Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study.
Purpose: This study evaluated the performance of multiparametric magnetic resonance imaging (MRI)–based fusion radiomics models (MMFRs) to predict telomerase reverse transcriptase (TERT) promoter mutation status and progression-free survival (PFS) in glioblastoma patients. Methods: We retrospectivel...
| Publicado en: | Neuroradiology Vol. 66; no. 1; pp. 81 - 93 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174559450&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174559450 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jan2024 vid: 66 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 174559450 173652333 174559450 174559450 10.1007/s00234-023-03245-3 174559450 ppf: 81 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study. aug: au: Zhang, Hongbo Zhang, Hanwen Zhang, Yuze Zhou, Beibei Wu, Lei Yang, Wanqun Lei, Yi Huang, Biao affil: https://ror.org/01vjw4z39 The Second School of Clinical Medicine, Southern Medical University, 510515, Guangzhou, China sug: subj: Glioma Prognosis Glioma Therapy Radiomics Methods Magnetic Resonance Imaging Methods Telomerase Analysis Mutation Prediction Models Outcome Assessment Human Multicenter Studies Retrospective Design Record Review Internal Validity Cancer Patients Cancer Care Facilities Progression-Free Survival Confidence Intervals Descriptive Statistics Temozolomide Therapeutic Use Glioma Surgery Glioma Drug Therapy Postoperative Care Methods Chemotherapy, Cancer Methods ab: Purpose: This study evaluated the performance of multiparametric magnetic resonance imaging (MRI)–based fusion radiomics models (MMFRs) to predict telomerase reverse transcriptase (TERT) promoter mutation status and progression-free survival (PFS) in glioblastoma patients. Methods: We retrospectively analysed 208 glioblastoma patients from two hospitals. Quantitative imaging features were extracted from each patient's T1-weighted, T1-weighted contrast-enhanced, and T2-weighted preoperative images. Using a coarse-to-fine feature selection strategy, four radiomics signature models were constructed based on the three MRI sequences and their combination for TERT promoter mutation status and PFS; model performance was subsequently evaluated. Subgroup analyses were performed by the radiomics signature of TERT promoter mutation status and PFS to distinguish patients who could benefit from prolonged temozolomide chemotherapy cycles. Results: TERT promoter mutation status was best predicted by MMFR, with an area under the curve (AUC) of 0.816 and 0.812 for the training and internal validation sets, respectively. The external test set also achieved stable and optimal prediction results (AUC, 0.823). MMFR better predicted patient PFS compared with the single-sequence radiomics signature in the test set (C-index, 0.643 vs 0.561 vs 0.620 vs 0.628). Subgroup analyses showed that more than six cycles of postoperative temozolomide chemotherapy were associated with improved PFS for patients in class 2 (high TERT promoter mutation and high survival rates; HR, 0.222; 95% CI, 0.054 − 0.923; p = 0.025). Conclusion: MMFR is an effective method to predict TERT promoter mutations and PFS in patients with glioblastoma. Moreover, subgroup analysis could differentiate patients who may benefit from prolonged TMZ chemotherapy cycles. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|