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

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

*Corresponding author for this work

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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.

Original languageEnglish
Number of pages10
JournalEuropean Radiology
Publication statusE-pub ahead of print - 26-May-2022


  • Breast neoplasms
  • Mass screening
  • Magnetic resonance imaging
  • Deep learning

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