Evidence map›Paper›PMID 28127586›Full record

ArticleTomography (Ann Arbor, Mich.)2016

Accrual Patterns for Clinical Studies Involving Quantitative Imaging: Results of an NCI Quantitative Imaging Network (QIN) Survey.

Brenda F Kurland, Sameer Aggarwal, Thomas E Yankeelov, Elizabeth R Gerstner, James M Mountz, Hannah M Linden, Ella F Jones, Kellie L Bodeker, John M Buatti

Abstract read
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Brenda F KurlandDepartment of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania.
Sameer AggarwalDepartment of Medicine, George Washington University, Washington, D.C.
Thomas E YankeelovInstitute for Computational and Engineering Sciences, and Departments of Biomedical Engineering and Internal Medicine, The University of Texas at Austin, Austin, Texas.
Elizabeth R GerstnerDepartment of Neurology, Massachusetts General Hospital, Boston, Massachusetts; Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania.
James M MountzDepartment of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania.
Hannah M LindenDivision of Medical Oncology, University of Washington, Seattle, Washington.
Ella F JonesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California.
Kellie L BodekerDepartment of Radiation Oncology, University of Iowa, Iowa City, Iowa.
John M BuattiDepartment of Radiation Oncology, University of Iowa, Iowa City, Iowa.

Funding

VECTOR CORE FACILITYP30CA047904 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Leonard J. Appleman · 1988 to 2026
$158.0M
University of Pittsburgh Clinical and Translational Science InstituteUL1TR000005 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2012 to 2015
$50.8M
Advanced PET/CT Imaging for Improving Clinical TrialsU01CA148131 · NCI · UNIVERSITY OF WASHINGTON · PI KINAHAN, PAUL E., LINDEN, HANNAH M · 2010 to 2019
$6.0M
Quantitative Imaging to Assess Response in Cancer Therapy TrialsU01CA140206 · NCI · UNIVERSITY OF IOWA · PI BUATTI, JOHN M, CASAVANT, THOMAS L. · 2010 to 2019
$5.8M
Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant TherapyU01CA142565 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI KARCZMAR, GREGORY S., NANDA, RITA · 2010 to 2019
$4.5M
Quantitative MRI of Glioblastoma ResponseU01CA154601 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI KALPATHY-CRAMER, JAYASHREE · 2011 to 2016
$3.4M
Quantitative Imaging for Assessing Breast Cancer Response to TreatmentU01CA151235 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HYLTON-WATSON, NOLA M. · 2011 to 2015
$2.8M
Quantitative Biomarker Imaging for Early Therapy Response Assessment in CancerU01CA140230 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MOUNTZ, JAMES M. · 2009 to 2013
$2.1M
NCATS NIH HHS UL1 TR000005NCI NIH HHS P30 CA047904NCI NIH HHS U01 CA140206NCI NIH HHS U01 CA140230NCI NIH HHS U01 CA142565NCI NIH HHS U01 CA148131NCI NIH HHS U01 CA151235NCI NIH HHS U01 CA154601
6 · The paper itself

Abstract

Patient accrual is essential for the success of oncology clinical trials. Recruitment for trials involving the development of quantitative imaging biomarkers may face different challenges than treatment trials. This study surveyed investigators and study personnel for evaluating accrual performance and perceived barriers to accrual and for soliciting solutions to these accrual challenges that are specific to quantitative imaging-based trials. Responses for 25 prospective studies were received from 12 sites. The median percent annual accrual attained was 94.5% (range, 3%-350%). The most commonly selected barrier to recruitment (n = 11/25, 44%) was that "patients decline participation," followed by "too few eligible patients" (n = 10/25, 40%). In a forced choice for the single greatest recruitment challenge, "too few eligible patients" was the most common response (n = 8/25, 32%). Quantitative analysis and qualitative responses suggested that interactions among institutional, physician, and patient factors contributed to accrual success and challenges. Multidisciplinary collaboration in trial design and execution is essential to accrual success, with attention paid to ensuring and communicating potential trial benefits to enrolled and future patients.

Indexed as

accrualclinical trialMRIpatient enrollmentPET

Identifiers

PMID28127586
PMCPMC5260812

What OpenQuestion holds

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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.