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.101594How to Cite
Abstract
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.