Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome

26 Oct 2016

Abstract excerpt

OBJECTIVE: Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the overall survival times of patients with GBM. METHODS:...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Glioma Diagnosis and Treatment
Share to Radiomics and Machine Learning in Medical Imaging

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 27781499
DOI: 10.1259/bjr.20160575

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcomePMID 27781499Read ↗

Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients.Semantic proximity Read ↗

Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma.Semantic proximity Read ↗

A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma.Semantic proximity Read ↗

Deciphering the glioblastoma phenotype by computed tomography radiomics.Semantic proximity Read ↗

Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activitiesSemantic proximity Read ↗

Multi-Habitat Radiomics Unravels Distinct Phenotypic Subtypes of Glioblastoma with Clinical and Genomic SignificanceSemantic proximity Read ↗

Radiomic Profiling of GBM Reveals Histopathological Correlations Associated with Tumour Response to Ionizing RadiationSemantic proximity Read ↗

Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles.Semantic proximity Read ↗

Predicting Survival in Patients with Glioblastoma Using MRI Radiomic Features Extracted from Radiation Planning VolumesSemantic proximity Read ↗

Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma.Semantic proximity Read ↗

Integrated morphologic analysis for the identification and characterization of disease subtypesSemantic proximity Read ↗

Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning Based Prediction Models in Distinguishing Glioblastomas from Single Brain MetastasesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.