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Body image in women with breast cancer undergoing surgical treatment - a comparative analysis

Brandt-Salmeri Anna, Ilska Michalina, Kołodziej-Zaleska Anna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2019

z. 141

7--24

Surgery is the most basic treatment in case of breast cancer: it involves a complete or partial removal of the mammary gland. The aim of the study was to assess the body image distress and self-esteem in a group of women with breast cancer undergoing various surgical procedures. The material was collected in a group of 229 women with breast cancer who were divided into subgroups based on the surgery criterion (mastectomy, breast-conserving therapy - BCT and mastectomy with breast reconstruction). The study used the Body Image Scale by Hopwood, Fletcher, Lee and Al Ghazal (2001; Polish adaptation by Brandt-Salmeri and Przybyła-Basista), Rosenberg Self-Esteem Scale - SES (Polish adaptation by Łaguna, Lachowicz-Tabaczek and Dzwonkowka, 2007) and an original survey. Analyses showed, among other things, significant differences in the assessment of discomfort associated with a change in body image depending on the type of surgery. The research also revealed that the assessment depended on differences between the women in terms of age and the time elapsed since the onset of treatment. Negative body image was adversely associated with self-esteem in all studied groups. Body image was significantly related to age and time elapsed since the treatment in the post-mastectomy group. At the same time, it was related only to age in the BCT group and with regards to the breast reconstruction group, the relationship concerned only elapsed time.

BCT Boost Segmentation with U-net in TensorFlow

Wieczorek Grzegorz, Antoniuk Izabella, Kurek Jarosław, Świderski Bartosz, Kruk Michał, Pach Jakub, Orłowski Arkadiusz

Machine Graphics and Vision

2019

Vol. 28, No. 1/4

25--34

In this paper we present a new segmentation method meant for boost area that remains after removing the tumour using BCT (breast conserving therapy). The selected area is a region on which radiation treatment will later be made. Consequently, an inaccurate designation of this region can result in a treatment missing its target or focusing on healthy breast tissue that otherwise could be spared. Needless to say that exact indication of boost area is an extremely important aspect of the entire medical procedure, where a better definition can lead to optimizing of the coverage of the target volume and, in result, can save normal breast tissue. Precise definition of this area has a potential to both improve the local control of the disease and to ensure better cosmetic outcome for the patient. In our approach we use U-net along with Keras and TensorFlow systems to tailor a precise solution for the indication of the boost area. During the training process we utilize a set of CT images, where each of them came with a contour assigned by an expert. We wanted to achieve a segmentation result as close to given contour as possible. With a rather small initial data set we used data augmentation techniques to increase the number of training examples, while the final outcomes were evaluated according to their similarity to the ones produced by experts, by calculating the mean square error and the structural similarity index (SSIM).