Evidence map›Paper›PMID 34538550›Full record

ArticleAcademic radiology2022

Prospective Comparison of Synthesized Mammography with DBT and Full-Field Digital Mammography with DBT Uncovers Recall Disagreements That may Impact Cancer Detection.

Monica L Huang, Kenneth Hess, Junsheng Ma, Lumarie Santiago, Marion E Scoggins, Elsa Arribas, Beatriz E Adrada, Huong T Le-Petross, Jessica W T Leung, Wei Yang and 2 more

Abstract read
In one paragraph

Article in Academic radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 24% of its field
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 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Monica L HuangDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1350, Houston, TX 77030. Electronic address: MLHuang@mdanderson.org.
Kenneth HessDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Junsheng MaDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Lumarie SantiagoDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1350, Houston, TX 77030.
Marion E ScogginsDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1350, Houston, TX 77030.
Elsa ArribasDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1350, Houston, TX 77030.
Beatriz E AdradaDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1350, Houston, TX 77030.
Huong T Le-PetrossDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1350, Houston, TX 77030.
Jessica W T LeungDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1350, Houston, TX 77030.
Wei YangDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1350, Houston, TX 77030.
William GeiserDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Rosalind P CandelariaDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1350, Houston, TX 77030.
The University of Texas MD Anderson Cancer Center · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

RATIONALE AND

objectivesSynthesized mammography with digital breast tomosynthesis (SM+DBT) and full-field digital mammography with DBT were prospectively evaluated for recall rate (RR), cancer detection rate (CDR), positive predictive value 1 (PPV1), lesion recall differences, and disagreements in recall for additional imaging. MATERIALS AND

methodsFrom December 15, 2015 to January 15, 2017, after informed consent was obtained for this Health Insurance Portability and Accountability Act compliant study, each enrolled patient's SM+DBT and FFDM+DBT were interpreted sequentially by one of eight radiologists. RR, CDR, PPV1, and imaging findings (asymmetry, focal asymmetry, mass, architectural distortion, and calcifications) recalled were reviewed.

resultsFor SM+DBT and FFDM+DBT in 1022 patients, RR was 7.3% and 7.9% (SM+DBT vs. FFDM+DBT: diff= -0.6%; 90% CI= -1.4%, 0.1%); CDR was 6.8 and 7.8 per 1000 (SM+DBT vs. FFDM+DBT: diff= -1.0, 95% CI= -5.5, 2.8, p = 0.317); PPV1 was 9.3% and 9.9% (relative positive predictive value for SM+DBT vs. FFDM+DBT: 0.95, 95% CI: 0.73-1.22, p = 0.669). FFDM+DBT detected eight cancers; SM+DBT detected seven (missed 1 cancer with calcifications). SM+DBT and FFDM+DBT disagreed on patient recall for additional imaging in 19 patients, with majority (68%, 13/19 patients) in the recall of patients for calcifications. For calcifications, SM+DBT recalled six patients that FFDM+DBT did not recall, and FFDM+DBT recalled seven patients that SM+DBT did not recall, even though the total number of calcifications finding recalled was similar overall for both SM+DBT and FFDM+DBT.

conclusionDisagreement in recall of patients for calcifications may impact cancer detection by SM+DBT, warranting further investigation.

Indexed as

Breast NeoplasmsCalcinosisEarly Detection of CancerFemaleHumansMammographyPredictive Value of TestsProspective StudiesRetrospective StudiesCalcificationsDigital breast tomosynthesisSynthesized mammography

Identifiers

PMID34538550
PMCPMC11891884
OpenAlexW3199130244

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.