北京生物醫(yī)學工程

基于CT影像組學的結直腸癌肝轉移與原發(fā)性肝癌病灶分類研究

Classification of colorectal liver metastases and hepatocellular carcinoma lesions based on CT-radiomics

作者：王雪虎郭海峰殷小平王云

單位：河北大學電子信息工程學院(河北保定071002) 河北大學附屬醫(yī)院(河北保定071002) 通信作者：王雪虎。E-mail: wangxuehu_tougao@ 163. com

分類號：R318. 04

出版年·卷·期（頁碼）：2021·40·6（551-556）

摘要：

目的探索結直腸癌肝轉移(colorectal liver metastases, CRLM )與原發(fā)性肝癌(hepatocellular carcinoma,HCC)影像組學特征的差異，以實現(xiàn)對CRLM的精準識別。方法納入河北大學附屬醫(yī)院102 例經病例證實的CRLM和HCC患者術前CT增強影像,將其以7 : 3的比例隨機分配到訓練集和測試集。首先，采用基于Python的Pyradiomics包從肝臟病灶中提取影像組學特征;然后，利用最小絕對收縮和選擇算子(least absolute shrinkage and selection operator, LASSO )和遞歸消除( recursive feature elimination, RFE)方法選擇出最優(yōu)特征集合;再應用支持向量機(support vector machine, SVM)、K-近鄰 (k-nearest neighbor, KNN)和隨機森林(random forest, RF)、邏輯回歸(logistic regression, LR) 4 種分類器算法訓練模型，以受試者工作特征曲線下面積(the area under the receiver operating characteristic curve, AUG)、準確率、敏感度和特異度來評估4種分類器的性能。結果應用SVM分類器算法訓練的模型對 CRLM識別效能較高(準確率為93%,特異度為88%,靈敏度為100%,AUC值為0.94)。結論本文應用 CT影像組學方法提取病灶異質性特征，并通過特征選擇找到訓練模型效果最佳的特征集合，應用SVM 分類器算法訓練的模型能夠比較準確地識別出CRLM病灶,對醫(yī)學診斷具有良好的應用價值。

Objective To explore the difference in radiomics characteristics between colorectal liver metastasis (CRLM) and hepatocellular carcinoma ( HCC ) , so as to realize accurate recognition of CRLM. Methods One hundred and two preoperative CT-enhanced images of CRLM and HCC patients from the Affiliated Hospital of Hebei University are included, and they are randomly assigned to the training set and the test set at a ratio of 7 ： 3. First, we use the Python-based Pyradiomics package to extract radiomics features from liver lesions. Second, we use the least absolute contraction and selection operator ( LASSO) and recursive elimination ( RFE) methods to select the optimal feature set. Then we apply the support vector machine (SVM) , K-nearest neighbor ( KNN) ,random forest ( RF) ,logistic regression ( LR) four classifier algorithms to train models.This article uses the area under the receiver operating characteristic curve ( AUG ) , accuracy, sensitivity and specificity to evaluate the performance of the four classifiers. Results The model trained with the SVM classifier algorithm has a high recognition performance for CRLM ( accuracy rate of 93%, specificity of 88% , sensitivity of 100%,and AUC value of 0.94) . Conclusions In this paper,CT-radiomics method is used to extract the heterogeneous features of the lesion, and the feature set with the best training model effect is found through feature selection. The model trained by the SVM classifier algorithm can identify CRLM lesions more accurately, which is useful for medical diagnosis.

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