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Predicting breast cancer types on and beyond molecular level in a multi-modal fashion
- Source :
- npj Breast Cancer, Vol 9, Iss 1, Pp 1-12 (2023)
- Publication Year :
- 2023
- Publisher :
- Nature Portfolio, 2023.
-
Abstract
- Abstract Accurately determining the molecular subtypes of breast cancer is important for the prognosis of breast cancer patients and can guide treatment selection. In this study, we develop a deep learning-based model for predicting the molecular subtypes of breast cancer directly from the diagnostic mammography and ultrasound images. Multi-modal deep learning with intra- and inter-modality attention modules (MDL-IIA) is proposed to extract important relations between mammography and ultrasound for this task. MDL-IIA leads to the best diagnostic performance compared to other cohort models in predicting 4-category molecular subtypes with Matthews correlation coefficient (MCC) of 0.837 (95% confidence interval [CI]: 0.803, 0.870). The MDL-IIA model can also discriminate between Luminal and Non-Luminal disease with an area under the receiver operating characteristic curve of 0.929 (95% CI: 0.903, 0.951). These results significantly outperform clinicians’ predictions based on radiographic imaging. Beyond molecular-level test, based on gene-level ground truth, our method can bypass the inherent uncertainty from immunohistochemistry test. This work thus provides a noninvasive method to predict the molecular subtypes of breast cancer, potentially guiding treatment selection for breast cancer patients and providing decision support for clinicians.
- Subjects :
- Neoplasms. Tumors. Oncology. Including cancer and carcinogens
RC254-282
Subjects
Details
- Language :
- English
- ISSN :
- 23744677
- Volume :
- 9
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- npj Breast Cancer
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.fe8d7f10afa0492b927494114288cff3
- Document Type :
- article
- Full Text :
- https://doi.org/10.1038/s41523-023-00517-2