A toolbox for data processing in Radiomics analysis

In the current study, we aimed to develop an easy-to-use toolbox for data processing in Radiomics analysis. The toolbox was design to conduct data processing of classification (eg. classifying benign and malignant tumor) and prognosis (eg. prediction of 3 years survival rate) analysis in Radiomics researches. The toolbox was composed of data preprocessing, feature extraction, feature selection, Radiomics signature construction, clinical variables selection, combined model construction and performance evaluation. The Radiomics signature was obtained with the procedure of data preprocessing, features extraction, features selection, and signature construction. The valuable clinical variables were selected by Akaike information criterion (AIC) or Bayesian Information Criterion (BIC). The combined model was constructed by integrating Radiomics signature with the selected clinical variables. The toolbox provided an evaluation part to assess the performance of the combined model. For the classification analysis, the toolbox provided the classification evaluation metrics including area under receiver operating characteristic curve (AUC), classification accuracy (ACC), true and false positive rate (TPR and FPR), positive and negative predictive values (PPV and NPV), together with other evaluation approaches including receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA). For the overall survival analysis, the toolbox provided the evaluation approach of C-index and K-M (Kaplan -Meier) curve. For the survival analysis of certain time point, the toolbox provided the evaluation metrics including time dependent-area under receiver operating characteristic curve (TD-AUC), time dependent-classification accuracy (TD-ACC), together with other evaluation approaches including time dependent-receiver operating characteristic curve (TD-ROC), calibration curve and DCA.