Use of a convolutional neural network-based mammographic evaluation to predict breast cancer recurrence among women with hormone receptor-positive operable breast cancer.
Breast cancer
Convolutional neural networks
Endocrine therapy
Imaging-based biomarker
Mammography
Journal
Breast cancer research and treatment
ISSN: 1573-7217
Titre abrégé: Breast Cancer Res Treat
Pays: Netherlands
ID NLM: 8111104
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
26
01
2022
accepted:
18
04
2022
pubmed:
17
5
2022
medline:
9
6
2022
entrez:
16
5
2022
Statut:
ppublish
Résumé
We evaluated whether a novel, fully automated convolutional neural network (CNN)-based mammographic evaluation can predict breast cancer relapse among women with operable hormone receptor (HR)-positive breast cancer. We conducted a retrospective cohort study among women with stage I-III, HR-positive unilateral breast cancer diagnosed at Columbia University Medical Center from 2007 to 2017, who received adjuvant endocrine therapy and had at least two mammograms (baseline, annual follow-up) of the contralateral unaffected breast for CNN analysis. We extracted demographics, clinicopathologic characteristics, breast cancer treatments, and relapse status from the electronic health record. Our primary endpoint was change in CNN risk score (range, 0-1). We used two-sample t-tests to assess for difference in mean CNN scores between patients who relapsed vs. remained in remission, and conducted Cox regression analyses to assess for association between change in CNN score and breast cancer-free interval (BCFI), adjusting for known prognostic factors. Among 848 women followed for a median of 59 months, there were 67 (7.9%) breast cancer relapses (36 distant, 25 local, 6 new primaries). There was a significant difference in mean absolute change in CNN risk score from baseline to 1-year follow-up between those who relapsed vs. remained in remission (0.001 vs. - 0.022, p = 0.030). After adjustment for prognostic factors, a 0.01 absolute increase in CNN score at 1-year was significantly associated with BCFI, hazard ratio = 1.05 (95% Confidence Interval 1.01-1.09, p = 0.011). Short-term change in the CNN-based breast cancer risk model on adjuvant endocrine therapy predicts breast cancer relapse, and warrants further evaluation in prospective studies.
Identifiants
pubmed: 35575954
doi: 10.1007/s10549-022-06614-3
pii: 10.1007/s10549-022-06614-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
35-47Subventions
Organisme : NCI NIH HHS
ID : R38 CA231577
Pays : United States
Organisme : Conquer Cancer Foundation
ID : 2021YIA-6088880692
Organisme : U.S. National Library of Medicine
ID : T15 LM007079-29
Organisme : NIH HHS
ID : R38 CA231577
Pays : United States
Organisme : NIH HHS
ID : P30 CA013696
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA226060-04SI
Pays : United States
Organisme : Irving Medical Center, Columbia University
ID : Velocity Pilot Grant
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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