Construction and evaluation of different machine learning models based on MRI combined with clinical indicators for predicting early recurrence of patients with hepatocellular carcinoma after radiofrequency ablation
10.3760/cma.j.cn113884-20231231-00187
- VernacularTitle:不同机器学习方法构建MRI影像组学联合临床指标预测肝细胞癌患者射频消融术后早期复发模型与评估
- Author:
Wenhua LI
1
;
Jing TANG
;
Nanjun WANG
;
Xueping LI
;
Xiao WANG
;
Tianran LI
Author Information
1. 中国人民解放军总医院第四医学中心放射诊断科，北京 100048
- Keywords:
Carcinoma, hepatocellular;
Radiofrequency ablatio;
Recurrence;
Magnetic resonance imaging;
Radiomics
- From:
Chinese Journal of Hepatobiliary Surgery
2024;30(5):347-353
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a model for predicting early recurrence of hepatocellular carcinoma (HCC) patients after radiofrequency ablation by different machine learning models based on multimodal MRI and clinical indicators, and to evaluate the predictive efficacy of the model.Methods:The data of patients with HCC who underwent radiofrequency ablation in Fourth Medical Center of Chinese PLA General Hospital and the First Medical Center of Chinese PLA General Hospital from January 2015 to December 2021 were retrospectively analyzed. A total of 169 patients with HCC were enrolled, including 152 males and 17 females, aged (57.2±9.2) years. The training set ( n=135) and the test set ( n=34) were randomly divided according to 8∶2. There were 49 cases recurrence in training set and 12 cases recurrence in test set. Based on the training set, the clinical influencing factors of early recurrence in patients with HCC after radiofrequency ablation were screened by univariated and multivariate logistic analysis, and the imaging features were sequentially screened by variance threshold method, select K-best and LASSO regression. Support vector machine (SVM), logistic regression and random forest (RFOREST) were used to construct the prediction models of early postoperative recurrence with simple imagomics alone or combined clinical features, respectively, and the receiver operating characteristic (ROC) curve was used to evaluate the prediction efficiency of the models. Results:Multivariate logistic regression analysis showed that preoperative alpha-fetoprotein >20 μg/L, platelet count >140×10 9 and tumor location were the influential factors for early recurrence of HCC patients after radiofrequency ablation (all P<0.05). Through variance threshold analysis, select K-best and LASSO regression, 16 optimal image omics features were selected. SVM, logistic regression and RFOREST were used to construct a simple imaging omics model for predicting early recurrence of HCC patients after radiofrequency ablation. The areas under ROC curve of the test set were 0.826, 0.830 and 0.826, respectively. And the areas under ROC curve of the constructed imagomics combined clinical model of test set were 0.830, 0.830 and 0.909, respectively. The area under ROC curve of RFOREST in the test set was better than that of SVM and logistic regression ( Z=2.19, 3.98, P=0.008, 0.008). Conclusion:The combined model constructed by SVM, logistic regression and RFOREST based on clinical indicators and image omics features is effective in predicting the early recurrence of patients with HCC after radiofrequency ablation, and the model constructed by RFOREST is the best.
