Online citations, reference lists, and bibliographies.
Please confirm you are human
(Sign Up for free to never see this)
← Back to Search

Improving Survival Prediction Of High-grade Glioma Via Machine Learning Techniques Based On MRI Radiomic, Genetic And Clinical Risk Factors.

Y. Tan, W. Mu, Xiaochun Wang, G. Yang, R. Gillies, H. Zhang
Published 2019 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy
OBJECTIVES To develop a radiomic signature to predict overall survival (OS) for high-grade glioma (HGG), and construct a nomogram by combining selected radiomic, genetic and clinical risk factors to further improve the performance of the risk model. MATERIALS AND METHODS 147 cases of HGG with MRI images, genetic data, clinical data were studied, wherein 112 patients were used as training cohort, and 35 patients were as independent test cohort. Radiomics features were extracted from tumor area and peritumoral edema area on CE-T1WI and T2FLAIR images. Association between radiomics signature, genetic, clinical risk factors and OS was explored by Kaplan-Meier survival analysis and log rank test. The multivariate Cox regression analysis was trained with radiomic features along with selected genetic and clinical risk factors, which was presented as a nomogram. RESULTS The radiomic signature constructed by 11 radiomics features stratified patients into low- and high-risk groups, and the C-Index for OS prediction was 0.707 and 0.711 in training and test cohorts, respectively. The multivariable Cox regression analysis identified radiomics signature (hazard ratio (HR): 2.18, P =  0.005), IDH (HR: 0.490, P =  0.007) and age (HR: 1.039, P =  0.005) as independent risk factors. A nomogram combining these independent risk factors further improved the performance for OS estimation (C-index = 0.764 and 0.758 in training and test cohorts, respectively). CONCLUSION The radiomics signature is a new prognostic biomarker for HGG. A nomogram incorporating radiomics signature, IDH and age improved the performance of OS estimation, which might be a new complement to the treatment guidelines of glioma.
This paper references
Which glioblastoma multiforme patient will become a long‐term survivor? A population‐based study
J. Scott (1999)
An independently validated nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825
H. Gittleman (2017)
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.
A. Zwanenburg (2020)
Clinical and immunological correlates of long term survival in glioblastoma
B. Czapski (2018)
A review on radiomics and the future of theranostics for patient selection in precision medicine.
Simon A Keek (2018)
Radiomics: Images Are More than Pictures, They Are Data
R. Gillies (2016)
Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial.
R. Stupp (2009)
Neurosurgical outcomes in a modern series of 400 craniotomies for treatment of parenchymal tumors.
R. Sawaya (1998)
The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis
Y. Liu (2018)
A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme
Qihua Li (2017)
IDH mutations in cancer and progress toward development of targeted therapeutics.
L. Dang (2016)
Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients
J. Peeken (2018)
Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1
S. Rathore (2018)
Factors influencing survival in high-grade gliomas.
J. Buckner (2003)
SU-E-J-261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features
L. Zhang (2015)
Diffusely infiltrating astrocytomas: pathology, molecular mechanisms and markers
K. Ichimura (2015)
MRI features predict survival and molecular markers in diffuse lower-grade gliomas
H. Zhou (2017)
Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer.
W. Liang (2015)
Prognostic Factors for Survival Outcome of High-Grade Multicentric Glioma.
Tianwei Wang (2018)
Accuracy of a nomogram to predict the survival benefit of surgical axillary staging in T1 breast cancer patients
Yuxia Chen (2018)
Surgical oncology for gliomas: the state of the art
N. Sanai (2018)
SU-E-J-258: Prediction of Cervical Cancer Treatment Response Using Radiomics Features Based On F18-FDG Uptake in PET Images
B. Altazi (2015)
Response: Re: Brain and Other Central Nervous System Cancers: Recent Trends in Incidence and Mortality
J. M. Legler (1999)
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.
Yanqi Huang (2016)
Interaction Between the Contributions of Tumor Location, Tumor Grade, and Patient Age to the Survival Benefit Associated with Gross Total Resection.
Kate T. Carroll (2018)
Novel, improved grading system(s) for IDH-mutant astrocytic gliomas
Mitsuaki Shirahata (2018)
Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models.
P. Kickingereder (2016)
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement
Gary S. Collins (2015)
Exciting New Advances in Neuro‐Oncology: The Avenue to a Cure for Malignant Glioma
E. V. Van Meir (2010)
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
D. N. Louis (2016)
Prediction of survival with multi-scale radiomic analysis in glioblastoma patients
A. Chaddad (2018)
Prognostic factors and outcomes in anaplastic gliomas: An institutional experience
D. Valiyaveettil (2018)
A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme
Jiangwei Lao (2017)

This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar