Proceedings Article | 7 April 2023
KEYWORDS: Data modeling, Education and training, Medical research
Lower-grade gliomas (LGG) can eventually progress to glioblastoma (GBM) and death. This study aimed to construct the radiomics model for predicting survival outcomes in glioma patients, compared the radiomics model with clinical and gene status models, and evaluated the combined models. Two direction approach was used: build models on GBM patients, and then verified in LGG patients and vice versa The Cancer Genome Atlas (TCGA) data set includes 102 patients in TCGA-GBM collection, and 107 patients in TCGA-LGG. The GBM data were divided randomly into seventy percent and thirty percent for training and testing. The LGG data set was used to validate. From the initial 704 MRI-based radiomics features of training set, we chose 17 optimal MRI-based radiomics signatures after consecutive selection steps to build the radiomics score of each patient as a representative of radiomics model. The iAUCs of combined models in training, testing, and validation sets were respectively 0.804 (95% CI, 0.741-0.866), 0.878 (95% CI, 0.802-0.955), and 0.802 (95% CI, 0.669-0.935), and those of radiomics models were 0.798 (95%CI, 0.743-0.852), 0.867 (95% CI, 0.736-0.999), and 0.717 (95% CI, 0.549-0.884). Applied the same consecutive selection steps, we chose 8 MRI-based radiomics signatures from LGG training set. The iAUCs of combined models in training, testing, and validation sets were respectively 0.842 (95% CI, 0.697_0.988), 0.894 (95% CI, 0.615_0.945), and 0.618 (95% CI, 0.526_ 0.710), and those of radiomics models were 0.780 (95%CI, 0.674_ 0.949), 0.832 (95% CI, 0.738_0.925), and 0.525 (95% CI, 0.506_0.545). In conclusion, the radiomics model can independently predict the overall survival of glioma patients, and the combined model integrating radiomics, clinical, and gene status models improved this ability.
