Evidence map›Paper›PMID 36445396›Full record

ArticleBrain : a journal of neurology2023

Assessment and prediction of glioblastoma therapy response: challenges and opportunities.

Dan Qi, Jing Li, C Chad Quarles, Ekokobe Fonkem, Erxi Wu

Open access · greenAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
7.8field-weighted citation impact, top 2% of its field
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

44 citing papers in PubMed, 58 citations in OpenAlex.

  1. The novel [RSC advances · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Coupling proteostasis andTheranostics · 2026
    Article
  16. Article
  17. Article
  18. Article
  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

5 authors at 4 institutions in 1 country.

Dan QiDepartment of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA.ORCID 0000-0001-7015-1849
Jing LiSchool of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
C Chad QuarlesDepartment of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
Ekokobe FonkemDepartment of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA.
Erxi WuDepartment of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA.ORCID 0000-0002-1680-3639
Baylor Scott & White Health · USGeorgia Institute of Technology · USLivestrong Foundation · USThe University of Texas MD Anderson Cancer Center · US

Funding

MRI Assessment of Tumor Perfusion, Permeability and CellularityR01CA158079 · NCI · VANDERBILT UNIVERSITY · PI QUARLES, CHRISTOPHER CHAD · 2011 to 2021
$3.5M
NCI NIH HHS R01 CA158079
6 · The paper itself

Abstract

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Indexed as

BiomarkersGlioblastomaClinical Decision RulesDisease ProgressionHumansMachine LearningBiomarkersglioblastomaliquid biomarkermachine learningmultiparametric imagingtherapy response

Identifiers

PMID36445396
PMCPMC10319779
OpenAlexW4310463909

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.