A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Hooshiar Zolfagharnasab (Author) Other; Sílvia Bessa (Author) Other; Sara P. Oliveira (Author) Other; Pedro Faria (Author) Other; João F. Teixeira (Author) Other; Jaime S. Cardoso (Author) FEUP; Hélder P. Oliveira (Author) FCUP

Journal

Title: Sensors

Vol. 18 No. 1

Pages: 1-29

ISSN: 1424-3210

Publisher: MDPI

Indexing

ISI Web of Knowledge - 8 Citations

Scopus - 10 Citations

INSPEC

Other information

Authenticus ID: P-00N-CSX

DOI: 10.3390/s18010167

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 29