Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University/Lishui Hospital of Zhejiang University, Lishui 323000, China; Department of Radiology, Lishui Central Hospital of Zhejiang Province, Lishui 323000, China.
Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University/Lishui Hospital of Zhejiang University, Lishui 323000, China.
Eur J Radiol. 2021 Nov;144:109955. doi: 10.1016/j.ejrad.2021.109955. Epub 2021 Sep 17.
To construct a precise prediction model of preoperative magnetic resonance imaging (MRI)-based nomogram for aggressive intrasegmental recurrence (AIR) of hepatocellular carcinoma (HCC) patients treated with radiofrequency ablation (RFA).
Among 891 patients with HCC treated by RFA, 22 patients with AIR and 36 patients without AIR (non-AIR) were finally enrolled in our study, and each patient was followed up for more than 6 months to determine the occurrence of AIR. The laboratory indicators and MRI features were compared and assessed. Preoperative contrast-enhanced T1-weighted images (CE-T1WI) were used for radiomics analysis. The selected clinical indicators and texture features were finally screened out to generate the novel prediction nomogram.
Tumor shape, ADC Value, DWI signal intensity and ΔSI were selected as the independent factors of AIR by univariate and multivariate logistic regression analysis. Meanwhile, two radiomics features were selected from 396 candidate features by LASSO (P < 0.05), which were further used to calculate the Rad-score. The selected clinical factors were further integrated with the Rad-score to construct the predictive model, and the AUCs were 0.941 (95% CI: 0.876-1.000) and 0.818 (95% CI: 0.576-1.000) in the training (15 AIR and 25 non-AIR) and validation cohorts (7 AIR and 11 non-AIR), respectively. The AIR predictive model was further converted into a novel radiomics nomogram, and decision curve analysis showed good agreement.
The predictive nomogram integrated with clinical factors and CE-T1WI -based radiomics signature could accurately predict the occurrence of AIR after RFA, which could greatly help individualized evaluation before treatment.
构建基于磁共振成像（MRI）术前列线图的预测模型，用于预测接受射频消融（RFA）治疗的肝细胞癌（HCC）患者的侵袭性节段内复发（AIR）。
在 891 例接受 RFA 治疗的 HCC 患者中，最终纳入 22 例发生 AIR 和 36 例未发生 AIR（非-AIR）的患者，每位患者的随访时间均超过 6 个月，以确定 AIR 的发生情况。比较和评估实验室指标和 MRI 特征。使用术前对比增强 T1 加权成像（CE-T1WI）进行放射组学分析。最终筛选出选定的临床指标和纹理特征，以生成新的预测列线图。
单因素和多因素逻辑回归分析选择肿瘤形状、ADC 值、DWI 信号强度和ΔSI 作为 AIR 的独立因素。同时，LASSO 从 396 个候选特征中选择了两个放射组学特征（P<0.05），并进一步计算 Rad-score。选择的临床因素进一步与 Rad-score 整合以构建预测模型，在训练组（15 例 AIR 和 25 例非-AIR）和验证组（7 例 AIR 和 11 例非-AIR）中，AUC 值分别为 0.941（95%CI：0.876-1.000）和 0.818（95%CI：0.576-1.000）。进一步将 AIR 预测模型转换为新的放射组学列线图，决策曲线分析显示出良好的一致性。
结合临床因素和基于 CE-T1WI 的放射组学特征的预测列线图可以准确预测 RFA 后 AIR 的发生，这可以极大地帮助治疗前的个体化评估。
