Computer-Aided Diagnosis for Preoperative Invasion Depth of Gastric Cancer with Dual-Energy Spectral CT Imaging

Abstract

This study evaluates the accuracy of dual-energy spectral computed tomography (DEsCT) imaging with the aid of computer-aided diagnosis (CAD) system in assessing serosal invasion in patients with gastric cancer. Thirty patients with gastric cancer were enrolled in this study. Two types of features (information) were collected with the use of DEsCT imaging: conventional features including patient's clinical information (eg, age, gender) and descriptive characteristics on the CT images (eg, location of the lesion, wall thickness at the gastric cardia) and additional spectral CT features extracted from monochromatic images (eg, 60keV) and material-decomposition images (eg, iodine- and water-density images). The classification results of the CAD system were compared to pathologic findings. Important features can be found out using support vector machine classification method in combination with feature-selection technique thereby helping the radiologists diagnose better. Statistical analysis showed that for the collected cases, the feature "long axis" was significantly different between group A (serosa negative) and group B (serosa positive) (P < .05). By adding quantitative spectral features from several regions of interest (ROIs), the total classification accuracy was improved from 83.33% to 90.00%. Two feature ranking algorithms were used in the CAD scheme to derive the top-ranked features. The results demonstrated that low single-energy (approximately 60keV) CT values, tumor size (long axis and short axis), iodine (water) density, and Effective-Z values of ROIs were important for classification. These findings concurred with the experience of the radiologist. The CAD system designed using machine-learning algorithms may be used to improve the identification accuracy in the assessment of serosal invasion in patients of gastric cancer with DEsCT imaging and provide some indicators which may be useful in predicting prognosis.