Evidence map›Paper›PMID 28456570›Full record

ReviewAcademic radiology2017

Beyond Correlations, Sensitivities, and Specificities: A Roadmap for Demonstrating Utility of Advanced Imaging in Oncology Treatment and Clinical Trial Design.

Erich P Huang, Frank I Lin, Lalitha K Shankar

Abstract readReview
In one paragraph

Review in Academic radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. 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

3 authors.

Erich P HuangBiometric Research Program, Division of Cancer Treatment, Diagnosis National Cancer Institute, NIH, 9609 Medical Center Drive, MSC 9735, Bethesda, MD 20892-9735. Electronic address: erich.huang@nih.gov.
Frank I LinCancer Imaging Program, Division of Cancer Treatment, Diagnosis National Cancer Institute, NIH, Bethesda, Maryland.
Lalitha K ShankarCancer Imaging Program, Division of Cancer Treatment, Diagnosis National Cancer Institute, NIH, Bethesda, Maryland.

Funding

Intramural NIH HHS Z99 CA999999
6 · The paper itself

Abstract

Despite the widespread belief that advanced imaging should be very helpful in guiding oncology treatment decision and improving efficiency and success rates in treatment clinical trials, its acceptance has been slow. Part of this is likely attributable to gaps in study design and statistical methodology for these imaging studies. Also, results supporting the performance of the imaging in these roles have largely been insufficient to justify their use within the design of a clinical trial or in treatment decision making. Statistically significant correlations between the imaging results and clinical outcomes are often incorrectly taken as evidence of adequate performance. Assessments of whether the imaging can outperform standard techniques or meaningfully supplement them are also frequently neglected. This paper provides guidance on study designs and statistical analyses for evaluating the performance of advanced imaging in the various roles in treatment decision guidance and clinical trial conduct. Relevant methodology from the imaging literature is reviewed; gaps in the literature are addressed using related concepts from the more extensive genomic and in vitro biomarker literature.

Indexed as

Data Interpretation, StatisticalResearch DesignBiomarkers, TumorClinical Decision-MakingClinical Trials as TopicDiagnostic ImagingHumansNeoplasmsNeoplasm StagingPrognosisTreatment OutcomeBiomarkers, Tumorcancer imagingClinical trialstatistical analysisstudy design

Identifiers

PMID28456570
PMCPMC5547568

What OpenQuestion holds

Textmetadata
LicenceTDM
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.