A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

Zolfagharnasab, Hooshiar; Bessa, Silvia; Oliveira, Sara P.; Faria, Pedro; Teixeira, João F.; Cardoso, Jaime S.; Oliveira, Hélder

doi:10.3390/s18010167

Cited by 8 publications

(4 citation statements)

References 49 publications

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“…In order to overcome these flaws, a combination of model-based and data-based techniques can be used. Previously, Zolfagharnasab et al [ 31 ] created a hybrid prediction model to forecast breast shape after breast correction surgery. The slow speed of purely model-based prediction was overcome by training a data-based model to mimic the results from a model-based prediction method.…”

Section: Prediction Based Virtual Surgery Planningmentioning

confidence: 99%

Virtual Surgical Planning: Modeling from the Present to the Future

Singh

2021

JCM

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Virtual surgery planning is a non-invasive procedure, which uses digital clinical data for diagnostic, procedure selection and treatment planning purposes, including the forecast of potential outcomes. The technique begins with 3D data acquisition, using various methods, which may or may not utilize ionizing radiation, such as 3D stereophotogrammetry, 3D cone-beam CT scans, etc. Regardless of the imaging technique selected, landmark selection, whether it is manual or automated, is the key to transforming clinical data into objects that can be interrogated in virtual space. As a prerequisite, the data require alignment and correspondence such that pre- and post-operative configurations can be compared in real and statistical shape space. In addition, these data permit predictive modeling, using either model-based, data-based or hybrid modeling. These approaches provide perspectives for the development of customized surgical procedures and medical devices with accuracy, precision and intelligence. Therefore, this review briefly summarizes the current state of virtual surgery planning.

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Section: Prediction Based Virtual Surgery Planningmentioning

confidence: 99%

Virtual Surgical Planning: Modeling from the Present to the Future

Singh

2021

JCM

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“…Moreover, patients have difficulties projecting themselves on 3D models that are not in the position they see themselves in the mirror (upright position). The prediction of shape deformations of the breast following BCS has also attracted the interest of the artificial intelligence and computer vision communities which designed BMs [9] or trained machine learning algorithms [10] to show expected surgery outcomes on upright personalized 3D models of breast that the patient can relate to.…”

Section: Multimodal Registration and Validationmentioning

confidence: 99%

“…In [4,5], a multi-modal image registration combines MRI/3D surface scans into a patient-specific breast model that enables precise non-invasive pre-operative location of the tumor and provides a proper 3D upright representation for computer assisted surgery planning [10]. This two-step strategy comprises ICP for initial alignment of interior and surface data, and free form deformation (FFD) for the fine matching of the breast anterior surfaces in both representations.…”

Section: Multimodal Registration and Validationmentioning

confidence: 99%

Personalized 3D Breast Cancer Models with Automatic Image Segmentation and Registration

Bessa¹,

Teixeira²,

Carvalho³

et al. 2020

Proceedings of 3DBODY.TECH 2020 - 11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

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Breast conservative surgery (BCS) combined with radiotherapy has become the treatment of choice for the majority of women suffering from breast cancer. Every year approximately 2.1million new cases arise worldwide, but fortunately the 10-year survival rate now exceeds 80% mostly due to the early detection of non-palpable breast cancer. The accurate localization of the tumor is of utmost importance to decrease the re-excision rate and necessity of a second surgery, and also pivotal to the minimization of unaesthetic outcomes caused by these interventions. Nevertheless, 7 out of 10 patients will need invasive pre-operative localization and women will live long lives with the dire consequences of cancer treatments. Currently, it is up to the physicians to correlate multimodal 2-dimensial (2D) sectional images to the 3D space for surgical planning. The challenge is now focused on the technology to create an alternative non-invasive tumor location procedure that can be ethical and fit to the clinical set. This paper describes an automatic image segmentation and registration algorithm that fuses 3D optical scans of the breast surface, with interior radiological data for tumor characterization, to build a personalized digital breast model that can potentially be used as a non-invasive digital pre-operative localization technique, improving tumor visualization and surgery planning.

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“…Catanuto et al [37] in his review had discussed the role of decision trees in assisting the decisional process for primary breast surgery. Zolfagharnasab et al [38] had developed a regression model using decision tree to predict the deformation of breast shape after surgery. The usage of decision tree is evident in an article by Hanna et al [39] to evaluate the benefit of radiotherapy.…”

Section: Treatment Of Breast Cancermentioning

confidence: 99%

The use of Decision Tree in Breast Cancer-Related Research: a Scoping Analysis Based on Scopus-Indexed Articles

Badi’auzzaman¹,

Moey²,

Azemin³

et al. 2019

IJITEE

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Breast cancer is the leading cancer that occurs in women globally. The use of machine learning has been introduced to supplement the work in breast cancer studies. There are undisputed pieces of evidence of the existence of publications pertaining to the use of decision tree in breast cancer-related research. However, little is known regarding the types and frequencies of the searched articles. The main objective of this paper is to unearth the broad variety of articles related to breast cancer research that utilized decision trees. The Scopus database was chosen to examine the trend, frequencies and themes of the related publications from the year 2013 until 2018. The study was also intended to disclose the categories of articles based on the areas of breast cancer that have employed the decision trees method. A total of 259 articles from Scopus database were found to meet the inclusion criteria. The analysis of the frequency of published articles generally shows an upward trend. The majority of articles targeted diagnosis of breast cancer (37.8%) in comparisons with other categories. Even though the number of articles found is adequate, several categories of breast cancer are lacking in publications specifically the survivability, incidence, and recurrence of breast cancer among patients. There is a need to redirect the focus of breast cancer research on these categories for future efforts

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A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

Cited by 8 publications

References 49 publications

Virtual Surgical Planning: Modeling from the Present to the Future

Virtual Surgical Planning: Modeling from the Present to the Future

Personalized 3D Breast Cancer Models with Automatic Image Segmentation and Registration

The use of Decision Tree in Breast Cancer-Related Research: a Scoping Analysis Based on Scopus-Indexed Articles

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