Dual-energy spectral CT quantitative parameters for the differentiation of Glioma recurrence from treatment-related changes: a preliminary study

摘要

Differentiating glioma recurrence from treatment-related changes can be challenging on conventional imaging. We evaluated the efficacy of quantitative parameters measured by dual-energy spectral computed tomographic (CT) for this differentiation. Twenty-eight patients were examined by dual-energy spectral CT. The effective and normalized atomic number (Zeff and Zeff-N, respectively); spectral Hounsfield unit curve (λHU) slope; and iodine and normalized iodine concentration (IC and ICN, respectively) in the post-treatment enhanced areas were calculated. Pathological results or clinicoradiologic follow-up of ≥2?months were used for final diagnosis. Nonparametric and t-tests were used to compare quantitative parameters between glioma recurrence and treatment-related changes. Sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively), and accuracy were calculated using receiver operating characteristic (ROC) curves. Predictive probabilities were used to generate ROC curves to determine the diagnostic value. Examination of pre-contrast λHU, Zeff, Zeff-N, IC, ICN, and venous phase ICN showed no significant differences in quantitative parameters (P??0.05). Venous phase λHU, Zeff, Zeff-N, and IC in glioma recurrence were higher than in treatment-related changes (P??0.001). The optimal venous phase threshold was 1.03, 7.75, 1.04, and 2.85?mg/cm3, achieving 66.7, 91.7, 83.3, and 91.7% sensitivity; 100.0, 77.8, 88.9, and 77.8% specificity; 100.0, 73.3, 83.3, and 73.3% PPV; 81.8, 93.3, 88.9, and 93.3% NPV; and 86.7, 83.3, 86.7, and 83.3% accuracy, respectively. The respective areas under the curve (AUCs) were 0.912, 0.912, 0.931, and 0.910 in glioma recurrence and treatment-related changes. Glioma recurrence could be potentially differentiated from treatment-related changes based on quantitative values measured by dual-energy spectral CT imaging.