Abstract
This work presents a cluster analysis approach aiming to determine distinct groups based on clinicopathological data from patients with breast cancer (BC). For this purpose, the clinical variables were considered: age at diagnosis, weight, height, lymph nodal invasion (LN), tumor-node-metastasis (TNM) staging and body mass index (BMI). Ward's hierarchical clustering algorithm was used to form specific groups. Based on this, BC patients were separated into four groups. The Kruskal-Wallis test was performed to assess the differences among the clusters. The intensity of the influence of variables on the prognosis of BC was also evaluated by calculating the Spearman's correlation. Positive correlations were obtained between weight and BMI, TNM and LN invasion in all analyzes. Negative correlations between BMI and height were obtained in some of the analyzes. Finally, a new correlation was obtained, based on this approach, between weight and TNM, demonstrating that the trophic-adipose status of BC patients can be directly related to disease staging.
Highlights
The growing number of existing diseases, and its several clinicopathological features, have led us to the need to provide tools that help clinicians to solve how to proceed in specific situations
Cluster analysis is a multivariate statistical tool used for the construction and classification of groups according to the characteristics of the variables, so that the groups are heterogeneous with each other, but the elements within each group have homogeneous characteristics (CHATFIELD et al 1980; JOHNSON et al, 2007; MADHULATHA, 2012; RODRIGUEZ et al, 2019)
The clusters are combined until the end of the process, when a single cluster is obtained with all elements
Summary
The growing number of existing diseases, and its several clinicopathological features, have led us to the need to provide tools that help clinicians to solve how to proceed in specific situations. It is necessary to join efforts from different areas of knowledge to generate results that can serve as a basis for some type of improvement in the diagnosis, treatment or remission of the disease (CARELS et al, 2016; FRANCESCHINI et al, 2013). Advances in the last decade have improved disease diagnosis for several chronic diseases, especially cancer. Breast cancer mortality rate has decreased worldwide, many women are still dying, in spite of treatment advances. This fact indicates that the current information used by clinicians to stratify patients and make decisions regarding their treatment are not enough.
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