首页|Hepatobiliary phase images of gadoxetic acid-enhanced MRI may improve accuracy of predicting the size of hepatocellular carcinoma at pathology
Hepatobiliary phase images of gadoxetic acid-enhanced MRI may improve accuracy of predicting the size of hepatocellular carcinoma at pathology
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NSTL
Sage
Background Gadoxetic acid-enhanced magnetic resonance imaging (MRI) has been widely used in clinical practice. However, scientific evidence is lacking for recommending a particular sequence for measuring tumor size. Purpose To retrospectively compare the size of hepatocellular carcinoma (HCC) measured on different gadoxetic acid-enhanced MRI sequences using pathology as a reference. Material and Methods A total of 217 patients with single HCC who underwent gadoxetic acid-enhanced MRI before surgery were included. The size of the HCC was measured by two abdominal radiologists independently on the following sequences: T1-weighted; T2-weighted; b-500 diffusion-weighted imaging (DWI); and arterial, portal venous, transitional, and hepatobiliary phases. Tumor size measured on MRI was compared with pathological size by using Pearson correlation coefficient, independent-sample t test, and Bland-Altman plot. Agreement between two readers was evaluated with intraclass correlation coefficient (ICC). Results Correlation between the MR images and pathology was high for both readers (0.899-0.955). Absolute error between MRI and pathologic assessment was lowest on hepatobiliary phase images for both readers (reader 1, 2.8 +/- 4.2 mm; reader 2, 3.2 +/- 3.4 mm) and highest on arterial phase images for reader 1 (4.9 +/- 4.4 mm) and DWI phase images for reader 2 (5.1 +/- 4.9 mm). Absolute errors were significantly different for hepatobiliary phase compared with other sequences for both readers (reader 1, P <= 0.012; reader 2, P <= 0.037). Inter-reader agreements for all sequence measurements were strong (0.971-0.997). Conclusion The performance of gadoxetic acid-enhanced MRI sequences varied with HCC size, and the hepatobiliary phase may be optimal among these sequences.
NSTL
Sage
