Predicting "Ideal" Breast Proportionality to Support Implant Selection in Breast Augmentation

The 45:55 upper-to-lower pole breast proportion described by Mallucci and Branford is widely cited as an aesthetic reference in breast augmentation. Anticipating whether this proportional relationship will be achieved preoperatively remains challenging, as postoperative breast shape is influenced by implant geometry, tissue characteristics, and lower pole deformation rather than implant volume alone.

A regression-informed, proportion-based decision-support framework was developed using standard preoperative breast measurements and implant specifications. The model incorporates implant-breast dimensional matching through a composite geometric index (R_ib ratio) and a regression-derived estimate of postoperative nipple-to-inframammary crease elongation to predict postoperative breast proportionality relative to the 45:55 target.

A stepwise clinical algorithm was constructed to allow comparative assessment of candidate implant options across a clinically relevant range. Predicted proportional outcomes are expressed as nipple position relative to total vertical breast height and classified using a red-amber-green framework to support structured preoperative planning and patient counselling.

The proposed framework provides a structured method for preoperative assessment of implant-related proportional risk that extends beyond volume-based planning alone. By translating established morphometric relationships into a reproducible decision-support model, it facilitates transparent comparison of implant options while acknowledging the inherent variability of soft tissue response and the continued importance of clinical judgement. The model has not been prospectively validated and is not intended as a substitute for clinical judgement; it functions as a proportional reasoning aid to support, not replace, surgical decision-making.

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .