Kang Tae Wook, Rhim Hyunchul, Lee Jisun, Song Kyoung Doo, Lee Min Woo, Kim Young-Sun, Lim Hyo Keun, Jang Kyung Mi, Kim Seong Hyun, Gwak Geum-Youn, Jung Sin-Ho
Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Irwon-Dong, Gangnam-gu, Seoul, 135-710, Korea.
Division of Hepatology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 135-710, Republic of Korea.
Eur Radiol. 2016 Oct;26(10):3437-46. doi: 10.1007/s00330-015-4190-5. Epub 2016 Jan 8.
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.
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.
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).
A predictive model based on MRI before RFA could robustly identify HCC patients at high risk for LTP after treatment.
• 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.
开发并验证一种利用磁共振成像（MRI）预测肝细胞癌（HCC）患者射频消融（RFA）后局部肿瘤进展（LTP）的模型。
回顾性分析211例接受RFA作为HCC一线治疗的患者。他们在治疗前接受了钆塞酸二钠增强MRI检查，使用包括肿瘤大小、边缘、T1加权像、T2加权像、扩散加权像及肝胆期图像（HBPI）上的信号强度、瘤内脂肪或肿瘤包膜以及HBPI上的瘤周低信号等参数来开发治疗后LTP的预测模型。基于Cox回归分析构建该区分低风险与高风险LTP组的模型。
我们的分析得出以下模型：“风险评分 = 0.617×肿瘤大小 + 0.965×肿瘤边缘 + 0.867×HBPI上的瘤周低信号”。这能够预测哪些患者在RFA后有LTP的高风险（p < 0.001）。低风险组患者的5年无LTP生存率显著高于高风险组（89.6%对65.1%；风险比，3.60；p < 0.001）。
基于RFA前MRI的预测模型能够可靠地识别治疗后有LTP高风险的HCC患者。
• 肿瘤大小、边缘及HBPI上的瘤周低信号是LTP的危险因素。• 风险评分模型可预测哪些患者有LTP的高风险。• 该预测模型可能有助于HCC患者的风险分层。
