术前增强CT影像组学结合临床指标预测肝癌术后早期复发

The enhanced preoperative tomography imaging combined with clinical indicators to predict early postoperative recurrence of hepatocellular carcinoma

目的探讨术前增强计算机断层扫描(CECT)的影像组学特征与临床指标在预测肝细胞肝癌(HCC)术后早期复发的价值。方法选取2018年1月至2021年12月本院收治的肝细胞肝癌(HCC)患者74例,随机分配到训练数据集(n=59)和测试数据集(n=15)。引入临床因素构建Logistic回归模型,结合影像组学评分建立列线图,评估模型性能。结果临床模型的训练集AUC为0.745(95%CI:0.646~0.844)。放射组学模型AUC为0.805(95%CI:0.714~0.897),结合影像特征与临床指标训练机器学习模型时,模型性能显著提高为0.846(95%CI:0.763~0.929)。结论放射组学特征可无创探索CECT图像与HCC早期复发间的潜在关联。

Objective To discuss the value of imaging characteristics and clinical indicators of enhanced preoperative tomography(CECT)for predicting early postoperative recurrence of hepatocellular carcinoma(HCC).Methods Imaging and clinical data collected from 74 HCC subjects were randomly assigned to a training dataset(n=59)and a test dataset(n=15).ITK-SNAP was used to map the lesion manually.Pyradiomics was used for image feature extraction.The smallest absolute shrinkage and selection operator(LASSO)were used for dimension reduction.Finally,the recursive feature elimination(RFE)method was used to synthesize the characteristics of the omics image.The early recurrence prediction model of HCC was established by using Logistic regression(LR).The Logistic regression model was constructed by introducing clinical factors,and the nomogram was established by combining with the image omics score.Model performance was assessed using the 95%confidence interval(CI),AUC,SD,sensitivity,specificity and accuracy.Results The AUC of the clinical model on the training set was 0.745(95%CI:0.646~0.844).The AUC of the radiomic model was 0.805(95%CI:0.714~0.897),and the model performance was significantly improved to 0.846(95%CI:0.763~0.929)when the machine learning model was trained with image features and clinical indicators.Conclusion The model based on CT imaging combined with clinical characteristics can be used to predict early HCC recurrence.