Mao Bing, Ren Yajun, Yu Xuan, Liang Xinliang, Ding Xiangming
Henan Provincial People's Hospital, Zhengzhou University People's Hospital; Henan University People's Hospital, Zhengzhou, Henan, China.
Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Oncol. 2024 Mar 15;14:1346124. doi: 10.3389/fonc.2024.1346124. eCollection 2024.
To develop a contrast-enhanced computed tomography (CECT) based radiomics model using machine learning method and assess its ability of preoperative prediction for the early recurrence of hepatocellular carcinoma (HCC).
A total of 297 patients confirmed with HCC were assigned to the training dataset and test dataset based on the 8:2 ratio, and the follow-up period of the patients was from May 2012 to July 2017. The lesion sites were manually segmented using ITK-SNAP, and the pyradiomics platform was applied to extract radiomic features. We established the machine learning model to predict the early recurrence of HCC. The accuracy, AUC, standard deviation, specificity, and sensitivity were applied to evaluate the model performance.
1,688 features were extracted from the arterial phase and venous phase images, respectively. When arterial phase and venous phase images were employed correlated with clinical factors to train a prediction model, it achieved the best performance (AUC with 95% CI 0.8300(0.7560-0.9040), sensitivity 89.45%, specificity 79.07%, accuracy 82.67%, p value 0.0064).
The CECT-based radiomics may be helpful to non-invasively reveal the potential connection between CECT images and early recurrence of HCC. The combination of radiomics and clinical factors could boost model performance.
利用机器学习方法建立基于对比增强计算机断层扫描（CECT）的放射组学模型，并评估其对肝细胞癌（HCC）早期复发的术前预测能力。
将297例确诊为HCC的患者按照8:2的比例分配到训练数据集和测试数据集，患者的随访期为2012年5月至2017年7月。使用ITK-SNAP手动分割病变部位，并应用pyradiomics平台提取放射组学特征。我们建立了机器学习模型来预测HCC的早期复发。应用准确率、AUC、标准差、特异性和敏感性来评估模型性能。
分别从动脉期和静脉期图像中提取了1688个特征。当将动脉期和静脉期图像与临床因素相关联以训练预测模型时，其性能最佳（AUC 95%CI为0.8300(0.7560 - 0.9040)，敏感性89.45%，特异性79.07%，准确率82.67%，p值0.0064）。
基于CECT的放射组学可能有助于无创地揭示CECT图像与HCC早期复发之间的潜在联系。放射组学与临床因素的结合可以提高模型性能。
