUSION: The machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.