Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China.
Med Phys. 2024 Jul;51(7):4673-4686. doi: 10.1002/mp.17087. Epub 2024 Apr 20.
Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients.
Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC).
The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC).
Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance.
This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis.
肝癌术前微血管侵犯（MVI）是降低肝癌复发率的有效方法。通过根除微转移，扩大肝切除术联合辅助或靶向治疗可显著提高 MVI+患者的生存率。术前预测 MVI 状态对手术决策和选择其他辅助治疗策略具有重要的临床意义，可改善患者的预后。
建立基于多模态 MRI 和临床数据的放射组学机器学习模型，并分析该模型对肝细胞癌（HCC）微血管侵犯（MVI）的术前预测价值。
回顾性分析 130 例经病理证实的 HCC 患者术前肝脏 MRI 数据和临床资料，根据术后病理将患者分为 MVI 阳性组（MVI+）和 MVI 阴性组（MVI-）。经过一系列降维分析，最终选择了 6 个放射组学特征。然后，采用线性支持向量机（linear SVM）、径向基核函数支持向量机（rbf-SVM）、逻辑回归（LR）、随机森林（RF）和 XGBoost（XGB）算法建立术前 HCC 患者 MVI 预测模型。然后，选择预测性能最佳的 rbf-SVM 构建放射组学评分（R-score）。最后，将 R-score 与临床病理影像独立预测因子相结合，建立联合列线图模型和相应的个体模型。通过受试者工作特征曲线（ROC）评估和比较个体模型和联合列线图模型的预测性能。
甲胎蛋白浓度、瘤周强化、最大肿瘤直径、肿瘤边缘光滑、肿瘤生长方式、肿瘤内出血和 RVI 是 MVI 的独立预测因子。与个体模型相比，最终构建的由放射组学评分（R-score）联合临床病理参数和表观影像特征的联合列线图模型（AUC：0.968，95%CI：0.920-1.000）具有最佳预测性能。
该多参数联合列线图模型在预测 HCC 的 MVI 方面具有良好的性能，对手术方案的制定和预后评估具有一定的辅助价值。
