Evidence map›Paper›PMID 29619649›Full record

ArticleJournal of neuro-oncology2018

Multi-center study finds postoperative residual non-enhancing component of glioblastoma as a new determinant of patient outcome.

Aikaterini Kotrotsou, Ahmed Elakkad, Jia Sun, Ginu A Thomas, Dongni Yang, Srishti Abrol, Wei Wei, Jeffrey S Weinberg, Ali S Bakhtiari, Moritz F Kircher and 6 more

Registry-linked trialAbstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Journal of neuro-oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07703761 (AI-driven Processing and Analysis of Glioma Imaging Data EUCAIM Database Contribution by the Italian Brain Glioma Initiative Italian Title), which is not on this map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
–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.

NCT07703761 recruitingnot on this mapstarted 2026, after this paper: background citation

AI-driven Processing and Analysis of Glioma Imaging Data EUCAIM Database Contribution by the Italian Brain Glioma Initiative Italian Title: Elaborazione ed Analisi Supportata Dall'Intelligenza Artificiale di Immagini di Risonanza Magnetica di Gliomi Cerebrali

TypeobservationalSponsorUniversità degli Studi di TrentoRan2026 to 2031Enrolled700ConditionsGlioma, Glioma (Diagnosis)ArmsAI-driven analysis of brain MRI data for early, non-invasive detection, segmentation, and histological characterization of gliomas using retrospective clinical and imaging records.
3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
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  7. Towards Optical Biopsy in Glioma Surgery.International journal of molecular sciences · 2025
    Review
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  9. Article
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  12. Review
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  17. AJNR. American journal of neuroradiology · 2021
    Article
  18. Review
  19. Article
  20. Article
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

16 authors.

Aikaterini KotrotsouDepartment of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ahmed ElakkadDepartment of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Jia SunDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ginu A ThomasDepartment of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Dongni YangDepartment of Diagnostic Interventional and Imaging, The University of Texas Health Science Center, Houston, TX, USA.
Srishti AbrolDepartment of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Wei WeiDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Jeffrey S WeinbergDepartment of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ali S BakhtiariDepartment of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Moritz F KircherDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
Markus M LuediDepartment of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
John F de GrootDepartment of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Raymond SawayaDepartment of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ashok J KumarDepartment of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Pascal O ZinnDepartment of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Rivka R ColenDepartment of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. rcolen@mdanderson.org.ORCID http://orcid.org/0000-0002-0882-0607

Funding

Cancer Prevention and Research Institute of Texas (US) RP160150John S. Dunn Foundation (US) John S. Dunn Sr. Distinguished Chair in Diagnostic Imaging FundUniversity of Texas MD Anderson Cancer Center (US) MD Anderson Brain Tumor Center ProgramUniversity of Texas MD Anderson Cancer Center (US) Startup
6 · The paper itself

Abstract

introductionThe aim of the present study is to assess whether postoperative residual non-enhancing volume (PRNV) is correlated and predictive of overall survival (OS) in glioblastoma (GBM) patients.

methodsWe retrospectively analyzed a total 134 GBM patients obtained from The University of Texas MD Anderson Cancer Center (training cohort, n = 97) and The Cancer Genome Atlas (validation cohort, n = 37). All patients had undergone postoperative magnetic resonance imaging immediately after surgery. We evaluated the survival outcomes with regard to PRNV. The role of possible prognostic factors that may affect survival after resection, including age, sex, preoperative Karnofsky performance status, postoperative nodular enhancement, surgically induced enhancement, and postoperative necrosis, was investigated using univariate and multivariate Cox proportional hazards regression analyses. Additionally, a recursive partitioning analysis (RPA) was used to identify prognostic groups.

resultsOur analyses revealed that a high PRNV (HR 1.051; p-corrected = 0.046) and old age (HR 1.031; p-corrected = 0.006) were independent predictors of overall survival. This trend was also observed in the validation cohort (higher PRNV: HR 1.127, p-corrected  = 0.002; older age: HR 1.034, p-corrected  = 0.022). RPA analysis identified two prognostic risk groups: low-risk group (PRNV < 70.2 cm

conclusionOur results demonstrate that high PRNV is associated with poor OS. Such results could be of great importance in a clinical setting, particularly in the postoperative management and monitoring of therapy.

Indexed as

Magnetic Resonance ImagingAdolescentAdultAgedAged, 80 and overBrainBrain NeoplasmsFemaleGlioblastomaHumansImage Interpretation, Computer-AssistedMaleMiddle AgedPostoperative PeriodPrognosisRetrospective StudiesGlioblastomaInvasionPostoperativeSurvival

Identifiers

What OpenQuestion holds

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Registered trials

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.