Evidence map›Paper›PMID 41101341›Full record

ArticleJournal of the National Comprehensive Cancer Network : JNCCN2025

Comparative Accuracy of Digital Breast Tomosynthesis and Digital Mammography for Surveillance After Breast Cancer Treatment.

Janie M Lee, Laura Ichikawa, Yu-Ru Su, Naomi Noguchi, Rebecca A Hubbard, Karla Kerlikowske, Jennifer M Specht, Ellen S O'Meara, Louise M Henderson, Nehmat Houssami

Abstract readComparative Study
In one paragraph

Article in Journal of the National Comprehensive Cancer Network : JNCCN, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Janie M Lee1Department of Radiology, University of Washington School of Medicine, Seattle, WA.
Laura Ichikawa2Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA.
Yu-Ru Su2Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA.
Naomi Noguchi4Faculty of Medicine and Health, Sydney School of Public Health, University of Sydney, Sydney, Australia.
Rebecca A Hubbard5Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Karla Kerlikowske7Departments of Medicine, and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA.
Jennifer M Specht8Department of Medicine, University of Washington School of Medicine, Seattle, WA.
Ellen S O'Meara2Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA.
Louise M Henderson9Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Nehmat Houssami4Faculty of Medicine and Health, Sydney School of Public Health, University of Sydney, Sydney, Australia.

Funding

Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KARLA M KERLIKOWSKE · 2011 to 2026
$52.9M
NCI NIH HHS P01 CA154292
6 · The paper itself

Abstract

backgroundDigital breast tomosynthesis (DBT) has been less extensively evaluated for surveillance after breast cancer treatment than for screening. We compared the performance of DBT and digital mammography (DM) for surveillance of asymptomatic women with a personal history of breast cancer (PHBC), using US clinical practice data adjusted for population differences.

methodsWe conducted a retrospective cohort study of 28,042 women with PHBC and posttreatment surveillance mammograms performed between 2012 and 2018 at 37 facilities within the Breast Cancer Surveillance Consortium. We calculated performance measures and absolute differences (ADs) for DBT versus DM, including cancer detection rate (CDR), invasive CDR, interval cancer rate, recall rate, and false-positive biopsy recommendation rate, sensitivity, specificity, and positive predictive value (PPV1), with 95% confidence intervals, adjusting for population characteristics using overlap weighting.

resultsAmong 80,077 mammograms (DBT, n=27,852; DM, n=52,225) and 1,098 subsequent breast cancer diagnoses, DBT had a higher CDR (AD, 2.3 per 1,000 [95% CI, 0.3 to 4.3]), invasive CDR (AD, 2.9 per 1,000 [95% CI, 1.0 to 4.7]), and PPV1 (AD, 5.2% [95% CI, 2.5% to 7.8%)] and lower recall rate (AD, -1.0% [95% CI, -1.5% to -0.5%]) compared with DM. No significant differences overall were identified for interval cancer rate or other performance measures. Surveillance-detected cancers with DBT or DM had similar proportions of favorable prognostic characteristics (smaller size, node-negative, and estrogen receptor-positive).

conclusionsFor women with PHBC, DBT had better detection metrics compared with DM, as demonstrated by higher CDR, higher invasive CDR, and lower recall rate. However, the lack of significant reduction in interval cancer rate suggests that both DBT and DM may be used for posttreatment surveillance. Identifying effective imaging surveillance strategies for reducing interval cancer rates is needed to further improve posttreatment outcomes.

Indexed as

Breast NeoplasmsMammographyAdultAgedEarly Detection of CancerFemaleHumansMiddle AgedRetrospective Studies

Identifiers

PMID41101341
PMCPMC13214580

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.