Normalized Dual-Energy Iodine Ratio Best Differentiates Renal Cell Carcinoma Subtypes Among Quantitative Imaging Biomarkers From Perfusion CT and Dual-Energy CT
- 1 December 2020
- journal article
- research article
- Published by American Roentgen Ray Society in American Journal of Roentgenology
- Vol. 215 (6), 1389-1397
- https://doi.org/10.2214/ajr.19.22612
Abstract
OBJECTIVE. The objective of our study was to assess and compare the diagnostic accuracy of perfusion CT (PCT) and dual-energy CT (DECT) in differentiating clear cell renal cell carcinoma (ccRCC) from non-ccRCC. MATERIALS AND METHODS. This retrospective study included 51 patients with 52 renal cell carcinomas (RCCs) (36 ccRCCs and 16 non-ccRCCs) who underwent both PCT and DECT before surgery or biopsy between January 2014 and December 2018. Three independent readers measured blood flow, blood volume (BV), and permeability using PCT and iodine concentration (IC) and iodine ratio using DECT. Interreader agreement was calculated using the intraclass correlation coefficient (ICC). Multivariable logistic regression analysis was performed to assess PCT and DECT models. Size-specific dose estimates of the two methods were compared. RESULTS. BV (ICC, 0.93) and iodine ratio (ICC, 0.85) were the most reproducible parameters. Both PCT and DECT were significant models (p < 0.05, all readers) for differentiating ccRCC from non-ccRCC. There was no significant difference in diagnostic accuracy between PCT and DECT (p > 0.05). BV and iodine ratio were independent predictors of nonccRCC (p < 0.05). However, the mean size-specific dose estimate was 16 times lower with DECT than with PCT (p < 0.001). The AUC of iodine ratio was 0.95, and sensitivity, specificity, and accuracy with an iodine ratio cutoff of 63.72% was 0.90, 0.86, and 0.87, respectively. CONCLUSION. PCT and DECT had comparable and high diagnostic accuracy in differentiating RCC subtypes; however, because of the significantly lower radiation dose of DECT, iodine ratio may be used as the best independent predictor.Keywords
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