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

研究成果: 書貢獻/報告類型會議貢獻

摘要

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.
原文英語
主出版物標題Medical Imaging 2023
主出版物子標題Computer-Aided Diagnosis
編輯Khan M. Iftekharuddin, Weijie Chen
發行者SPIE
ISBN(電子)9781510660359
DOIs
出版狀態已發佈 - 2023
事件Medical Imaging 2023: Computer-Aided Diagnosis - San Diego, 美国
持續時間: 2月 19 20232月 23 2023

出版系列

名字Progress in Biomedical Optics and Imaging - Proceedings of SPIE
12465
ISSN(列印)1605-7422

會議

會議Medical Imaging 2023: Computer-Aided Diagnosis
國家/地區美国
城市San Diego
期間2/19/232/23/23

ASJC Scopus subject areas

  • 電子、光磁材料
  • 原子與分子物理與光學
  • 生物材料
  • 放射學、核子醫學和影像學

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