Research Paper Volume 10, Issue 10 pp 2884—2899
Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction
- 1 Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- 2 Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- 3 Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- 4 Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- 5 Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China
- 6 Chinese Academy of Sciences, Institute of Automation, Beijing, China
received: July 9, 2018 ; accepted: October 12, 2018 ; published: October 22, 2018 ;https://doi.org/10.18632/aging.101594
How to Cite
Copyright: Qian et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Objective: We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation. Methods: We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: n = 85; validation cohort: n = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature. Gene ontology analysis of highly expressed genes in the high-risk score group was conducted to establish a radiogenomic map. A nomogram was constructed for individualized survival prediction.
Results: The six-feature radiomic signature stratified patients in the training cohort into low- or high-risk groups for overall survival (P = 0.0018). This result was successfully verified in the validation cohort (P = 0.0396). Radiogenomic analysis revealed that the prognostic radiomic signature was associated with hypoxia, angiogenesis, apoptosis, and cell proliferation. The nomogram resulted in high prognostic accuracy (C-index: 0.92, C-index: 0.70) and favorable calibration for individualized survival prediction in the training and validation cohorts.
Conclusions: Our results suggest a great potential for the use of radiomic signature as a biological surrogate in providing prognostic information for patients with LGGs.