Literature DB >> 33052738

Normalized Dual-Energy Iodine Ratio Best Differentiates Renal Cell Carcinoma Subtypes Among Quantitative Imaging Biomarkers From Perfusion CT and Dual-Energy CT.

Dinesh Manoharan1, Arjunlokesh Netaji1, Kanika Diwan1, Sanjay Sharma1.   

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.

Entities:  

Keywords:  CT; biomarkers; dual-energy CT; iodine; low-kilovoltage CT; perfusion imaging; renal cell carcinoma

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Year:  2020        PMID: 33052738     DOI: 10.2214/AJR.19.22612

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  2 in total

1.  Implementation of Hospital-to-Home Model for Nutritional Nursing Management of Patients with Chronic Kidney Disease Using Artificial Intelligence Algorithm Combined with CT Internet.

Authors:  Xing Chen; Xueqin Huang; Mingyuan Yin
Journal:  Contrast Media Mol Imaging       Date:  2022-03-27       Impact factor: 3.161

2.  Effect of spectral CT on tumor microvascular angiogenesis in renal cell carcinoma.

Authors:  Bei Zhang; Qiong Wu; Xiang Qiu; Xiaobo Ding; Jin Wang; Jing Li; Pengfei Sun; Xiaohan Hu
Journal:  BMC Cancer       Date:  2021-07-30       Impact factor: 4.430

  2 in total

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