Article ; Online: Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model.
2023 Volume 58, Issue 10, Page(s) 710–719
Abstract: Objectives: The 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 methods: In this ... ...
| Abstract | Objectives: The 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 methods: In 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. Results: In 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. Conclusions: Our 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. |
|||||
|---|---|---|---|---|---|---|
| MeSH term(s) | Humans ; Female ; Triage/methods ; Retrospective Studies ; Deep Learning ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/pathology ; Magnetic Resonance Imaging/methods | |||||
| Language | English | |||||
| Publishing date | 2023-04-11 | |||||
| Publishing country | United States | |||||
| Document type | Journal Article ; Research Support, N.I.H., Extramural | |||||
| ZDB-ID | 80345-5 | |||||
| ISSN | 1536-0210 ; 0020-9996 | |||||
| ISSN (online) | 1536-0210 | |||||
| ISSN | 0020-9996 | |||||
| DOI | 10.1097/RLI.0000000000000976 | |||||
| Shelf mark |
|
|||||
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
In stock of ZB MED Cologne/Königswinter
| Zs.A 734: Show issues | Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG) |
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.