| Home > Publications database > AI-Based screening for thoracic aortic aneurysms in routine breast MRI. |
| Journal Article | DKFZ-2025-01210 |
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
2025
Springer Nature
[London]
This record in other databases:

Please use a persistent id in citations: doi:10.1038/s41467-025-59694-2
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.
Keyword(s): Humans (MeSH) ; Female (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Aortic Aneurysm, Thoracic: diagnostic imaging (MeSH) ; Aortic Aneurysm, Thoracic: diagnosis (MeSH) ; Breast Neoplasms: diagnostic imaging (MeSH) ; Middle Aged (MeSH) ; Neural Networks, Computer (MeSH) ; Retrospective Studies (MeSH) ; Breast: diagnostic imaging (MeSH) ; Aged (MeSH) ; Male (MeSH) ; Mass Screening: methods (MeSH) ; Adult (MeSH)
|
The record appears in these collections: |