Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: Stage T1 nasopharyngeal carcinoma (NPC) 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 NPC and BH and to explore the clinical value of DECT for differentiating these 2 diseases.
Methods: A total of 91 patients (44 NPC, 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 NPC and BH. The demarcation of NPC 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 (λ) and normalized iodine concentration (NIC). The nonenhanced phase images were used to calculate the normalized effective atomic number (NZ). 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. NPC yielded higher attenuation values on VMI (+) at 40 keV (A), NIC, λ, and NZ values than BH. The multivariate logistic regression model combining image features (tumor symmetry) with quantitative parameters (A, NIC, λ, and NZ) 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 NPC and BH.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339654 | PMC |
| http://dx.doi.org/10.21037/qims-20-1269 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Comparison of two dual-energy CT-based methods for proton stopping-power ratio estimation in brain cancer patients with metal implants.
Acta Oncol
September 2025
Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark.
Background And Purpose: Accurate stopping-power ratio (SPR) estimation is crucial for proton therapy planning. In brain cancer patients with metal clips, SPR accuracy may be affected by high-density materials and imaging artefacts. Dual-energy CT (DECT)-based methods have been shown to improve SPR accuracy.
View Article and Find Full Text PDFDiagnostic accuracy of dual-layer spectral computed tomography virtual monoenergetic imaging with multiplanar reformation for T-staging of colorectal cancer.
World J Gastroenterol
August 2025
Department of Radiology, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong 226001, Jiangsu Province, China.
Background: Accurate preoperative T staging is essential for determining optimal treatment strategies in colorectal cancer (CRC). Low-keV virtual monoenergetic images (VMIs) have been shown to enhance lesion conspicuity. This study aimed to assess the diagnostic value of dual-layer spectral computed tomography (CT)-derived VMIs, in combination with multiplanar reformation (MPR) and evaluation of peritumoral fat stranding (PFS), for improving the accuracy of T staging in CRC.
View Article and Find Full Text PDFMulti-DECT Image-based Interpretable Model Incorporating Habitat Radiomics and Vision Transformer Deep Learning for Preoperative Prediction of Muscle Invasion in Bladder Cancer.
Acad Radiol
August 2025
Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian, China (L.L.). Electronic address:
Rationale And Objectives: The research aims to evaluate the effectiveness of a multi-dual-energy CT (DECT) image-based interpretable model that integrates habitat radiomics with a 3D Vision Transformer (ViT) deep learning (DL) for preoperatively predicting muscle invasion in bladder cancer (BCa).
Materials And Methods: This retrospective study analyzed 200 BCa patients, who were divided into a training cohort (n=140) and a test cohort (n=60) in a 7:3 ratio. Univariate and multivariate analyses were performed on the DECT quantitative parameters to identify independent predictors, which were subsequently used to develop a DECT model.
Novel silicon-based material decomposition images in diagnosis of pancreatic ductal adenocarcinoma: comparison with iodine-based and 50-keV virtual monoenergetic images.
Jpn J Radiol
August 2025
Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Objectives: To identify the optimal material decomposition (MD) images for diagnosis of pancreatic ductal adenocarcinoma (PDAC) and evaluate the added value of the MD image to 50-keV virtual monoenergetic images (VMIs) by comparing with iodine-based images and 50-keV VMIs.
Methods: This retrospective study included patients who underwent pancreatic protocol dual-energy CT (DECT) between February 2019 and May 2023. First, a radiologist evaluated 702 image datasets generated using 27 different materials to identify the top three MD images which provided maximum contrast difference between normal pancreas and PDAC, and subsequently, the best MD image was selected based on z value and image quality by four radiologists.
Prediction of Early Recurrence After Bronchial Arterial Chemoembolization in Non-small Cell Lung Cancer Patients Using Dual-energy CT: An Interpretable Model Based on SHAP Methodology.
Acad Radiol
August 2025
Zhejiang Province Key Laboratory of Imaging and Interventional Medicine, Wenzhou Medical University Affiliated Fifth Hospital, Zhejiang, China (Y.F., Y.X., B.L., Z.D., L.Z., H.H., W.W., J.M., J.T.); Interventional Department, Wenzhou Medical University Affiliated Fifth Hospital, Zhejiang, China (L
Rationale And Objectives: Bronchial artery chemoembolization (BACE) is a new treatment method for lung cancer. This study aimed to investigate the ability of dual-energy computed tomography (DECT) to predict early recurrence (ER) after BACE among patients with non-small cell lung cancer (NSCLC) who failed first-line therapy.
Materials And Methods: Clinical and imaging data from NSCLC patients undergoing BACE at Wenzhou Medical University Affiliated Fifth *** Hospital (10/2023-06/2024) were retrospectively analyzed.