Abstract

Abstract

Abstract

Abstract
Locally advanced breast cancer (LABC) constitutes a heterogeneous entity that includes advanced-stage primary tumours, cancers with extensive nodal involvement and inflammatory breast carcinomas. Although the definition of LABC can be broadened to include some large operable breast tumours, we use this term to strictly refer to inoperable cancers that are included in the above-mentioned categories. The prognosis of such tumours is often unfavourable; despite aggressive treatment, many patients eventually develop distant metastases and die from the disease. Advances in systemic therapy, including radiation treatment, surgical techniques and the development of new targeted agents have significantly improved clinical outcomes for patients with this disease. Notwithstanding these advances, LABC remains an important clinical problem, particularly in developing countries and those without widely adapted breast cancer awareness programmes. The optimal management of LABC requires a multidisciplinary approach, a well-coordinated treatment schedule and close cooperation between medical, surgical and radiation oncologists. In this Review, we discuss the current state of the art and possible future treatment strategies for patients with LABC.

Abstract
Common breast cancer treatments, as the removal of axillary lymph nodes, cause severe impairments in women's upper-body function. As a result, several daily activities are affected which contributes to a decreased QOL. Thus, the assessment of functional restrictions after treatment is essential to avoid further complications. This paper presents a pioneer work, which aims to develop an upper-body function evaluation method, traduced by the identification of lymphedema. Using the Kinect, features of the upper-limbs motion are extracted and supervised learning algorithms are used to construct a predictive classification model. Very promising results are obtained, with high classification accuracy.

Abstract
Common breast cancer treatment techniques, such as radiation therapy or the surgical removal of the axillary lymphatic nodes, result in several impairments in women's upper-body function. These impairments include restricted shoulder mobility and arm swelling. As a consequence, several daily life activities are affected, which contribute to a decreased quality of life (QOL). Therefore, it is of extreme importance to assess the functional restrictions caused by cancer treatment, in order to evaluate the quality of procedures and to avoid further complications. Although the research in this field is still very limited and the methods currently available suffer from a lack of objectivity, this highlights the relevance of the pioneer work presented in this paper, which aims to develop an effective method for the evaluation of the upper-body function, suitable for breast cancer patients. For this purpose, the use of both depth and skeleton data, provided by the Microsoft Kinect, is investigated to extract features of the upper-limbs motion. Supervised classification algorithms are used to construct a predictive model of classification, and very promising results are obtained, with high classification accuracy.