AI-Based screening for thoracic aortic aneurysms in routine breast MRI.
Authors
Affiliations (11)
Affiliations (11)
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280, Heidelberg, Germany.
- Medical Faculty Heidelberg, Heidelberg University, Im Neuenheimer Feld 672, Heidelberg, Germany.
- Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, Erlangen, Germany.
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, Erlangen-Tennenlohe, Germany.
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), Im Neuenheimer Feld 280, Heidelberg, Germany.
- German Cancer Consortium (DKTK), Partner Site Heidelberg, Im Neuenheimer Feld 280, Heidelberg, Germany.
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, Heidelberg, Germany.
- Radiologie München, Burgstraße 7, München, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany.
- Radiological Institute, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, Erlangen, Germany. [email protected].
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.