Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia

Background: Stage T1 nasopharyngeal carcinoma (NPCT1) and benign hyperplasia (BH) are 2 common causes of nasopharyngeal mucosa/submucosa thickening without specific clinical symptoms. The treatment management of these 2 entities is significantly different. Reliable differentiation between the 2 entities is critical for the treatment decision and prognosis of patients. Therefore, our study aims to explore the optimal energy level of noise-optimized virtual monoenergetic images [VMI (+)] derived from dual-energy computed tomography (DECT) to display NPCT1 and BH and to explore the clinical value of DECT for differentiating these 2 diseases. Methods: A total of 91 patients (44 NPCT1, 47 BH) were enrolled. The demarcation of the lesion margins and overall image quality, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were evaluated for 40-80 kiloelectron volts (keV) VMIs (+) and polyenergetic images in the contrast-enhanced phase. Image features were assessed in the contrast-enhanced images with optimal visualization of NPCT1 and BH. The demarcation of NPCT1 and BH in iodine-water maps was also assessed. The contrast-enhanced images were used to calculate the slope of the spectral Hounsfield unit curve (lambda HU) and normalized iodine concentration (NIC). The nonenhanced phase images were used to calculate the normalized effective atomic number (NZeff). The attenuation values on 40-80 keV VMIs (+) in the contrast-enhanced phase were recorded. The diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Results: The 40 keV VMI (+) in the enhanced phase yielded higher demarcation of the lesion margins scores, overall image quality scores, noise, SNR, and CNR values than 50-80 keV VMIs (+) and polyenergetic images. NPCT1 yielded higher attenuation values on VMI (+) at 40 keV (A40), NIC, lambda HU, and NZeff values than BH. The multivariate logistic regression model combining image features (tumor symmetry) with quantitative parameters (A40, NIC, lambda HU, and NZeff) yielded the best performance for differentiating the 2 diseases (AUC: 0.963, sensitivity: 89.4%, specificity: 93.2%). Conclusions: The combination of DECT-derived image features and quantitative parameters contributed to the differentiation between NPCT1 and BH.

Automated summary

The study aimed to differentiate between NPCT1 and BH using DECT-derived virtual monoenergetic images, with 40 keV VMI(+) showing better demarcation, quality, and contrast effects in the enhanced phase. The combination of image features and quantitative parameters resulted in effective differentiation between the two diseases, with high sensitivity and specificity.