Construction of a Prediction Model for Cerebral Gliomas Grading Based on Radiomics
[Objective]To develop a prediction system for cerebral glioma grading based on ra-diomics.[Methods]The imaging data of 184 patients with cerebral gliomas who underwent MRI examination in Taizhou Cancer Hospital and The Second Affiliated Hospital,Zhejiang University School of Medicine from March 2013 to June 2018 were collected.A total of 107 radiomics fea-tures were extracted from image data using the pyradiomics library in Python.Lasso regression was employed to select the optimal subset of features.The machine learning algorithms Naive Bayes,Support Vector Machine(SVM),K-Nearest Neighbors(KNN),and LightGBM were applied for glioma grading prediction.During this process,synthetic minority over-sampling tech-nique(SMOTE)was utilized to balance the training data,and the performance of different models was evaluated and compared.[Results]Among the models without over-sampling,LightGBM ex-hibited the best performance across various metrics,with an AUC(area under the ROC curve)of 0.64,accuracy of 0.71,Fl score of 0.80,sensitivity of 0.82,and specificity of 0.44.After ap-plying SMOTE over-sampling,the performance of all models improved,LightGBM still achieved the best results,with an AUC of 0.86,accuracy of 0.81,Fl score of 0.81,sensitivity of 0.79,and specificity of 0.84.[Conclusion]Through the application of radiomics feature extraction and multiple machine learning algorithms,this study has successfully developed an efficient system for predicting glioma grades.The LightGBM model demonstrated superior performance in handling data imbalance and glioma grading tasks,showing substantial clinical application value.
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