Evidence map›Paper›PMID 35158845›Full record

ArticleCancers2022

Predicting Glioblastoma Cellular Motility from In Vivo MRI with a Radiomics Based Regression Model.

Kellen Mulford, Mariah McMahon, Andrew M Gardeck, Matthew A Hunt, Clark C Chen, David J Odde, Christopher Wilke

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Kellen MulfordDepartment of Radiation Oncology, University of Minnesota, 516 Delaware St. SE, Minneapolis, MN 55455, USA.
Mariah McMahonDepartment of Biomedical Engineering, University of Minnesota, 312 Church St. SE, Minneapolis, MN 55455, USA.
Andrew M GardeckDepartment of Biomedical Engineering, University of Minnesota, 312 Church St. SE, Minneapolis, MN 55455, USA.
Matthew A HuntDepartment of Neurosurgery, University of Minnesota, 420 Delaware St. SE, Minneapolis, MN 55455, USA.
Clark C ChenDepartment of Neurosurgery, University of Minnesota, 420 Delaware St. SE, Minneapolis, MN 55455, USA.
David J OddeDepartment of Biomedical Engineering, University of Minnesota, 312 Church St. SE, Minneapolis, MN 55455, USA.ORCID 0000-0001-7731-2799
Christopher WilkeDepartment of Radiation Oncology, University of Minnesota, 516 Delaware St. SE, Minneapolis, MN 55455, USA.

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
Project 2: Cell Migration in Mechanically Complex MicroenvironmentsU54CA210190 · NCI · UNIVERSITY OF MINNESOTA · PI WOOD, DAVID KEVIN · 2016 to 2020
$10.0M
NRSA Training CoreTL1TR002493 · NCATS · UNIVERSITY OF MINNESOTA · PI FULKERSON, JAYNE ALLYN · 2018 to 2022
$3.1M
National Institutes of Health's National Center for Advancing Translational Sciences TL1R002493National Institutes of Health's National Center for Advancing Translational Sciences UL1TR002494NCATS NIH HHS TL1 TR002493NCATS NIH HHS UL1 TR002494NCI NIH HHS U54 CA210190
6 · The paper itself

Abstract

Characterizing the motile properties of glioblastoma tumor cells could provide a useful way to predict the spread of tumors and to tailor the therapeutic approach. Radiomics has emerged as a diagnostic tool in the classification of tumor grade, stage, and prognosis. The purpose of this work is to examine the potential of radiomics to predict the motility of glioblastoma cells. Tissue specimens were obtained from 31 patients undergoing surgical resection of glioblastoma. Mean tumor cell motility was calculated from time-lapse videos of specimen cells. Manual segmentation was used to define the border of the enhancing tumor T1-weighted MR images, and 107 radiomics features were extracted from the normalized image volumes. Model parameter coefficients were estimated using the adaptive lasso technique validated with leave-one-out cross validation (LOOCV) and permutation tests. The R-squared value for the predictive model was 0.60 with

Indexed as

cellular motilityglioblastomaMRIradiomics

Identifiers

PMID35158845
PMCPMC8833801

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.