Aim: To investigate the effect of radiomics obtained from different virtual monochromatic images (VMIs) in dual-energy spectral computed tomography (CT) on the World Health Organization/International Association for Urological Pathology (WHO/ISUP) classification of clear cell renal cell carcinoma (ccRCC).
Materials And Methods: A retrospective study of 99 ccRCC patients who underwent contrast-enhanced dual-energy CT was undertaken. ccRCC was confirmed at surgery or biopsy and graded according to the WHO/ISUP pathological grading criteria as low grade (n=68, grade I and II) or high grade (n=31, grade III and IV). Radiomics risk scores (RRSs) for differentiating high and low grades of ccRCC were constructed from 11 sets of VMI in (40-140 keV, 10 keV interval) the cortical phase. Receiver operating characteristic (ROC) curves were drawn and the area under the curves (AUCs) was calculated to evaluate the discriminatory power of RRS for each VMI. The Hosmer-Lemeshow test was used to evaluate the goodness-of-fit of each model and the decision curve was used to analyse its net benefit to patients.
Results: The AUC values for distinguishing low-from high-grade ccRCC with RRS of 40-140 keV VMIs were all >0.920. The Hosmer-Lemeshow test showed that the p-values of RRS of VMIs were >0.05, suggesting good fits. In the decision curve analysis, RRS from the 40-140 keV VMIs had similar decision curves and provided better net benefits than considering all patients either as high-grade or low-grade.
Conclusions: The RRS obtained from multiple VMIs in dual-energy spectral CT have high diagnostic efficiencies for distinguishing between low- and high-grade ccRCC with no significant differences between different VMIs.
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http://dx.doi.org/10.1016/j.crad.2021.02.033 | DOI Listing |
Transl Lung Cancer Res
December 2024
Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Lung adenocarcinoma (LUAD) is a sub-type of non-small cell lung cancer (NSCLC) that is often associated with genetic alterations, including the Kirsten rat sarcoma viral oncogene homolog () mutation. The mutation is particularly challenging to treat due to resistance to targeted therapies. This study aims to develop a predictive model for the mutation in patients with LUAD by integrating clinical, dual-energy spectral computed tomography (DESCT), and radiomics features.
View Article and Find Full Text PDFObjectives: This study evaluates the performance of a clinical dual-source photon-counting computed tomography (PCCT) system in quantifying iodine within calcified vessels, using 3D- printed phantoms with vascular-like structures lined with calcium.
Methods: Parameters assessed include lumen diameters (4, 6, 8, 10, and 12 mm), phantom sizes (S: 20×20 cm, M: 25×25 cm, L: 30×40 cm, XL: 40×50 cm, representing the 99th percentile of US patient sizes), and iodine concentrations (2, 5, and 10 mg/mL). Scans were performed at radiation dose levels of 5, 10, 15, and 20 mGy to systematically evaluate iodine quantification accuracy and spectral imaging performance.
Eur J Radiol Open
June 2025
Department of Diagnostic Radiology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba 277-8577, Japan.
Purpose: The potential of spectral images, particularly electron density and effective Z-images, generated by dual-energy computed tomography (DECT), for the histopathologic classification of lung cancer remains unclear. This study aimed to explore which imaging factors could better reflect the histopathological status of lung cancer.
Method: The data of 31 patients who underwent rapid kV-switching DECT and subsequently underwent surgery for lung cancer were analyzed.
Comput Methods Programs Biomed
January 2025
Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; School of Computer Science and Engineering, Southeast University, Nanjing, China.
Purpose: Dual-energy computed tomography (DECT) enables the differentiation of different materials. Additionally, DECT images consist of multiple scans of the same sample, revealing information similarity within the energy domain. To leverage this information similarity and address safety concerns related to excessive radiation exposure in DECT imaging, sparse view DECT imaging is proposed as a solution.
View Article and Find Full Text PDFPurpose: With the widespread introduction of dual energy computed tomography (DECT), applications utilizing the spectral information to perform material decomposition became available. Among these, a popular application is to decompose contrast-enhanced CT images into virtual non-contrast (VNC) or virtual non-iodine images and into iodine maps. In 2021, photon-counting CT (PCCT) was introduced, which is another spectral CT modality.
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