Giuseppe Catanuto1,2, Wafa Taher3, Nicola Rocco4, Francesca Catalano1, Dario Allegra5, Filippo Luigi Maria Milotta5, Filippo Stanco5, Giovanni Gallo5, Maurizio Bruno Nava2. 1. U.O.C. Multidisciplinare di Senologia, Azienda Ospedaliera Cannizzaro Catania, Catania, Italy. 2. Group for Reconstructive and Therapeutic Advancements, Milano-Napoli-Catania, Italy. 3. Fellow of the International Fellowship Mr G. Querci della Rovere and Locum Consultant, Oxford University Hospital NHS Foundation Trust, Oxford, UK. 4. Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy. 5. Dipartimento di Matematica e Informatica, Università degli Studi di Catania, Catania, Italy.
Abstract
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: The authors quantitatively described breast shape with two parameters derived from a statistical methodology denominated by principal component analysis (PCA). Methods: The authors created a heterogeneous dataset of breast shapes acquired with a commercial infrared 3-dimensional scanner on which PCA was performed. The authors 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 posttreatment surgical case and test-retest was performed by two operators. Results: The first two principal components derived from PCA characterize the shape of the breast included in the dataset. The test-retest demonstrated that different operators obtain very similar values of PCA. The system is also able to identify major changes in the preoperative and posttreatment 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.
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: The authors quantitatively described breast shape with two parameters derived from a statistical methodology denominated by principal component analysis (PCA). Methods: The authors created a heterogeneous dataset of breast shapes acquired with a commercial infrared 3-dimensional scanner on which PCA was performed. The authors 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 posttreatment surgical case and test-retest was performed by two operators. Results: The first two principal components derived from PCA characterize the shape of the breast included in the dataset. The test-retest demonstrated that different operators obtain very similar values of PCA. The system is also able to identify major changes in the preoperative and posttreatment 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.
Authors: R M Lacher; F Vasconcelos; N R Williams; G Rindermann; J Hipwell; D Hawkes; D Stoyanov Journal: Med Image Anal Date: 2019-01-11 Impact factor: 8.545