Evidence map›Paper›PMID 31058185›Full record

ArticleBioMed research international2019

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

Li-Yeh Chuang, Guang-Yu Chen, Sin-Hua Moi, Fu Ou-Yang, Ming-Feng Hou, Cheng-Hong Yang

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Article in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Li-Yeh ChuangDepartment of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan.ORCID 0000-0002-9817-5102
Guang-Yu ChenDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Sin-Hua MoiDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.ORCID 0000-0002-4082-1909
Fu Ou-YangDivision of Breast Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Ming-Feng HouDivision of Breast Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.ORCID 0000-0001-6030-9702
Cheng-Hong YangDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.ORCID 0000-0002-2741-0072

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Disease ManagementPrognosisAlgorithmsBreast NeoplasmsDisease-Free SurvivalErb-b2 Receptor Tyrosine KinasesEstrogen Receptor alphaFemaleHormone Replacement TherapyHumansLymphatic MetastasisMastectomyMiddle AgedNeoplasm InvasivenessPhylogeographyReceptors, ProgesteroneERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesESR1 protein, humanEstrogen Receptor alphaReceptors, Progesterone

Identifiers

PMID31058185
PMCPMC6463600

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