AI-Based screening for thoracic aortic aneurysms in routine breast MRI
Abstract Prognosis for thoracic aortic aneurysms is significantly worse for women than men, with a higher mortality rate observed among female patients. The increasing use of magnetic resonance breast imaging (MRI) offers a unique opportunity for simultaneous detection of both breast cancer and thor...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59694-2 |
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| author | Dimitrios Bounias Tobit Führes Luise Brock Johanna Graber Lorenz A. Kapsner Andrzej Liebert Hannes Schreiter Jessica Eberle Dominique Hadler Dominika Skwierawska Ralf Floca Peter Neher Balint Kovacs Evelyn Wenkel Sabine Ohlmeyer Michael Uder Klaus Maier-Hein Sebastian Bickelhaupt |
| author_facet | Dimitrios Bounias Tobit Führes Luise Brock Johanna Graber Lorenz A. Kapsner Andrzej Liebert Hannes Schreiter Jessica Eberle Dominique Hadler Dominika Skwierawska Ralf Floca Peter Neher Balint Kovacs Evelyn Wenkel Sabine Ohlmeyer Michael Uder Klaus Maier-Hein Sebastian Bickelhaupt |
| author_sort | Dimitrios Bounias |
| collection | DOAJ |
| description | Abstract Prognosis for thoracic aortic aneurysms is significantly worse for women than men, with a higher mortality rate observed among female patients. The increasing use of magnetic resonance breast imaging (MRI) offers a unique opportunity for simultaneous detection of both breast cancer and thoracic aortic aneurysms. We retrospectively validate a fully-automated artificial neural network (ANN) pipeline on 5057 breast MRI examinations from public (Duke University Hospital/EA1141 trial) and in-house (Erlangen University Hospital) data. The ANN, benchmarked against 3D-ground-truth segmentations, clinical reports, and a multireader panel, demonstrates high technical robustness (dice/clDice 0.88-0.91/0.97-0.99) across different vendors and field strengths. The ANN improves aneurysm detection rates by 3.5-fold compared with routine clinical readings, highlighting its potential to improve early diagnosis and patient outcomes. Notably, a higher odds ratio (OR = 2.29, CI: [0.55,9.61]) for thoracic aortic aneurysms is observed in women with breast cancer or breast cancer history, suggesting potential further benefits from integrated simultaneous assessment for cancer and aortic aneurysms. |
| format | Article |
| id | doaj-art-ac677e5859244cedb971b95f57623b97 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-ac677e5859244cedb971b95f57623b972025-08-20T02:06:36ZengNature PortfolioNature Communications2041-17232025-06-0116111610.1038/s41467-025-59694-2AI-Based screening for thoracic aortic aneurysms in routine breast MRIDimitrios Bounias0Tobit Führes1Luise Brock2Johanna Graber3Lorenz A. Kapsner4Andrzej Liebert5Hannes Schreiter6Jessica Eberle7Dominique Hadler8Dominika Skwierawska9Ralf Floca10Peter Neher11Balint Kovacs12Evelyn Wenkel13Sabine Ohlmeyer14Michael Uder15Klaus Maier-Hein16Sebastian Bickelhaupt17German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280Radiologie München, Burgstraße 7Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3Abstract Prognosis for thoracic aortic aneurysms is significantly worse for women than men, with a higher mortality rate observed among female patients. The increasing use of magnetic resonance breast imaging (MRI) offers a unique opportunity for simultaneous detection of both breast cancer and thoracic aortic aneurysms. We retrospectively validate a fully-automated artificial neural network (ANN) pipeline on 5057 breast MRI examinations from public (Duke University Hospital/EA1141 trial) and in-house (Erlangen University Hospital) data. The ANN, benchmarked against 3D-ground-truth segmentations, clinical reports, and a multireader panel, demonstrates high technical robustness (dice/clDice 0.88-0.91/0.97-0.99) across different vendors and field strengths. The ANN improves aneurysm detection rates by 3.5-fold compared with routine clinical readings, highlighting its potential to improve early diagnosis and patient outcomes. Notably, a higher odds ratio (OR = 2.29, CI: [0.55,9.61]) for thoracic aortic aneurysms is observed in women with breast cancer or breast cancer history, suggesting potential further benefits from integrated simultaneous assessment for cancer and aortic aneurysms.https://doi.org/10.1038/s41467-025-59694-2 |
| spellingShingle | Dimitrios Bounias Tobit Führes Luise Brock Johanna Graber Lorenz A. Kapsner Andrzej Liebert Hannes Schreiter Jessica Eberle Dominique Hadler Dominika Skwierawska Ralf Floca Peter Neher Balint Kovacs Evelyn Wenkel Sabine Ohlmeyer Michael Uder Klaus Maier-Hein Sebastian Bickelhaupt AI-Based screening for thoracic aortic aneurysms in routine breast MRI Nature Communications |
| title | AI-Based screening for thoracic aortic aneurysms in routine breast MRI |
| title_full | AI-Based screening for thoracic aortic aneurysms in routine breast MRI |
| title_fullStr | AI-Based screening for thoracic aortic aneurysms in routine breast MRI |
| title_full_unstemmed | AI-Based screening for thoracic aortic aneurysms in routine breast MRI |
| title_short | AI-Based screening for thoracic aortic aneurysms in routine breast MRI |
| title_sort | ai based screening for thoracic aortic aneurysms in routine breast mri |
| url | https://doi.org/10.1038/s41467-025-59694-2 |
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