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Predicting Resection Weights of Reduction Mammaplasty: A Multi-Institutional Retrospective Analysis Using Machine

Devin J Clegg¹, Stefanos Boukovalas², Brett Beaulieu-Jones³

¹From the Department of Surgery.

Plastic and Reconstructive Surgery

|June 17, 2025

Summary

This summary is machine-generated.

Machine learning and regression models accurately predict reduction mammaplasty resection weights, outperforming the Schnur Scale in a multi-institutional study. These advanced models offer improved accuracy for predicting breast resection weights.

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Area of Science:

Plastic Surgery
Medical Informatics
Biostatistics

Background:

Previous single-institution study showed machine learning (ML) accurately predicted reduction mammaplasty (RM) resection weights using preoperative anthropometric variables.
The Schnur Scale is a current standard for predicting resection weights, but its accuracy and generalizability may be limited.

Purpose of the Study:

To evaluate ML and regression modeling for predicting RM resection weights in a diverse, multi-institutional patient population.
To compare the accuracy of ML and regression models against the Schnur Scale for predicting individual and total breast resection weights.

Main Methods:

A multi-institutional retrospective study included 635 patients undergoing RM for macromastia (2017-2022).
Preoperative variables included body surface area (BSA), body mass index (BMI), sternal notch-to-nipple (SN-N), and nipple-to-inframammary fold (N-IMF).
Seven ML and regression models were assessed for predicting resection weights, with mean absolute errors (MAE) reported.

Main Results:

The study population had a mean age of 38.5 years, mean BMI of 32.8 kg/m², and mean BSA of 2.0 m².
Preoperative BMI, SN-N, N-IMF, and race/ethnicity were significant predictors of resection weight.
Six of seven models showed lower MAEs than the Schnur Scale; Elastic Net regression yielded the lowest MAEs for individual and total resection weights.

Conclusions:

ML and regression models demonstrate superior accuracy in predicting RM resection weights compared to the Schnur Scale.
These findings support ML and regression modeling as accurate and generalizable alternatives to the Schnur Scale in a multi-institutional setting.