Published May 10, 2026 | Version v2

Estimating Mastectomy Weight From Standard Breast Measurements, Bmi, Sister Bra Size: A Simple Clinical Formula

Description

Purpose: Accurately estimating breast weight is essential for surgical planning, particularly in mastectomy and immediate reconstruction. Current prediction methods are often impractical in routine clinical settings due to cost, complexity, or accessibility. We sought to derive and validate a simple formula that can be utilized by both patients and providers to estimate mastectomy specimen weight using standard breast measurements, BMI, and sister bra size. Methods: We performed a retrospective review of 177 mastectomy patients (333 breasts) from UCLA. Data collected included nipple-to-inframammary fold (N-IMF) distance, sternal notch-to-nipple (SN-N) distance, body mass index (BMI), Regnault ptosis grade, and sister bra size (SS). Spearman and Pearson correlation tests were conducted to identify terms with strong predictive strength. A formula with three strong predictive terms was derived using linear regression. Results: The three terms included in the predictive equation were BMI x N-IMF (ρ = 0.857; p < 0.001), SS x N-IMF (ρ = 0.832; p < 0.001), and SN-N 2 (ρ = 0.72; p < 0.001). The final formula, which was Specimen Weight (g) = 1.32 (BMI x N-IMF) + 2.0 (SS x N-IMF) + 0.13 x (SN-N) 2 - 32 demonstrated strong predictive performance (R = 0.783; RMSE = 119 g). Conclusions: We present a simple formula that accurately estimates mastectomy specimen weight using BMI, N-IMF distance, SN-N distance, and SS. This is the first formula incorporating SS to estimate mastectomy specimen weight, validated across multiple mastectomy types. This surgeon-friendly tool, requiring only four routine variables, enables accurate and rapid weight estimation to guide implant selection, flap choice, and patient counseling, offering a practical alternative to imaging-based methods.© 2026. Plastic Surgery Research Council | All rights reserved |*Source: https://ps-rc.org/meeting/Program/2026/OS06.cgi*

Notes

Abstract ID: OS06

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