ArticleJournal of neuro-oncology2017
Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study.
Article in Journal of neuro-oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Roadmap to Local Tumour Growth: Insights from Cervical Cancer.Scientific reports · 2019Trial
- Spatial Mapping of Glioblastoma Infiltration: Diffusion Tensor Imaging-Based Radiomics and Connectomics in Recurrence Prediction.Brain sciences · 2025Review
- Profiles of survival prediction in glioblastoma.Brain & spine · 2025Article
- Artificial Intelligence in Brain Tumors.Advances in experimental medicine and biology · 2024Review
- Analysis of Functional Neuroplastic Changes in the Cortical Language System in Relation to Different Growth Patterns of Glioblastoma.Brain sciences · 2023Article
- Relapse patterns and radiation dose exposure in IDH wild-type glioblastoma at first radiographic recurrence following chemoradiation.Journal of neuro-oncology · 2022Article
- Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.Diagnostics (Basel, Switzerland) · 2022Review
- Review
- Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges.Neuro-oncology advances · 2020Article
- Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.Acta neurochirurgica · 2020Article
- Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.Neuroradiology · 2020Review
- Assessment of Pre-operative Measurements of Tumor Size by MRI Methods as Survival Predictors in Wild Type IDH Glioblastoma.Frontiers in oncology · 2020Article
- Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images.Medical & biological engineering & computing · 2019Article
- Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation.Frontiers in oncology · 2019Review
- Localization patterns of cathepsins K and X and their predictive value in glioblastoma.Radiology and oncology · 2018Article
- Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.Medical physics · 2018Article
- Imaging in neuro-oncology.Therapeutic advances in neurological disorders · 2018Review
- Effects of MRI scanner parameters on breast cancer radiomics.Expert systems with applications · 2017Article
- Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data.Journal of neuro-oncology · 2017Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this retrospective, IRB-exempt study, we analyzed data from 68 patients diagnosed with glioblastoma (GBM) in two institutions and investigated the relationship between tumor shape, quantified using algorithmic analysis of magnetic resonance images, and survival. Each patient's Fluid Attenuated Inversion Recovery (FLAIR) abnormality and enhancing tumor were manually delineated, and tumor shape was analyzed by automatic computer algorithms. Five features were automatically extracted from the images to quantify the extent of irregularity in tumor shape in two and three dimensions. Univariate Cox proportional hazard regression analysis was performed to determine how prognostic each feature was of survival. Kaplan Meier analysis was performed to illustrate the prognostic value of each feature. To determine whether the proposed quantitative shape features have additional prognostic value compared with standard clinical features, we controlled for tumor volume, patient age, and Karnofsky Performance Score (KPS). The FLAIR-based bounding ellipsoid volume ratio (BEVR), a 3D complexity measure, was strongly prognostic of survival, with a hazard ratio of 0.36 (95% CI 0.20-0.65), and remained significant in regression analysis after controlling for other clinical factors (P = 0.0061). Three enhancing-tumor based shape features were prognostic of survival independently of clinical factors: BEVR (P = 0.0008), margin fluctuation (P = 0.0013), and angular standard deviation (P = 0.0078). Algorithmically assessed tumor shape is statistically significantly prognostic of survival for patients with GBM independently of patient age, KPS, and tumor volume. This shows promise for extending the utility of MR imaging in treatment of GBM patients.
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