Magnetic resonance imaging with gadoxetic acid for local tumour progression after radiofrequency ablation in patients with hepatocellular carcinoma.
Journal Article (Journal Article)
OBJECTIVES: To develop and validate a prediction model using magnetic resonance imaging (MRI) for local tumour progression (LTP) after radiofrequency ablation (RFA) in hepatocellular carcinoma (HCC) patients. METHODS: Two hundred and eleven patients who had received RFA as first-line treatment for HCC were retrospectively analyzed. They had undergone gadoxetic acid-enhanced MRI before treatment, and parameters including tumour size; margins; signal intensities on T1-, T2-, and diffusion-weighted images, and hepatobiliary phase images (HBPI); intratumoral fat or tumoral capsules; and peritumoural hypointensity in the HBPI were used to develop a prediction model for LTP after treatment. This model to discriminate low-risk from high-risk LTP groups was constructed based on Cox regression analysis. RESULTS: Our analyses produced the following model: 'risk score = 0.617 × tumour size + 0.965 × tumour margin + 0.867 × peritumoural hypointensity on HBPI'. This was able to predict which patients were at high risk for LTP after RFA (p < 0.001). Patients in the low-risk group had a significantly better 5-year LTP-free survival rate compared to the high-risk group (89.6 % vs. 65.1 %; hazard ratio, 3.60; p < 0.001). CONCLUSION: A predictive model based on MRI before RFA could robustly identify HCC patients at high risk for LTP after treatment. KEY POINTS: • Tumour size, margin, and peritumoural hypointensity on HBPI were risk factors for LTP. • The risk score model can predict which patients are at high risk for LTP. • This prediction model could be helpful for risk stratification of HCC patients.
- Kang, TW; Rhim, H; Lee, J; Song, KD; Lee, MW; Kim, Y-S; Lim, HK; Jang, KM; Kim, SH; Gwak, G-Y; Jung, S-H
- October 2016
Volume / Issue
- 26 / 10
Start / End Page
- 3437 - 3446
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)