Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies.
Background: Approximately 10%-15% of patients with breast cancer die of cancer metastasis or recurrence, and early diagnosis of it can improve prognosis. Breast cancer outcomes may be prognosticated on the basis of surface markers of tumor cells and serum tests. However, evaluation of a combination...
| Publicado en: | International Journal of Medical Informatics Vol. 128; pp. 79 - 87 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Aug2019
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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=136745143&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136745143 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Aug2019 vid: 128 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 136745143 136745143 NLM31103449 136745143 10.1016/j.ijmedinf.2019.05.003 NLM31103449 136745143 ppf: 79 ppct: 8 formats: tig: atl: Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies. aug: au: Tseng, Yi-Ju Huang, Chuan-En Wen, Chiao-Ni Lai, Po-Yin Wu, Min-Hsien Sun, Yu-Chen Wang, Hsin-Yao Lu, Jang-Jih affil: Department of Information Management, Chang Gung University, Taiwan sug: subj: Breast Neoplasms Algorithms Breast Neoplasms Blood Prospective Studies Human Middle Age Probability Female Prognosis ROC Curve Validation Studies Comparative Studies Evaluation Research Multicenter Studies Memorial Pain Assessment Card Middle Aged: 45-64 years Female ab: Background: Approximately 10%-15% of patients with breast cancer die of cancer metastasis or recurrence, and early diagnosis of it can improve prognosis. Breast cancer outcomes may be prognosticated on the basis of surface markers of tumor cells and serum tests. However, evaluation of a combination of clinicopathological features may offer a more comprehensive overview for breast cancer prognosis.Materials and Methods: We evaluated serum human epidermal growth factor receptor 2 (sHER2) as part of a combination of clinicopathological features used to predict breast cancer metastasis using machine learning algorithms, namely random forest, support vector machine, logistic regression, and Bayesian classification algorithms. The sample cohort comprised 302 patients who were diagnosed with and treated for breast cancer and received at least one sHER2 test at Chang Gung Memorial Hospital at Linkou between 2003 and 2016.Results: The random-forest-based model was determined to be the optimal model to predict breast cancer metastasis at least 3 months in advance; the correspondingarea under the receiver operating characteristic curve value was 0. 75 (p < 0. 001).Conclusion: The random-forest-based model presented in this study may be helpful as part of a follow-up intervention decision support system and may lead to early detection of recurrence, early treatment, and more favorable outcomes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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