Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images.
Glioblastoma multiforme (GBM) are malignant brain tumors, associated with poor overall survival (OS). This study aims to predict OS of GBM patients (in days) using a regression framework and assess the impact of tumor shape features on OS prediction. Multi-channel MR image derived texture features,...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 8; pp. 1683 - 1692 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| 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=137705921&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137705921 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2019 vid: 57 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137705921 137705921 NLM31104273 137705921 10.1007/s11517-019-01986-z NLM31104273 137705921 ppf: 1683 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images. aug: au: Sanghani, Parita Ang, Beng Ti King, Nicolas Kon Kam Ren, Hongliang affil: Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore sug: subj: Brain Neoplasms Mortality Image Processing, Computer Assisted Magnetic Resonance Imaging Methods Glioma Mortality Brain Neoplasms Glioma Imaging, Three-Dimensional Regression Resource Databases Human Funding Source ab: Glioblastoma multiforme (GBM) are malignant brain tumors, associated with poor overall survival (OS). This study aims to predict OS of GBM patients (in days) using a regression framework and assess the impact of tumor shape features on OS prediction. Multi-channel MR image derived texture features, tumor shape, and volumetric features, and patient age were obtained for 163 GBM patients. In order to assess the impact of tumor shape features on OS prediction, two feature sets, with and without tumor shape features, were created. For the feature set with tumor shape features, the mean prediction error (MPE) was 14.6 days and its 95% confidence interval (CI) was 195.8 days. For the feature set excluding shape features, the MPE was 17.1 days and its 95% CI was observed to be 212.7 days. The coefficient of determination (R2) value obtained for the feature set with shape features was 0.92, while it was 0.90 for the feature set excluding shape features. Although marginal, inclusion of shape features improves OS prediction in GBM patients. The proposed OS prediction method using regression provides good accuracy and overcomes the limitations of GBM OS classification, like choosing data-derived or pre-decided thresholds to define the OS groups. Graphical abstract Two feature sets: with and without tumor shape features were extracted from T1-weighted contrast-enhanced, T2-weighted and FLAIR MRI. These feature sets were analyzed using the Mean Prediction Error (MPE) and its 95% Confidence Interval (CI) obtained from the Bland-Altman plot, along with the coefficient of determination (R2) value to assess the impact of tumor shape features on overall survival prediction of glioblastoma multiforme patients. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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