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Can I read Predicting Breast Cancer Types on and Beyond Molecular Level in a Multi-modal Fashion on EtoBox?
Predicting Breast Cancer Types on and Beyond Molecular Level in a Multi-modal Fashion by Tianyu Zhang; Tao Tan; Luyi Han; Linda Appelman; Jeroen Veltman; Ronni Wessels; Katya M. Duvivier; Claudette Loo; Yuan Gao; Xin Wang; Hugo M. Horlings; Regina G. H. Beets-Tan; Ritse M. Mann is a Medicine article available to read on EtoBox.
What is Predicting Breast Cancer Types on and Beyond Molecular Level in a Multi-modal Fashion about?
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 th
Who reads Predicting Breast Cancer Types on and Beyond Molecular Level in a Multi-modal Fashion?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Tianyu Zhang; Tao Tan; Luyi Han; Linda Appelman; Jeroen Veltman; Ronni Wessels; Katya M. Duvivier; Claudette Loo; Yuan Gao; Xin Wang; Hugo M. Horlings; Regina G. H. Beets-Tan; Ritse M. Mann
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Medicine (Health Sciences)