Details

Autor(en) / Beteiligte

• Liu, Xing
• Li, Yiming
• Qian, Zenghui
• Sun, Zhiyan
• Xu, Kaibin
• Wang, Kai
• Liu, Shuai
• Fan, Xing
• Li, Shaowu
• Zhang, Zhong
• Jiang, Tao
• Wang, Yinyan

Titel

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

Ist Teil von

• NeuroImage clinical, 2018-01, Vol.20, p.1070-1077

Ort / Verlag

Netherlands: Elsevier Inc

Erscheinungsjahr

2018

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. 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. 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 < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts. PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors. •We developed a non-invasive model for the prediction of PFS in patients with lower-grade gliomas.•We further revealed the biological processes underlying the radiomic signature by using comprehensive radiogenomic analysis.•PFS of lower-grade gliomas could be predicted effectively based on the radiomics model.