Computational quantitative measures of Gd-EOB-DTPA enhanced MRI hepatobiliary phase images can predict microvascular invasion of small HCC. Issue 133 (December 2020)

• Record Type: Journal Article
• Authors: Wang, Xinxin; Zhang, Ziqian; Zhou, Xueyan; Zhang, Yuning; Zhou, Jiamin; Tang, Shuli; Liu, Yang; Zhou, Yang
• Is Part Of: European journal of radiology. Issue 133(2020)
• Publication Date: 2020-12
• Subjects: sHCC small hepatocellular carcinoma -- MVI microvascular invasion -- Gd-EOB-DTPA gadolinium-ethoxybenzyl-diethylenetriamine -- MRI magnetic resonance imaging -- RFA radiofrequency ablation -- HR hepatic resection -- HBP hepatobiliary phase -- ROI regions of interest -- LBI lesion boundary index -- MGC marginal gray changes -- STD standard deviations -- CV coefficients of variation -- ROC receiver operating characteristic -- AUC area under the curve -- PPV positive predictive value -- NPV negative predictive value; Microvascular invasion -- sHCC -- Quantitative analysis -- Magnetic resonance imaging -- Gd-EOB-DTPA; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2020.109361
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Using HBP images predicted sHCC with MVI preoperatively. Quantitative analysis can be used on MVI diagnosis. LBI on HBP images is most useful on predicting MVI. Abstract: Purpose: This study was designed to preoperatively predict microvascular invasion (MVI) of solitary small hepatocellular carcinoma (sHCC) by quantitative analysis of Gd-EOB-DTPA enhanced hepatobiliary phase (HBP) magnetic resonance imaging (MRI). Method: Sixty-one patients, 19 with and 42 without histologically confirmed MVI following hepatic resection for solitary sHCC (≤ 3 cm), were preoperatively examined with Gd-EOB-DTPA-enhanced MRI. The regions of interest (ROIs) of the hepatic lesions were manually delineated on the maximum cross-sectional area in the HBP images and used to calculate the lesion boundary index (LBI) and marginal gray changes (MGC). Histogram analysis was performed to measure standard deviations (STD) and coefficients of variation (CV). Correlations between quantitative parameters and MVI were evaluated and differences between MVI positive and negative groups were assessed. Results: The average LBI (0.85 ± 0.07) and MGC (0.48 ± 0.27) values of the negative group were significantly higher ( p < 0.05) than the corresponding LBI (0.72 ± 0.07) and MGC (0.28 ± 0.18) values of the positive group. STDs and CVs in the negative group were significantly smaller ( p < 0.05) than those of the positive group. Receiver operating characteristic (ROC) analysis revealed that LBI had the best predictive value with an AUC, sensitivity, and specificity of 0.91, 87 %, and 80 %, respectively. Conclusions: Quantitative analysis of HBP images is useful for predicting MVI and beneficial to clinicians in making decisions before treatment.