ESTRO 2024 - Abstract Book

S5060

Physics - Radiomics, functional and biological imaging and outcome prediction

ESTRO 2024

Conclusion:

In conclusion, this research suggests that utilizing digital mammographic images for TNBC subtyping, which can subtype at zero cost within minutes, shows promise in potentially improving diagnosis and treatment precision, pending further validation. This approach compares favorably to the methods employed in clinical classification.

Keywords: triple-negative breast cancer , deep-learning

References:

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2.Jiang, M. et al. Insights into the theranostic value of precision medicine on advanced radiotherapy to breast cancer. International Journal of Medical Sciences 18, 626 (2021).

3.Boulenger, A. et al. Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images. Medical & Biological Engineering & Computing 61, 567-578 (2023).

4.Yala, A., Lehman, C., Schuster, T., Portnoi, T. & Barzilay, R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292, 60-66 (2019).

5.Sammut, S.-J. et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature 601, 623-629 (2022).