基于T1W1+C的影像建立支持向量机预测模型对胶质瘤细胞增殖活性研究应用
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南京医科大学附属淮安第一医院

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江苏省卫生健康科研项目ZD2021051


Based on the enhancement T1-weighted image to build a support vector machine predication model for the proliferation activity of glioma cells
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南京医科大学附属淮安第一医院

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the Key medical research projects of Jiangsu Provincial Health Commission foundation,china (No.ZD 2021051).

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    摘要:

    目的:基于胶质瘤增强T1加权成像(Enhancement T1-Weighted Image,T1WI+C)影像组学特征进行机器学习,构建预测胶质瘤细胞增殖活性的ki67指数预测模型。方法:回顾性分析本院手术病理结果为胶质瘤并经免疫组化测定ki67指数的患者113例,随机按约8:2拆分为训练集与测试集。通过MRIcroGL软件手绘胶质瘤感兴趣区域(Region Of Interest, ROI),利用python中 pyradiomics模块获得1338个影像特征,通过T检验与最小绝对收缩和选择算子(Least absolute shrinkage and selection operator,Lasso)回归算法筛选最佳影像组学特征,基于最佳特征使用支持向量机(Support Vector Machine,SVM)分类器建立ki67预测模型,利用受试者操作特征曲线(Receiver Operating Characteristic Curve,ROC曲线)与模型校准曲线评估模型的预测效能。结果:每例患者T1WI+C图像提取1338个影像组学特征,降维后筛选出 6个与胶质瘤ki67指数密切相关特征。基于支持向量机算法模型在训练集ki67指数预测中AUC为0.82、准确率0.74;在测试集中ki67指数预测中AUC为0.91、准确率为0.83。模型校准曲线结果显示布尔里得分为0.175。结论:基于T1WI+C的影像建立支持向量机预测模型对胶质瘤细胞增殖活性可能具有较好的预测效能,有助于患者个体化诊疗和精准化医疗未来的发展。

    Abstract:

    Objective:This machine learning was performed based on the radiomic features of contrast-enhanced T1-weighted imaging (T1WI+C) of glioma to construct a prediction model of ki67 index for predicting the proliferation activity of glioma cells.Methods:A retrospective analysis of 113 patients with glioma confirmed by the surgical and pathological results of our hospital and the ki67 index was defined by immunohistochemistry. The training set and the test set were divided into 8:2. The glial region of interest (ROI) was hand-painted by MRIcroGL software, 1338 image features were obtained by using the pyradiomics module in python, and the best image features were obtained through T-test and the least absolute shrinkage and selection operator (lasso) regression algorithm. Furthermore, using the support vector machine classifier to build the ki67 prediction model based on the best features, using the receiver operating characteristic (ROC) curve and the calibration curve to evaluate the predictive performance of the model.Results:1338 radiomics features were extracted from T1WI+C images of each patient, and 6 features closely related to glioma ki67 index were screened out after dimensionality reduction. Based on the support vector machine algorithm model, the AUC in the training set ki67 index prediction was 0.82, and the accuracy rate was 0.74; in the test set ki67 index prediction, the AUC was 0.91, and the accuracy rate was 0.83. The results of the calibration curve of the model showed that the difference was not statistically significant (Brier Score=0.175).Conclusion: The prediction model of support vector machine established on the learning of the T1W1+C imaging omics characteristics may have a better predictive effert on the proliferation activity ki67 index of glioma cells,and it will help the selection of individual diagnosis and treatment plans for patients and the development of precision medical care in the future.

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  • 收稿日期:2022-06-12
  • 最后修改日期:2022-09-02
  • 录用日期:2023-05-10
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