Two Numbers one shape.

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

Catanuto G, Taher W, Rocco N, Catalano F, Allegra D, Milotta FLM, Stanco F, Gallo G, Nava MB.
Aesthet Surg J. 2018 Mar 20. doi: 10.1093/asj/sjy070.
Current research on outcomes of breast surgery have had a solid development on the side of subjective evaluation across the last few years. PROMs are now well-established instruments and part of the most important studies on results of cosmetic, reconstructive and oncological surgery of the breast. However we believe that objective measures should still be available as this may reflect much better technical capabilities, failures or improvements related to new devices or surgical technique and experience. Objective evaluations cannot be biased by external factors such as interaction among patients and doctors or breast care nurses, or ethnical, socio-economic or health related conditions. Shape transformation is part of breast surgery and the breast itself is a three-dimensional curved surface that requires appropriate tools for assessment. With this study we propose a simple statistic methodology based on curvature assessment that is able to identify each shape using just two numbers. This was demonstrated in a complex case of a patient who had breast reconstruction with tissue expansion on the right side as a secondary procedure after wide local excision with positive margins. Tissue expansion was not entirely satisfactory (PCA1 = 0.0098 and PCA2 = 0.0281) and markedly asymmetrical compared to the contralateral hypoplastic breast (PCA1 = 0.12 and PCA2 = 0.0198).
After the second reconstructive stage, the two breasts still retained different shapes despite after breast augmentation on the healthy side reduced the distance between the two breasts. Thus the system identified morphological changes induced by surgical operations with new PCA valuese. (posttreatment values LEFT: PCA1 = 0.0124; PCA2 = 0.0352; RIGHT: PCA1 = 0.0273; PCA2 = 0.0843).
This methodology, in our opinion may replace complex estimates based on linear measurements or angles, and creates a quantitative language that will replace qualitative evaluation (big, bigger, full, sweet slope etc). Developments may include an integration of shape analysis with PCA into PROMs in order to anticipate results in quality of life according to shapes either pre-operatively or post-op.

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