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Sensors 2018, 18(1), 167; https://doi.org/10.3390/s18010167

A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

1
INESC TEC, 4200-465 Porto, Portugal
2
Departamento de Engenharia Eletrotécnica e de Computadores, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
3
Departamento de Engenharia Informática, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
4
Departamento de Ciência de Computadores, Faculdade de Ciência da Universidade do Porto, 4169-007 Porto, Portugal
*
Authors to whom correspondence should be addressed.
Received: 7 November 2017 / Revised: 3 January 2018 / Accepted: 5 January 2018 / Published: 9 January 2018
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Abstract

Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained. View Full-Text
Keywords: regression model; Random Forests; breast cancer; breast conserving surgery; breast deformation; shape prediction regression model; Random Forests; breast cancer; breast conserving surgery; breast deformation; shape prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zolfagharnasab, H.; Bessa, S.; Oliveira, S.P.; Faria, P.; Teixeira, J.F.; Cardoso, J.S.; Oliveira, H.P. A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery. Sensors 2018, 18, 167.

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