Artificial intelligence-assisted triage of screening mammograms following breast-conserving therapy: a comparative simulation study.

Authors

Ga Eun Park, Bong Joo Kang, Sung Hun Kim, Han Song Mun

Published in

La Radiologia medica. Sep 11, 2026. Epub Sep 11, 2026.

Abstract

To evaluate the diagnostic performance and potential of artificial intelligence (AI)-based triage for screening mammograms following breast-conserving therapy (BCT).
In this retrospective study, consecutive post-BCT mammograms obtained between January and May 2021 were analyzed and divided into ipsilateral and contralateral breasts. Triage was simulated using three models with a commercial AI-based computer-aided detection (CAD), and outcomes were categorized as recall or no recall: (1) original report-based triage, (2) standalone AI, and (3) decision referral AI-triage. Cancer detection rate (CDR), recall rates, and diagnostic performance were evaluated.
A total of 1190 women were enrolled. For the ipsilateral breast, 10 mammography-visible recurrences were identified. All three models-original report, standalone AI, and decision referral AI-achieved equivalent CDR (6.6 per 1000) and sensitivity (80%), with recall rates of 3.4%, 23.0%, and 2.8%, respectively. AI-CAD classified 77% of examinations as negative without a reduction in CDR or sensitivity. For the contralateral breast, three recurrences were identified. The original report yielded a CDR of 1.8 per 1000, a recall rate of 1.9%, and 66.7% sensitivity. While AI-CAD triaged 90% of examinations as negative, standalone AI achieved a CDR of 2.6 per 1000, recall rate of 9.8%, and 100% sensitivity. Decision referral AI maintained CDR and sensitivity with a lower recall rate (2.0%).
Our simulation suggests that AI-based triage can exclude a substantial portion of negative mammograms following BCT without a reduction in CDR or sensitivity. Nonetheless, radiologist expertise remains crucial, particularly in interpreting the ipsilateral breast, given the higher false positive rate of AI-CAD.

PMID:
42726463
Bibliographic data and abstract were imported from PubMed on 12 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.