Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation

被引:13
作者
Zhang, Xiaojuan [1 ,2 ]
Wang, Chuandong [3 ,4 ]
Zheng, Dan [2 ,5 ]
Liao, Yuting [6 ]
Wang, Xiaoyang [5 ]
Huang, Zhifeng [5 ]
Zhong, Qun [7 ]
机构
[1] Fujian Med Univ, Xiamen Humanity Hosp, Dept Radiol, Xiamen, Peoples R China
[2] Fujian Med Univ, Fuzong Clin Med Coll, Fuzhou, Peoples R China
[3] Fujian Med Univ, Xiamen Humanity Hosp, Dept Thyroid & Breast Surg, Xiamen, Peoples R China
[4] Fujian Med Univ, Shengli Clin Med Coll, Fuzhou, Peoples R China
[5] 900th Hosp Joint Logist Support Force, Dept Radiol, Fuzhou, Peoples R China
[6] GE Healthcare, Inst Precis Med, Shanghai, Peoples R China
[7] Fujian Univ Tradit Chinese Med, Affiliated Peoples Hosp, Dept Radiol, Fuzhou, Peoples R China
关键词
small hepatocellular carcinoma; radiofrequency ablation; early recurrence; magnetic resonance imaging; radiomics; nomogram; RISK-FACTORS; RESECTION; SURVIVAL; IMAGES; TRIAL;
D O I
10.3389/fonc.2022.1013770
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
BackgroundThere are few studies on the application of radiomics in the risk prediction of early recurrence (ER) after radiofrequency ablation (RFA). This study evaluated the value of a multi-parametric magnetic resonance imaging (MRI, mpMRI)-based radiomics nomogram in predicting ER of small hepatocellular carcinoma (HCC) after RFA. Materials and methodsA retrospective analysis was performed on 90 patients with small HCC who were treated with RFA. Patients were divided into two groups according to recurrence within 2 years: the ER group (n=38) and the non-ER group (n=52). Preoperative T1WI, T2WI, and contrast-enhanced MRI (CE-MRI) were used for radiomic analysis. Tumor segmentation was performed on the images and applied to extract 1316 radiomics features. The most predictive features were selected using analysis of variance + Mann-Whitney, Spearman's rank correlation test, random forest (importance), and least absolute shrinkage and selection operator analysis. Radiomics models based on each sequence or combined sequences were established using logistic regression analysis. A predictive nomogram was constructed based on the radiomics score (rad-score) and clinical predictors. The predictive efficiency of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to evaluate the clinical efficacy of the nomogram. ResultsThe radiomics model mpMRI, which is based on T1WI, T2WI, and CE-MRI sequences, showed the best predictive performance, with an AUC of 0.812 for the validation cohort. Combined with the clinical risk factors of albumin level, number of tumors, and rad-score of mpMRI, the AUC of the preoperative predictive nomogram in the training and validation cohorts were 0.869 and 0.812, respectively. DCA demonstrated that the combined nomogram is clinically useful. ConclusionsThe multi-parametric MRI-based radiomics nomogram has a high predictive value for ER of small HCC after RFA, which could be helpful for personalized risk stratification and further treatment decision-making for patients with small HCC.
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页数:13
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