Tomography (Ann Arbor, Mich.) · 2026
Can AI Detect What Is Not Injected? Evaluation of Lesion Detection in Virtual Contrast-Enhanced Breast MRI Using a Large-Scale AI Model Trained on GBCA-Enhanced Data.
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Heidarikahkesh Shirin, Schreiter Hannes, George Aju, Nguyen Tri-Thien, Skwierawska Dominika, Brock Luise, Hadler Dominique, Uder Michael, Laun Frederik B, Ehring Chris, Graber Johanna, Döppmann Lorenz, Horishnyi Ihor, Kapsner Lorenz A, Ohlmeyer Sabine, Liebert Andrzej, Bickelhaupt Sebastian
Аннотация
BACKGROUND/OBJECTIVES: Artificial intelligence (AI) can support lesion detection in gadolinium-based contrast agent-enhanced (GBCA-enhanced) breast MRI. However, its effectiveness on virtual contrast-enhanced (vCE) images remains unclear. This feasibility study evaluated the publicly available MAMA-MIA nnU-Net model trained on GBCA-enhanced data using an independent cohort of both GBCA-enhanced and vCE breast MRI.
METHODS: This IRB-approved retrospective study included the publicly available nnU-Net model trained on n = 1506 MAMA-MIA breast MRI scans and a cohort of n = 2126 in-house 3T breast MRI scans. A generative adversarial network (Pix2Pix-GAN) was developed on n = 1870 of the in-house scans and used to generate vCE data on the remaining independent n = 256 in-house cases. The MAMA-MIA nnU-net was applied to both GBCA-enhanced (GBCA) and corresponding vCE images. Ground-truth segmentations of malignant lesions served to calculate the Dice score, Hausdorff distance, and lesion dimension differences.
RESULTS: The final test set comprised n = 250 cases (n = 69 malignant, n = 181 benign). Lesion detection rates were 91% (n = 63/n = 69; 95% confidence interval (CI): 82.3-96.0%) for GBCA and 84% (n = 58/n = 69; 95% CI: 73.7-90.9%) for vCE. Two lesions missed in GBCA were identified by vCE. The Hausdorff distances were similar (GBCA: 6.4 (IQR: 3.2-9.3; 95% CI: 5.2-7.8) mm; vCE: 6.7 (IQR: 3.9-9.7; 95% CI: 5.3-8.0) mm, p = 0.564). The Dice scores showed minor differences (GBCA: 0.829 (IQR: 0.723-0.900; 95% CI: 0.786-0.865) vs. vCE: 0.826 (IQR: 0.720-0.857; 95% CI: 0.770-0.836); p < 0.001). vCE images had slightly higher non-target tissue segmentation (median 6072 mm3 vs. 5754 mm3).
CONCLUSIONS: A GBCA-trained algorithm demonstrated some cross-domain transferability to vCE images, albeit with a reduced case-level sensitivity of 84% (95% CI: 73.7-90.9%) vs. 91% (95% CI: 82.3-96.0%). Based on these preliminary results, further research, including larger cohorts and more diverse datasets, is warranted.
METHODS: This IRB-approved retrospective study included the publicly available nnU-Net model trained on n = 1506 MAMA-MIA breast MRI scans and a cohort of n = 2126 in-house 3T breast MRI scans. A generative adversarial network (Pix2Pix-GAN) was developed on n = 1870 of the in-house scans and used to generate vCE data on the remaining independent n = 256 in-house cases. The MAMA-MIA nnU-net was applied to both GBCA-enhanced (GBCA) and corresponding vCE images. Ground-truth segmentations of malignant lesions served to calculate the Dice score, Hausdorff distance, and lesion dimension differences.
RESULTS: The final test set comprised n = 250 cases (n = 69 malignant, n = 181 benign). Lesion detection rates were 91% (n = 63/n = 69; 95% confidence interval (CI): 82.3-96.0%) for GBCA and 84% (n = 58/n = 69; 95% CI: 73.7-90.9%) for vCE. Two lesions missed in GBCA were identified by vCE. The Hausdorff distances were similar (GBCA: 6.4 (IQR: 3.2-9.3; 95% CI: 5.2-7.8) mm; vCE: 6.7 (IQR: 3.9-9.7; 95% CI: 5.3-8.0) mm, p = 0.564). The Dice scores showed minor differences (GBCA: 0.829 (IQR: 0.723-0.900; 95% CI: 0.786-0.865) vs. vCE: 0.826 (IQR: 0.720-0.857; 95% CI: 0.770-0.836); p < 0.001). vCE images had slightly higher non-target tissue segmentation (median 6072 mm3 vs. 5754 mm3).
CONCLUSIONS: A GBCA-trained algorithm demonstrated some cross-domain transferability to vCE images, albeit with a reduced case-level sensitivity of 84% (95% CI: 73.7-90.9%) vs. 91% (95% CI: 82.3-96.0%). Based on these preliminary results, further research, including larger cohorts and more diverse datasets, is warranted.