An MRI-based radiomics signatures for overall survival prediction of glioma patients

Viet Huan Le, Tran Nguyen Tuan Minh, Quang Hien Kha, Nguyen Quoc Khanh Le

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationMedical Imaging 2023
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKhan M. Iftekharuddin, Weijie Chen
ISBN (Electronic)9781510660359
Publication statusPublished - 2023
EventMedical Imaging 2023: Computer-Aided Diagnosis - San Diego, United States
Duration: Feb 19 2023Feb 23 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2023: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego


  • glioblastoma
  • gliomas
  • low-grades gliomas
  • prediction model
  • radiomics features
  • radiomics model
  • radiomics signature
  • survival analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'An MRI-based radiomics signatures for overall survival prediction of glioma patients'. Together they form a unique fingerprint.

Cite this