Predicting Factors Affecting Lymph Node Involvement in Breast Cancer Using Random Forest Approaches.
Objectives: The objective of this study was to utilize random forest methodology to develop a practical diagnostic function for predicting lymph node metastasis in patients diagnosed with breast cancer. Methods: The research data of this retrospective cohort study was obtained through a comprehensiv...
| Publicado en: | International Journal of Cancer Management Vol. 17; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Medical Journals Commission of the Ministry of Health & Medical Education
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182175707&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182175707 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 25384422 L6AI jtl: International Journal of Cancer Management issn: 25384422 maglogo: N pubinfo: dt: Dec2024 vid: 17 iid: 1 pid: 66482 pub: Medical Journals Commission of the Ministry of Health & Medical Education artinfo: ui: 182175707 182175707 182175707 10.5812/ijcm-140283 182175707 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Predicting Factors Affecting Lymph Node Involvement in Breast Cancer Using Random Forest Approaches. aug: au: Zamaninasab, Fatemeh Fendereski, Afsaneh Zamaninasab, Zahra Godazandeh, Gholamali Charati, Jamshid Yazdani affil: Biostatistics and Epidemiology Department, Faculty of Health, Mazandaran University of Medical Sciences, Sari, Iran sug: subj: Breast Neoplasms Risk Factors Lymph Nodes Pathology Neoplasm Metastasis Risk Factors Random Forest Utilization Machine Learning Human Female Adult Middle Age Retrospective Design Record Review Cancer Patients Adult: 19-44 years Middle Aged: 45-64 years Female ab: Objectives: The objective of this study was to utilize random forest methodology to develop a practical diagnostic function for predicting lymph node metastasis in patients diagnosed with breast cancer. Methods: The research data of this retrospective cohort study was obtained through a comprehensive analysis of telephone interviews and medical records of 241 patients with breast cancer referred to the hospitals affiliated with Mazandaran University of Medical Sciences between 2016 and 2022. The data analysis method used in this study was random forest analysis to identify the influential factors associated with lymph node metastasis using R software. Results: The mean age of diagnosis for patients was 52.03 ± 10.932. Based on the random forest analysis outcomes, an accuracy rate of 72.2% has been attained. The influential factors in our study included grade, tubule formation, skin involvement, p53 marker, margin involvement, nuclear pleomorphism, Ki67, tumor location, estrogen receptor (ER), and (progesterone receptor) PR markers. These factors were determined to have a significant impact based on the mean accuracy reduction index. Furthermore, the variables that demonstrated significance based on the mean Gini reduction index included age, grade, tubule formation, tumor size, nuclear pleomorphism, disease level, mitosis, skin involvement, tumor location, and margin involvement. Conclusions: The utilization of the random forest algorithm, which demonstrates a favorable level of discriminative capability, may serve as a suitable approach for predicting metastasis in patients with breast cancer. Furthermore, by identifying these factors, experts can employ effective strategies to mitigate the condition. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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