Evidence map›Paper›PMID 31959544›Full record

ArticleInternational journal of radiation oncology, biology, physics2020

Improving Delineation of True Tumor Volume With Multimodal MRI in a Rat Model of Brain Metastasis.

James R Larkin, Manon A Simard, Axel de Bernardi, Vanessa A Johanssen, Francisco Perez-Balderas, Nicola R Sibson

Open access · hybridAbstract read
In one paragraph

Article in International journal of radiation oncology, biology, physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.3field-weighted citation impact, top 18% 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

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Biological profile of breast cancer brain metastasis.Acta neuropathologica communications · 2025
    Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. VCAM-1-targeted MRI Improves Detection of the Tumor-brain Interface.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022
    Article
  7. Article
  8. Animal models of brain metastasis.Neuro-oncology advances · 2021
    Article
  9. Review
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

6 authors at 4 institutions in 1 country.

James R LarkinDepartment of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
Manon A SimardDepartment of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
Axel de BernardiDepartment of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
Vanessa A JohanssenDepartment of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
Francisco Perez-BalderasDepartment of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
Nicola R SibsonDepartment of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford. Electronic address: nicola.sibson@oncology.ox.ac.uk.
Cancer Research UK · GBUniversity of Oxford · GBCRUK/MRC Oxford Institute for Radiation Oncology · GBMedical Research Council · GB

Funding

Cancer Research UK 16466Cancer Research UK 16945Cancer Research UK C5255/A15935Cancer Research UK C5255/A16466Cancer Research UK C5255/A18085Medical Research Council MC_PC_15029Wellcome Trust
6 · The paper itself

Abstract

purposeBrain metastases are almost universally lethal with short median survival times. Despite this, they are often potentially curable, with therapy failing only because of local relapse. One key reason relapse occurs is because treatment planning did not delineate metastasis margins sufficiently or accurately, allowing residual tumor to regrow. The aim of this study was to determine the extent to which multimodal magnetic resonance imaging (MRI), with a simple and automated analysis pipeline, could improve upon current clinical practice of single-modality, independent-observer tumor delineation. METHODS AND MATERIALS: We used a single rat model of brain metastasis (ENU1564 breast carcinoma cells in BD-IX rats), with and without radiation therapy. Multimodal MRI data were acquired using sequences either in current clinical use or in clinical trial and included postgadolinium T

resultsIn all cases, independent observers underestimated the true size of metastases from single-modality gadolinium-enhanced MRI (85 ± 36 μL vs 131 ± 40 μL histologic measurement), although multimodal MRI more accurately delineated tumor volume (132 ± 41 μL). Multimodal MRI offered increased sensitivity compared with independent observer for detecting metastasis (0.82 vs 0.61, respectively), with only a slight decrease in specificity (0.86 vs 0.98). Blood flow maps conferred the greatest improvements in margin detection for late-stage metastases after radiation therapy. Gadolinium-enhanced T

conclusionsThese findings suggest that multimodal MRI of brain metastases could significantly improve the visualization of brain metastasis margins, beyond current clinical practice, with the potential to decrease relapse rates and increase patient survival. This finding now needs validation in additional tumor models or clinical cohorts.

Indexed as

Magnetic Resonance ImagingMultimodal ImagingTumor BurdenAnimalsBrain NeoplasmsFemaleImage Processing, Computer-AssistedRats

Identifiers

PMID31959544
PMCPMC7082766
OpenAlexW2996892611

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.