Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridAkbulut, Sami/0000-0002-6864-7711
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.authorwosidAkbulut, Sami/L-9568-2014
dc.contributor.authorAkbulut, Sami
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorColak, Cemil
dc.date.accessioned2024-08-04T20:10:35Z
dc.date.available2024-08-04T20:10:35Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIntroduction: Despite significant advances in breast cancer (BC) management, the prognosis for most patients with distant metastasis remains poor. We predicted distant metastasis in BC patients with artificial intelligence (AI) methods based on genomic biomarkers. Methods: The dataset used in the study included 97 patients with BC, of whom 46 (47%) developed distant metastases, and 51 (53%) did not develop distant metastases, and the expression level of 24,481 genes of these patients. An approach combining Boruta + LASSO methods was applied to identify biomarker genes associated with BC distant metastasis. Mann-Whitney U test was used to examine the difference between groups in terms of gene expression levels in statistical analyses, and Cohen d effect sizes and odds ratios were calculated. AdaBoost and XGBoost algorithms, which are tree-based methods, were used for BC distant metastasis prediction, and the results were compared by evaluating comprehensive performance criteria. Results: After Boruta + LASSO methods, 14 biomarker candidate genes were identified. These predictive genes were PIB5PA, OR1F1, ALDH4A1, FGF18, WISP1, PRAME, CEGP1, AL080059, NMU, ATP5E, SMARCE1, FGD6, and 5LC37A1. In effect size results; in particular, show that the AL080059 (Cohen's D: 1.318) gene is clinically predictive of BC Metastasis. The accuracy, F1-score, positive predictive value, sensitivity, and area under the ROC Curve (AUC) values obtained with the AdaBoost algorithm for BC metastasis prediction was 95%, 96.3%,100%, 92.6%, and 98.8%, respectively. The model created with the XGBoost algorithm, on the other hand, obtained 90%, 92.9%, 92.9%, 92.9%, 97.6% accuracy, F1-score, positive predictive value, sensitivity, and AUC values, respectively. Conclusion: Identifying genes that successfully predict BC distant metastasis with AI methods in the study may be decisive for future therapeutic targets and help clinicians better adapt adjuvant chemotherapy to their patients. Additionally, the AdaBoost prediction model created can discriminate patients at risk of BC distant metastases.en_US
dc.identifier.doi10.4274/imj.galenos.2022.62443
dc.identifier.endpage215en_US
dc.identifier.issn2619-9793
dc.identifier.issn2148-094X
dc.identifier.issue3en_US
dc.identifier.startpage210en_US
dc.identifier.trdizinid1123315en_US
dc.identifier.urihttps://doi.org/10.4274/imj.galenos.2022.62443
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1123315
dc.identifier.urihttps://hdl.handle.net/11616/92888
dc.identifier.volume23en_US
dc.identifier.wosWOS:000847358400011en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherGalenos Publ Houseen_US
dc.relation.ispartofIstanbul Medical Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBreast canceren_US
dc.subjectdistant metastasisen_US
dc.subjectgenetic risk factorsen_US
dc.subjectgenomicsen_US
dc.subjectartificial intelligenceen_US
dc.titlePrediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspectiveen_US
dc.typeArticleen_US

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