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Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction
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-weigh...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6224242/ https://www.ncbi.nlm.nih.gov/pubmed/30362964 http://dx.doi.org/10.18632/aging.101594 |
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author | Qian, Zenghui Li, Yiming Sun, Zhiyan Fan, Xing Xu, Kaibin Wang, Kai Li, Shaowu Zhang, Zhong Jiang, Tao Liu, Xing Wang, Yinyan |
author_facet | Qian, Zenghui Li, Yiming Sun, Zhiyan Fan, Xing Xu, Kaibin Wang, Kai Li, Shaowu Zhang, Zhong Jiang, Tao Liu, Xing Wang, Yinyan |
author_sort | Qian, Zenghui |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6224242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Impact Journals |
record_format | MEDLINE/PubMed |
spelling | pubmed-62242422018-11-19 Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction Qian, Zenghui Li, Yiming Sun, Zhiyan Fan, Xing Xu, Kaibin Wang, Kai Li, Shaowu Zhang, Zhong Jiang, Tao Liu, Xing Wang, Yinyan Aging (Albany NY) Research Paper 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. Impact Journals 2018-10-22 /pmc/articles/PMC6224242/ /pubmed/30362964 http://dx.doi.org/10.18632/aging.101594 Text en Copyright © 2018 Qian et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Paper Qian, Zenghui Li, Yiming Sun, Zhiyan Fan, Xing Xu, Kaibin Wang, Kai Li, Shaowu Zhang, Zhong Jiang, Tao Liu, Xing Wang, Yinyan Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction |
title | Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction |
title_full | Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction |
title_fullStr | Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction |
title_full_unstemmed | Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction |
title_short | Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction |
title_sort | radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6224242/ https://www.ncbi.nlm.nih.gov/pubmed/30362964 http://dx.doi.org/10.18632/aging.101594 |
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