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Interrogating a multifactorial model of breast conserving therapy with clinical data.
Remi Salmon1, Marc Garbey2, Linda W Moore3
1Department of Computer Science, University of Houston, Houston, TX, USA.
Plos One
|April 24, 2015
Summary
Breast conserving therapy (BCT) can achieve good cosmetic outcomes, but predicting post-surgical breast contour remains challenging. This study proposes a multiscale modeling approach to improve predictions for better patient decision-making.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Oncology
Background:
- Breast conserving therapy (BCT), including lumpectomy and radiation, is a common treatment for early-stage breast cancer, preserving the breast in up to 70% of patients.
- Suboptimal cosmetic outcomes occur in approximately 30% of patients post-BCT, impacting patient satisfaction and decision-making.
Observation:
- The final breast contour after BCT is influenced by a complex interplay of mechanical forces, tissue properties, radiation-induced inflammation, and healing processes.
- Even in cases with excellent cosmetic results, predicting breast contour requires sophisticated multiscale modeling.
Findings:
- A novel multiscale modeling method is proposed to predict breast contour after BCT.
- The approach aims to identify dominant, patient-specific parameters for different healing phases to enhance prediction accuracy.
Implications:
- Accurate prediction of breast contour can significantly improve patient decision-making regarding breast cancer surgical options.
- This research contributes to optimizing BCT outcomes and enhancing patient quality of life.
- The study is part of a clinical trial (NCT02310711) for further validation.
