Abstract
Unilateral breast reconstruction is different from bilateral breast reconstruction and breast augmentation, as it requires more consideration of the symmetry between the reconstructed breast and the contralateral breast. However, there are currently no implants specifically targeted to unilateral breast reconstruction, nor is there is objective classification criteria for breast appearance type. A total of 212 breasts from 153 patients were measured. Breast measurements, breast surface curvature distributions, and elliptic Fourier coefficients were used to describe breast features. We objectively classified the appearance of breasts using machine learning and used the resulting cluster centers to construct the most adaptive implants for each type of breast. All of the breasts clustered into 4 types. The implants corresponding to each type of breast were constructed using cluster centers. The resulting cluster centers were then used to choose a suitable implant for patients requiring unilateral breast reconstruction. Contour coefficients were used to evaluate the clustering results, with an average score of 0.53. The two breasts that develop normally in the same person were treated as the same class. The score obtained after statistical classification was 0.47. These results demonstrate that our proposed method can improve the classification of breasts of different shapes. This method provides a foundation for improving the symmetry of unilateral breast reconstruction and the construction and selection of implants.
Highlights
Breast cancer is the most common cancer among women
In order to overcome the limitations of current methods, we propose a new breast implant construction and breast classification method for unilateral breast reconstruction
We propose a method for breast shape classification and for constructing the most representative breast implants for each type of breast
Summary
Breast cancer is the most common cancer among women. According to the latest World Cancer Report (2014), in 2012, there were 1,700,000 newly diagnosed cases of breast cancer. For unilateral breast reconstruction following mastectomy, we believe it is necessary to propose an objective method for constructing and selecting breast implants based on a patient’s healthy breast. Melchels et al used CAD/CAM technology to create lifesize, patient-specific customized molds to aid in autologous breast reconstruction [18]. Current computer-aided personalized breast reconstruction and implant customization require that converts laser scan data, CT or MRI data into CAD files. Existing methods require customized breast model or scaffold for each patient. They are complicated and time-consuming to implement. In order to overcome the limitations of current methods, we propose a new breast implant construction and breast classification method for unilateral breast reconstruction. Since the appearance of the breast has no objective label to evaluate the classification results, we innovatively classify the breasts on both sides of the same person into the same class, which can be regarded as a natural label of the breast appearance for evaluating the classification results
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