Department of Hepatobiliary Surgery, The People's Hospital of Qijiang, Chongqing, China.
Department of Hepatopancreatobiliary Surgery, The Affiliated Calmette Hospital of Kunming Medical University, The First People's Hospital of Kunming, Calmette Hospital Kunming, Yunnan Province, China.
Clin Radiol. 2022 Apr;77(4):e269-e279. doi: 10.1016/j.crad.2021.12.008. Epub 2021 Dec 31.
To develop a reliable model to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) by combining a large number of clinical and imaging examinations, especially the radiomic features of magnetic resonance imaging (MRI).
Three hundred and one consecutive patients from two centres were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was used to shrink the feature size, and logistic regression was used to construct a predictive radiomic signature. The ability of the nomogram to discriminate MVI in patients with HCC was evaluated using area under the curve (AUC) of receiver operating characteristics (ROC), accuracy, and calibration curves.
The radiomic signature showed a significant association with MVI (p<0.001 for all data sets). Other useful predictors of MVI included non-smooth tumour margin, internal arteries, and the alpha-fetoprotein (AFP) level. The nomogram demonstrated a strong prognostic capability in the training set and both validation sets, providing AUCs of 0.914 (95% confidence interval [CI] 0.853-0.956), 0.872 (95% CI: 0.757-0.946), and 0.881 (95% CI: 0.806-0.934), respectively.
The preoperative radiomic nomogram, incorporating clinical risk factors and a radiomic signature, could predict MVI in patients with HCC. The MRI-based radiomic-clinical model predicted the MVI of HCC effectively and was more efficient compared with the radiomic model or clinical model alone.
通过结合大量临床和影像学检查，尤其是磁共振成像（MRI）的放射组学特征，开发一种可靠的模型来预测肝细胞癌（HCC）患者的微血管侵犯（MVI）。
本研究纳入了来自两个中心的 311 例连续患者。最小绝对收缩和选择算子（LASSO）回归用于缩小特征大小，逻辑回归用于构建预测放射组学特征。通过接受者操作特征（ROC）曲线下面积（AUC）、准确性和校准曲线评估列线图区分 HCC 患者 MVI 的能力。
放射组学特征与 MVI 显著相关（所有数据集均为 p<0.001）。MVI 的其他有用预测因子包括肿瘤边缘不光滑、内部动脉和甲胎蛋白（AFP）水平。该列线图在训练集和两个验证集中均表现出较强的预后能力，AUC 分别为 0.914（95%置信区间[CI]：0.853-0.956）、0.872（95%CI：0.757-0.946）和 0.881（95%CI：0.806-0.934）。
术前放射组学列线图，纳入临床危险因素和放射组学特征，可预测 HCC 患者的 MVI。与单独的放射组学模型或临床模型相比，基于 MRI 的放射组学-临床模型更有效地预测了 HCC 的 MVI。
