Machine learning models for volume and weight estimation in breast reconstruction planning

Background
Accurate estimation of breast volume and weight is critical for post-mastectomy reconstruction. Existing methods are frequently costly or complex. We developed a machine learning framework that leverages demographic and anthropometric data to address these challenges.

Methods
We collected data from 199 patients between 2021 and 2023. The workflow comprised data collection, pre-processing, feature selection, model training, and performance evaluation. Three feature selection techniques were applied: domain expert knowledge, Spearman's rank correlation, and the Boruta algorithm. Each feature set was used to train linear regression, random forest regression, and support vector regression models. Model performance was evaluated using the coefficient of determination (R2) and Pearson's correlation coefficient. Significant correlations were identified between breast volume or weight and key patient characteristics, such as BMI, breast cup size, ptosis severity, and anthropometric measurements.

Results
The optimal linear regression model, which incorporated both domain-expert and statistically selected features, achieved R2 values of 81.8% for breast volume and 72% for breast weight.

Conclusion
The results indicate that integrating demographic and anthropometric data with machine learning yields an accurate, interpretable, and accessible method for preoperative breast assessment. In contrast to conventional imaging or mathematical models, this approach eliminates costs related to imaging equipment, relies on routinely collected clinical data, reduces the need for specialized equipment and training, and enables rapid integration into existing clinical workflows. By overcoming the limitations of traditional methods, the proposed model provides a practical, efficient, and cost-effective solution for clinical practice.