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Open Access 26-05-2022 | Artificial Intelligence | Imaging Informatics and Artificial Intelligence

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time

Authors: Xueping Jing, Mirjam Wielema, Ludo J. Cornelissen, Margo van Gent, Willie M. Iwema, Sunyi Zheng, Paul E. Sijens, Matthijs Oudkerk, Monique D. Dorrius, Peter M.A. van Ooijen

Published in: European Radiology | Issue 12/2022

Abstract

Objectives

To investigate the feasibility of automatically identifying normal scans in ultrafast breast MRI with artificial intelligence (AI) to increase efficiency and reduce workload.

Methods

In this retrospective analysis, 837 breast MRI examinations performed on 438 women from April 2016 to October 2019 were included. The left and right breasts in each examination were labelled normal (without suspicious lesions) or abnormal (with suspicious lesions) based on final interpretation. Maximum intensity projection (MIP) images of each breast were then used to train a deep learning model. A high sensitivity threshold was calculated based on the detection trade - off (DET) curve on the validation set. The performance of the model was evaluated by receiver operating characteristic analysis of the independent test set. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with the high sensitivity threshold were calculated.

Results

The independent test set consisted of 178 examinations of 149 patients (mean age, 44 years ± 14 [standard deviation]). The trained model achieved an AUC of 0.81 (95% CI: 0.75–0.88) on the independent test set. Applying a threshold of 0.25 yielded a sensitivity of 98% (95% CI: 90%; 100%), an NPV of 98% (95% CI: 89%; 100%), a workload reduction of 15.7%, and a scan time reduction of 16.6%.

Conclusion

This deep learning model has a high potential to help identify normal scans in ultrafast breast MRI and thereby reduce radiologists’ workload and scan time.

Key Points

• Deep learning in TWIST may eliminate the necessity of additional sequences for identifying normal breasts during MRI screening.

• Workload and scanning time reductions of 15.7% and 16.6%, respectively, could be achieved with the cost of 1 (1 of 55) false negative prediction.

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Title: Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time
Authors: Xueping Jing
Mirjam Wielema
Ludo J. Cornelissen
Margo van Gent
Willie M. Iwema
Sunyi Zheng
Paul E. Sijens
Matthijs Oudkerk
Monique D. Dorrius
Peter M.A. van Ooijen
Publication date: 26-05-2022
Publisher: Springer Berlin Heidelberg
Keywords: Artificial Intelligence
Breast MRI
Published in: European Radiology / Issue 12/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08863-8

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time

Abstract

Objectives

Methods

Results

Conclusion

Key Points

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

Key Points

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