摘要
本文基于影像组学预测胰腺囊性肿瘤(PCN)的Ki67分子标记物表达情况。首先手动分割患者术前多排螺旋断层扫描(MDCT)图像中的肿瘤区域,然后根据肿瘤特点设计并提取409个高通量特征,再利用最小化的绝对收缩与选择算子(LASSO)回归模型进行多因素分析筛选特征,最后将筛选后的特征输入支持向量机(SVM)实现分类判别。通过重复200次LASSO筛选,记录每次被选择的特征,并将特征按照被选择的次数从高到低排序。使用十折交叉验证的SVM,测试不同的特征数量下的分类效果,重复200次并将结果取平均值以降低误差。实验结果表明,被选择次数最多的前20个特征构成最优特征子集,预测的AUC达到91.54%,准确率达到85.29%,敏感度为81.88%,特异性为86.75%。实验结果证明了通过影像组学方法预测Ki67分子标记物的可行性。
This study aims to predict expression of Ki67 molecular marker in pancreatic cystic neoplasm using radiomics.We firstly manually segmented tumor area in multi-detector computed tomography(MDCT) images.Then409 high-throughput features were automatically extracted and the least absolute shrinkage selection operator(LASSO)regression model was used for feature selection.After 200 bootstrapping repetitions of LASSO,20 most frequently selected features made up the optimal feature set.Then 200 bootstrapping repetitions of support vector machine(SVM) classifier with 10-fold cross-validation were used to avoid overfitting and accurately predict the Ki67 expression.The highest prediction accuracy could achieve 85.29% and the highest area under the receiver operating characteristic curve(AUC)was 91.54% with a sensitivity(SENS) of 81.88% and a specificity(SPEC) of 86.75%.According to the results of experiment,the feasibility of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics was verified.
引文
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