TY - GEN
T1 - An MRI-based radiomics signatures for overall survival prediction of glioma patients
AU - Le, Viet Huan
AU - Minh, Tran Nguyen Tuan
AU - Kha, Quang Hien
AU - Le, Nguyen Quoc Khanh
N1 - Publisher Copyright:
© 2023 SPIE.
PY - 2023
Y1 - 2023
N2 - 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 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, 0.794_0.995), and 0.618 (95% CI, 0.526_0.710), and those of radiomics models were 0.780 (95%CI, 0.615_0.945), 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.
AB - 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 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, 0.794_0.995), and 0.618 (95% CI, 0.526_0.710), and those of radiomics models were 0.780 (95%CI, 0.615_0.945), 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.
KW - glioblastoma
KW - gliomas
KW - low-grades gliomas
KW - prediction model
KW - radiomics features
KW - radiomics model
KW - radiomics signature
KW - survival analysis
UR - http://www.scopus.com/inward/record.url?scp=85160200206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85160200206&partnerID=8YFLogxK
U2 - 10.1117/12.2653413
DO - 10.1117/12.2653413
M3 - Conference contribution
AN - SCOPUS:85160200206
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2023
A2 - Iftekharuddin, Khan M.
A2 - Chen, Weijie
PB - SPIE
T2 - Medical Imaging 2023: Computer-Aided Diagnosis
Y2 - 19 February 2023 through 23 February 2023
ER -