Unknown

Dataset Information

0

A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas.


ABSTRACT:

Objective

The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature.

Methods

In this retrospective study, training (n?=?216) and validation (n?=?84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS.

Results

There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P?ConclusionsPFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors.

SUBMITTER: Liu X 

PROVIDER: S-EPMC6202688 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

altmetric image

Publications

A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas.

Liu Xing X   Li Yiming Y   Qian Zenghui Z   Sun Zhiyan Z   Xu Kaibin K   Wang Kai K   Liu Shuai S   Fan Xing X   Li Shaowu S   Zhang Zhong Z   Jiang Tao T   Wang Yinyan Y  

NeuroImage. Clinical 20181016


<h4>Objective</h4>The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature.<h4>Methods</h4>In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T  ...[more]

Similar Datasets

| S-EPMC6366985 | biostudies-literature
| S-EPMC6224242 | biostudies-literature
| S-EPMC8099476 | biostudies-literature
| S-EPMC6377997 | biostudies-literature
| S-EPMC4673237 | biostudies-literature
| S-EPMC8111095 | biostudies-literature
| S-EPMC7244462 | biostudies-literature
| S-EPMC6656314 | biostudies-literature
| S-EPMC7753319 | biostudies-literature
| S-EPMC6374451 | biostudies-literature