Automated triage of screening breast MRI examinations in high-risk women using an ensemble deep learning model Journal Article

   

Authors:	Bhowmik, A.; Monga, N.; Belen, K.; Varela, K.; Sevilimedu, V.; Thakur, S. B.; Martinez, D. F.; Sutton, E. J.; Pinker, K.; Eskreis-Winkler, S.
Article Title:	Automated triage of screening breast MRI examinations in high-risk women using an ensemble deep learning model
Abstract:	ObjectivesThe aim of the study is to develop and evaluate the performance of a deep learning (DL) model to triage breast magnetic resonance imaging (MRI) findings in high-risk patients without missing any cancers.Materials and MethodsIn this retrospective study, 16,535 consecutive contrast-enhanced MRIs performed in 8354 women from January 2013 to January 2019 were collected. From 3 New York imaging sites, 14,768 MRIs were used for the training and validation data set, and 80 randomly selected MRIs were used for a reader study test data set. From 3 New Jersey imaging sites, 1687 MRIs (1441 screening MRIs and 246 MRIs performed in recently diagnosed breast cancer patients) were used for an external validation data set. The DL model was trained to classify maximum intensity projection images as "extremely low suspicion" or "possibly suspicious." Deep learning model evaluation (workload reduction, sensitivity, specificity) was performed on the external validation data set, using a histopathology reference standard. A reader study was performed to compare DL model performance to fellowship-trained breast imaging radiologists.ResultsIn the external validation data set, the DL model triaged 159/1441 of screening MRIs as "extremely low suspicion" without missing a single cancer, yielding a workload reduction of 11%, a specificity of 11.5%, and a sensitivity of 100%. The model correctly triaged 246/246 (100% sensitivity) of MRIs in recently diagnosed patients as "possibly suspicious." In the reader study, 2 readers classified MRIs with a specificity of 93.62% and 91.49%, respectively, and missed 0 and 1 cancer, respectively. On the other hand, the DL model classified MRIs with a specificity of 19.15% and missed 0 cancers, highlighting its potential use not as an independent reader but as a triage tool.ConclusionsOur automated DL model triages a subset of screening breast MRIs as "extremely low suspicion" without misclassifying any cancer cases. This tool may be used to reduce workload in standalone mode, to shunt low suspicion cases to designated radiologists or to the end of the workday, or to serve as base model for other downstream AI tools.
Keywords:	magnetic resonance imaging; breast cancer; screening; learning; high-risk; deep; cancer; ensemble model
Journal Title:	Investigative Radiology
Volume:	58
Issue:	10
ISSN:	0020-9996
Publisher:	Lippincott Williams & Wilkins
Date Published:	2023-10-01
Start Page:	710
End Page:	719
Language:	English
ACCESSION:	WOS:001071299400002
DOI:	10.1097/rli.0000000000000976
PROVIDER:	wos
PUBMED:	37058323
PMCID:	PMC11334216
Notes:	The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF. Corresponding MSK author is Sarah Eskreis-Winkler -- Source: Wos

https://synapse.mskcc.org/synapse/works/has_related_data_chk/210789