Iodine quantification of renal lesions: Preliminary results using spectral-based material extraction on photon-counting CT.

Autor: Tóth A; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, United States., Chamberlin JH; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, United States., Mendez S; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, United States., Varga-Szemes A; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, United States., Hardie AD; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, United States.
Jazyk: angličtina
Zdroj: Journal of clinical imaging science [J Clin Imaging Sci] 2024 Mar 08; Vol. 14, pp. 7. Date of Electronic Publication: 2024 Mar 08 (Print Publication: 2024).
DOI: 10.25259/JCIS_1_2024
Abstrakt: Objectives: To assess the range of quantitative iodine values in renal cysts (RC) (with a few renal neoplasms [RNs] as a comparison) to develop an expected range of values for RC that can be used in future studies for their differentiation.
Material and Methods: Consecutive patients ( n = 140) with renal lesions who had undergone abdominal examination on a clinical photon-counting computed tomography (PCCT) were retrospectively included. Automated iodine quantification maps were reconstructed, and region of interest (ROI) measurements of iodine concentration (IC) (mg/cm 3 ) were performed on whole renal lesions. In addition, for heterogeneous lesions, a secondary ROI was placed on the area most suspicious for malignancy. The discriminatory values of minimum, maximum, mean, and standard deviation for IC were compared using simple logistic regression and receiver operating characteristic curves (area under the curve [AUC]).
Results: A total of 259 renal lesions (243 RC and 16 RN) were analyzed. There were significant differences between RC and RN for all IC measures with the best-performing metrics being mean and maximum IC of the entire lesion ROI (AUC 0.912 and 0.917, respectively) but also mean and minimum IC of the most suspicious area in heterogeneous lesions (AUC 0.983 and 0.992, respectively). Most RC fell within a range of low measured iodine values although a few had higher values.
Conclusion: Automated iodine quantification maps reconstructed from clinical PCCT have a high diagnostic ability to differentiate RCs and neoplasms. The data from this pilot study can be used to help establish quantitative values for clinical differentiation of renal lesions.
Competing Interests: The authors disclose Andrew D. Hardie received financial considerations either ongoing or in the past from Siemens entities.
(© 2024 Published by Scientific Scholar on behalf of Journal of Clinical Imaging Science.)
Databáze: MEDLINE
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