Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction
| Autor: | Mu, Zhou, Baishali, Chaudhury, Lawrence O, Hall, Dmitry B, Goldgof, Robert J, Gillies, Robert A, Gatenby |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Adult
Aged 80 and over Male Adolescent Brain Neoplasms Incidence Reproducibility of Results Middle Aged Prognosis Sensitivity and Specificity Survival Analysis United States Pattern Recognition Automated Machine Learning Young Adult Spatio-Temporal Analysis Risk Factors Image Interpretation Computer-Assisted Humans Female Glioblastoma Biomarkers Aged |
| Zdroj: | Journal of magnetic resonance imaging : JMRI. 46(1) |
| ISSN: | 1522-2586 |
| Popis: | Glioblastoma multiforme (GBM) is the most common malignant brain tumor in adults. Most GBMs exhibit extensive regional heterogeneity at tissue, cellular, and molecular scales, but the clinical relevance of the observed spatial imaging characteristics remains unknown. We investigated pretreatment magnetic resonance imaging (MRI) scans of GBMs to identify tumor subregions and quantify their image-based spatial characteristics that are associated with survival time.We quantified tumor subregions (termed habitats) in GBMs, which are hypothesized to capture intratumoral characteristics using multiple MRI sequences. For proof-of-concept, we developed a computational framework that used intratumoral grouping and spatial mapping to identify GBM tumor subregions and yield habitat-based features. Using a feature selector and three classifiers, experimental results from two datasets are reported, including Dataset1 with 32 GBM patients (594 tumor slices) and Dataset2 with 22 GBM patients, who did not undergo resection (261 tumor slices) for survival group prediction.In both datasets, we show that habitat-based features achieved 87.50% and 86.36% accuracies for survival group prediction, respectively, using leave-one-out cross-validation. Experimental results revealed that spatially correlated features between signal-enhanced subregions were effective for predicting survival groups (P0.05 for all three machine-learning classifiers).The quantitative spatial-correlated features derived from MRI-defined tumor subregions in GBM could be effectively used to predict the survival time of patients.2 J. MAGN. RESON. IMAGING 2017;46:115-123. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text