Evidence map›Paper›PMID 39834611›Full record

ArticleRadiology advances2025

Comparing quantitative imaging biomarker alliance volumetric CT classifications with RECIST response categories.

Binsheng Zhao, Nancy Obuchowski, Hao Yang, Yen Chou, Hong Ma, Pingzhen Guo, Ying Tang, Lawrence Schwartz, Daniel Sullivan

Abstract read
In one paragraph

Article in Radiology advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Binsheng ZhaoDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.ORCID https://orcid.org/0009-0009-9934-9141
Nancy ObuchowskiDepartment of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH 44195, United States.
Hao YangDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.ORCID https://orcid.org/0009-0003-2718-0101
Yen ChouDepartment of Radiology, Fu Jen Catholic University Hospital, New Taipei City 24352, Taiwan.ORCID https://orcid.org/0000-0003-2388-194X
Hong MaDepartment of Radiology, Columbia University Irving Medical Center, New York, NY 10032, United States.
Pingzhen GuoDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.
Ying TangDepartment of Clinical Research and Regulatory Affairs, CCS Associates, McLean, VA 22102, United States.
Lawrence SchwartzDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.
Daniel SullivanDepartment of Radiology, Duke University Medical Center, Durham, NC 27710, United States.ORCID https://orcid.org/0000-0002-7556-5650

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
FDA HHS U01 FD007470NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Purpose: To assess agreement between CT volumetry change classifications derived from Quantitative Imaging Biomarker Alliance Profile cut-points (ie, QIBA CTvol classifications) and the Response Evaluation Criteria in Solid Tumors (RECIST) categories. Materials and Methods: Target lesions in lung, liver, and lymph nodes were randomly chosen from patients in 10 historical clinical trials for various cancers, ensuring a balanced representation of lesion types, diameter ranges described in the QIBA Profile, and variations in change magnitudes. Three radiologists independently segmented these lesions at baseline and follow-up scans using 2 software tools. Two types of predefined disagreements were assessed: Type I: substantive disagreement, where the disagreement between QIBA CTvol classifications and RECIST categories could not be attributed to the improved sensitivity of volumetry in detecting changes; and Type II: disagreement potentially arising from the improved sensitivity of volumetry in detecting changes. The proportion of lesions with disagreements between QIBA CTvol and RECIST, as well as the type of disagreements, was reported along with 95% CIs, both overall and within subgroups representing various factors. Results: A total of 2390 measurements from 478 lesions (158 lungs, 170 livers, 150 lymph nodes) in 281 patients were included. QIBA CTvol agreed with RECIST in 66.6% of interpretations. Of the 33.4% of interpretations with discrepancies, substantive disagreement (Type I) occurred in only 1.5% (95% CI: [0.8%, 2.1%]). Factors such as scanner vendor ( Conclusion: We conclude that QIBA CTvol classifications agree with RECIST categories.

Indexed as

computed tomographyresponse assessment criteriatumor volumetryvolumetric response assessment

Identifiers

PMID39834611
PMCPMC11739520

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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.