Breast Shape Analysis With Curvature Estimates and Principal Component Analysis for Cosmetic and Reconstructive Breast Surgery.

Background: Breast shape is defined utilizing mainly qualitative assessment (full, flat, ptotic) or estimates, such as volume or distances between reference points, that cannot describe it reliably.

Objectives: We will quantitatively describe breast shape with two parameters derived from a statistical methodology denominated principal component analysis (PCA).

Methods: We created a heterogeneous dataset of breast shapes acquired with a commercial infrared 3-dimensional scanner on which PCA was performed. We plotted on a Cartesian plane the two highest values of PCA for each breast (principal components 1 and 2). Testing of the methodology on a preoperative and postoperative surgical case and test-retest was performed by two operators.

Results: The first two principal components derived from PCA are able to characterize the shape of the breast included in the dataset. The test-retest demonstrated that different operators are able to obtain very similar values of PCA. The system is also able to identify major changes in the preoperative and postoperative stages of a two-stage reconstruction. Even minor changes were correctly detected by the system.

Conclusions: This methodology can reliably describe the shape of a breast. An expert operator and a newly trained operator can reach similar results in a test/re-testing validation. Once developed and after further validation, this methodology could be employed as a good tool for outcome evaluation, auditing, and benchmarking.