Evidence map›Paper›PMID 42200192›Full record

ArticleNeuro-oncology advances

Prediction of treatment failure in patients with glioblastoma with perfusion MRI and molecular biomarkers.

Riccardo Ludovichetti, Jordan Villiers, Gergely Bertalan, Nicolin Hainc, Andrea Bink, Emilie Le Rhun, Ulrike Held, Michael Weller, Zsolt Kulcsar, Ramona-Alexandra Todea

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Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Riccardo LudovichettiDepartment of Neuroradiology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Jordan VilliersEpidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Gergely BertalanDepartment of Neuroradiology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0009-0000-3986-9844
Nicolin HaincDepartment of Neuroradiology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-0916-7387
Andrea BinkDepartment of Neuroradiology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-2163-3400
Emilie Le RhunDepartment of Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-9408-3278
Ulrike HeldEpidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-3105-5840
Michael WellerDepartment of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-1748-174X
Zsolt KulcsarDepartment of Neuroradiology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-6805-5150
Ramona-Alexandra TodeaDepartment of Neuroradiology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-4650-8312

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Predicting early treatment failure in glioblastoma remains challenging. Given the median survival of 13.5 months, better prognostic biomarkers are needed. This study aimed to identify predictors of early treatment failure and their association with overall survival (OS). Methods: We performed a retrospective analysis of consecutive patients with newly diagnosed glioblastoma who underwent DSC- and DCE-MRI before surgery and radiochemotherapy. Treatment failure was defined as tumor progression per RANO 2.0 criteria within 6 months of surgery. Factors independently associated with outcome among clinical, MRI perfusion at the diagnosis and molecular parameters were identified using multivariable logistic regression models. OS was evaluated using a 180-day landmark analysis to avoid time-dependent bias. Results: Among 62 patients diagnosed between 01/2017 and 12/2021, 44 patients (71%) were non-responders and 18 patients (29%) responders. Elevated pretreatment rCBV and Ktrans were associated with treatment response ( Conclusion: Pretreatment MRI perfusion metrics may predict short-term treatment failure and may guide early experimental therapy, while the

Indexed as

glioblastomaMGMTperfusiontreatment failure

Identifiers

PMID42200192
PMCPMC13200788

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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.