Evidence map›Paper›PMID 36443594›Full record

ReviewNature reviews. Clinical oncology2023

Criteria for the translation of radiomics into clinically useful tests.

Erich P Huang, James P B O'Connor, Lisa M McShane, Maryellen L Giger, Philippe Lambin, Paul E Kinahan, Eliot L Siegel, Lalitha K Shankar

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nature reviews. Clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
149citing papers in PubMed, 8 pooled it
–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

149 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Guideline
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  14. Invariant Texture Features to Gray Level Discretization.Journal of imaging informatics in medicine · 2026
    Article
  15. The Next Frontier in Quantitative Co-Clinical Imaging to Advance Functional Precision Oncology.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
    Article
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  18. Review
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89 more citing papers are in PubMed but not listed here.

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

8 authors.

Erich P HuangDivision of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. erich.huang@nih.gov.ORCID http://orcid.org/0000-0002-8458-655X
James P B O'ConnorDivision of Radiotherapy and Imaging, Institute of Cancer Research, London, UK.
Lisa M McShaneDivision of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.ORCID http://orcid.org/0000-0001-8195-3206
Maryellen L GigerDepartment of Radiology, University of Chicago, Chicago, IL, USA.
Philippe LambinDepartment of Precision Medicine, Maastricht University, Maastricht, Netherlands.ORCID http://orcid.org/0000-0001-7961-0191
Paul E KinahanDepartment of Radiology, University of Washington, Seattle, WA, USA.
Eliot L SiegelDepartment of Diagnostic Radiology, University of Maryland, Baltimore, MD, USA.
Lalitha K ShankarDivision of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.

Funding

Project 4: Risk stratification for pulmonary nodules detected by CT imaging using plasma and imaging biomarkersP50CA228944 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI A McGarry Houghton · 2019 to 2026
$19.6M
NCI NIH HHS P50 CA228944
6 · The paper itself

Abstract

Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefit-risk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in the future.

Identifiers

PMID36443594
PMCPMC9707172

What OpenQuestion 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 OpenQuestion graph.