Scholarlink

Background <p>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.</p> Methods <p>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 (R<sup>2</sup>) 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.</p> Results <p>The optimal linear regression model, which incorporated both domain-expert and statistically selected features, achieved R<sup>2</sup> values of 81.8% for breast volume and 72% for breast weight.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月24日

查看全文
  • Sheng-Pu Teo,
  • Mee-Hoong See,
  • Lee-Lee Lai,
  • Lai-Kuan Wong,
  • Sidharrth Nagappan,
  • John See,
  • Kartini Rahmat,
  • Teng-Aik Ong,
  • Mas Ira Syafila Mohd Hilmi Tan,
  • Kwan-Hoong Ng

摘要

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.