Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm.

Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportiona...

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Published in:BioMed Research International pp. 1 - 13
Main Authors: Chuang, Li-Yeh, Chen, Guang-Yu, Moi, Sin-Hua, Ou-Yang, Fu, Hou, Ming-Feng, Yang, Cheng-Hong
Format: algorithm equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 4/1/2019
Online Access:View this record in EBSCOhost
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      dt: 4/1/2019
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2019/2304128
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        atl: Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm.
      aug:
        au:
          Chuang, Li-Yeh
          Chen, Guang-Yu
          Moi, Sin-Hua
          Ou-Yang, Fu
          Hou, Ming-Feng
          Yang, Cheng-Hong
        affil: Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
      sug:
        subj:
          Breast Neoplasms Pathology
          Breast Neoplasms Prognosis
          Algorithms
          Disease Attributes
          Human
          Survival
          Cox Proportional Hazards Model
          Academic Medical Centers
          Breast Neoplasms Surgery
          Neoplasms Pathology
          Lymph Nodes Pathology
          Odds Ratio
          Neoplasm Metastasis
          Neoplasm Invasiveness
          Peripheral Nervous System Neoplasms
          Skin Neoplasms
          Mastectomy Methods
          Hormone Therapy
          Chemotherapy, Cancer
      ab: Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.
      pubtype: Academic Journal
      doctype:
        algorithm
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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