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Research Paper|Volume 10, Issue 10|pp 2884—2899

Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction

Zenghui Qian1, Yiming Li1, Zhiyan Sun1, Xing Fan1,2, Kaibin Xu6, Kai Wang4, Shaowu Li1,3, Zhong Zhang2, Tao Jiang1,2,5, Xing Liu1, Yinyan Wang2
  • 1Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
  • 2Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  • 3Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  • 4Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  • 5Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China
  • 6Chinese Academy of Sciences, Institute of Automation, Beijing, China

* * Equal contribution

Received: July 9, 2018Accepted: October 12, 2018Published: October 22, 2018
https://doi.org/10.18632/aging.101594

Copyright: © 2018 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.

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.