Breast <scp>MRI</scp> Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist

Eskreis‐Winkler, Sarah; Sutton, Elizabeth; D’Alessio, Donna; Gallagher, Katherine; Saphier, Nicole; Stember, Joseph N.; Martinez, Danny F.; Morris, Elizabeth A.; Pinker, Katja

doi:10.1002/jmri.28111

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…There is no basis for comparison in CEM literature, but in breast CE-MRI, BPE classification models have been implemented with accuracy values ranging from 67% to 75% for 4-class classification and 79% to 91.5% for binary classification (Borkowski et al 2020, Nam et al 2021, Eskreis-Winkler et al 2022. Our results fall within these ranges, and are even better for binary classification.…”

Section: Discussionmentioning

confidence: 54%

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Ripaud,

Jailin,

Quintana

et al. 2024

Phys. Med. Biol.

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Background: Breast Background Parenchymal Enhancement (BPE) is correlated withthe risk of breast cancer. BPE level is currently assessed by radiologists in Contrast-EnhancedMammography (CEM) using 4 classes: minimal, mild, moderate and marked, as describedin Breast Imaging Reporting and Data System (BI-RADS). However, BPE classification re-mains subject to intra- and inter-reader variability. Fully automated methods to assess BPElevel have already been developed in breast Contrast-Enhanced MRI (CE-MRI) and have beenshown to provide accurate and repeatable BPE level classification. However, to our knowledge,no BPE level classification tool is available in the literature for CEM.Materials & Methods: A BPE level classification tool based on Deep Learning (DL) hasbeen trained and optimized on 7012 CEM image pairs (low-energy and recombined images)and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbonearchitecture and loss function were analyzed, as well as the influence of lesion presence andtype on BPE assessment. The evaluation of the model performance was conducted using dif-ferent metrics including 4-class balanced accuracy and mean absolute error. The results of theoptimized model for a binary classification: minimal/mild versus moderate/marked, were alsoinvestigated.Results: The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI:71.2–71.9) with 98.8% of classification errors between adjacent classes. For binary classifi-cation, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in thepresence of lesions, but it is not statistically significant, suggesting that our model is robust tothe presence of lesions in the image for a classification task. Visual assessment also confirmsthat the model is more affected by non-mass enhancements than by mass-like enhancements.Conclusion: The proposed BPE classification tool for CEM achieves similar results thanwhat is published in the literature for CE-MRI.

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Section: Discussionmentioning

confidence: 54%

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Ripaud,

Jailin,

Quintana

et al. 2024

Phys. Med. Biol.

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“…1) consisted of 2 separately trained CNNs that performed (1) whole-breast segmentation and (2) examination triage into “extremely low suspicion” and “possibly suspicious” categories. Precontrast and first postcontrast fat-saturated T1-weighted, images were used to generate axial subtraction images, which were used to create standard maximum intensity projections (MIPs), as well as upper slab, middle slab, and lower slab MIPs, generated from the upper, middle, and lower axial images, respectively 30 …”

Section: Methodsmentioning

confidence: 99%

“…Precontrast and first postcontrast fat-saturated T1-weighted, images were used to generate axial subtraction images, which were used to create standard maximum intensity projections (MIPs), as well as upper slab, middle slab, and lower slab MIPs, generated from the upper, middle, and lower axial images, respectively. 30…”

Section: Model Developmentmentioning

confidence: 99%

Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model

et al. 2023

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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.

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“… 83 Radiologists demonstrate significant interreader variability in categorizing BPE. DL has been applied both to segment 84 and to classify BPE on breast MRI, 85 enabling full automation and standardization of this process.…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Deep learning in breast imaging

Bhowmik

Eskreis‐Winkler

2022

BJR|Open

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Millions of breast imaging exams are performed each year in an effort to reduce morbidity and mortality rates of breast cancer. Breast imaging exams are mainly performed for cancer screening, diagnostic workup of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, and to evaluate whether they are ready for real-world clinical use. Additionally, new regulatory frameworks are needed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and finally conclude by discussing the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.

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Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist

Cited by 18 publications

References 20 publications

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model

Deep learning in breast imaging

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