Evidence map›Paper›PMID 27358767›Full record

ArticleNeuroImage. Clinical2016

Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma.

Michel Bilello, Hamed Akbari, Xiao Da, Jared M Pisapia, Suyash Mohan, Ronald L Wolf, Donald M O'Rourke, Maria Martinez-Lage, Christos Davatzikos

Abstract read
In one paragraph

Article in NeuroImage. Clinical, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed.

  1. An MRI Atlas of Regional Brain Vulnerability to Metastatic Disease.medRxiv : the preprint server for health sciences · 2026
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  15. Sex-Specific Differences in Patients withAJNR. American journal of neuroradiology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Michel BilelloDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.
Hamed AkbariDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.
Xiao DaDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.
Jared M PisapiaDepartment of Neurosurgery, University of Pennsylvania, Philadelphia, USA.
Suyash MohanDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.
Ronald L WolfDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.
Donald M O'RourkeDepartment of Neurosurgery, University of Pennsylvania, Philadelphia, USA.
Maria Martinez-LageDepartment of Pathology & Laboratory Medicine, University of Pennsylvania, Philadelphia, USA.
Christos DavatzikosDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.

Funding

Statistical atlases of brain tumor MRI:do imaging phenotypes predict progression?R01NS042645 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2002 to 2024
$8.7M
Cancer imaging phenomics software suite: application to brain and breast cancerU24CA189523 · NCI · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS, KONTOS, DESPINA · 2015 to 2019
$3.2M
NCI NIH HHS U24 CA189523NINDS NIH HHS R01 NS042645
6 · The paper itself

Abstract

background and purposeIn treating glioblastoma (GB), surgical and chemotherapeutic treatment guidelines are, for the most part, independent of tumor location. In this work, we compiled imaging data from a large cohort of GB patients to create statistical atlases illustrating the disease spatial frequency as a function of patient demographics as well as tumor characteristics. MATERIALS AND

methodsTwo-hundred-six patients with pathology-proven glioblastoma were included. Of those, 65 had pathology-proven recurrence and 113 had molecular subtype and genetic information. We used validated software to segment the tumors in all patients and map them from patient space into a common template. We then created statistical maps that described the spatial location of tumors with respect to demographics and tumor characteristics. We applied a chi-square test to determine whether pattern differences were statistically significant.

resultsThe most frequent location for glioblastoma in our patient population is the right temporal lobe. There are statistically significant differences when comparing patterns using demographic data such as gender (p = 0.0006) and age (p = 0.006). Small and large tumors tend to occur in separate locations (p = 0.0007). The tumors tend to occur in different locations according to their molecular subtypes (p < 10(- 6)). The classical subtype tends to spare the frontal lobes, the neural subtype tend to involve the inferior right frontal lobe. Although the sample size is limited, there was a difference in location according to EGFR VIII genotype (p < 10(- 4)), with a right temporal dominance for EFGR VIII negative tumors, and frontal lobe dominance in EGFR VIII positive tumors.

conclusionsSpatial location of GB is an important factor that correlates with demographic factors and tumor characteristics, which should therefore be considered when evaluating a patient with GB and might assist in personalized treatment.

Indexed as

AdultAgedAged, 80 and overAtlases as TopicBrain NeoplasmsFemaleGlioblastomaHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedYoung AdultAtlasGlioblastomaMRISpatialStatistical

Identifiers

PMID27358767
PMCPMC4916067

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.