A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors

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dc.authoridDIRICAN, EMRE/0000-0003-3550-1326
dc.contributor.authorDirican, E.
dc.contributor.authorKilic, E.
dc.date.accessioned2024-09-18T21:06:28Z
dc.date.available2024-09-18T21:06:28Z
dc.date.issued2018
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractki-67 score is a solid tumor proliferation marker being associated with the prognosis of breast carcinoma and its response to neoadjuvant chemotherapy. In the present study, we aimed to investigate the way of clustering of prognostic factors by ki-67 score using a machine learning approach and multiple correspondence analysis. In this study, 223 patients with breast carcinoma were analyzed using the random forest method for classification of prognostic factors according to ki-67 groups (<14% and >14%). Also the relationship between subgroups of prognostic factors and ki-67 scores was examined by multiple correspondence analysis. There was a clustering of molecular classification LA, 0-3 metastatic lymph node, age <50, absence of LVI, T1 tumor size with ki-67 <14% and grade III, 10 or more metastatic lymph nodes, and presence of LVI and molecular classification LB, age >50, and T3-T4 tumor size categories with ki-67 >14%. The fact that the low scores of ki-67 correlate with early stage diseases and high scores with advanced disease suggests that 14% threshold value is crucial for ki-67 score.en_US
dc.identifier.doi10.1155/2018/1912438
dc.identifier.issn1687-8450
dc.identifier.issn1687-8469
dc.identifier.pmid30158977en_US
dc.identifier.scopus2-s2.0-85053066710en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1155/2018/1912438
dc.identifier.urihttps://hdl.handle.net/20.500.12483/13626
dc.identifier.volume2018en_US
dc.identifier.wosWOS:000441976700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofJournal of Oncologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectInternational Expert Consensusen_US
dc.subjectBreast-Cancer Highlightsen_US
dc.subjectLymph-Node Ratioen_US
dc.subjectNeoadjuvant Chemotherapyen_US
dc.subjectLymphovascular Invasionen_US
dc.subjectMolecular Subtypesen_US
dc.subjectPrimary Therapyen_US
dc.subjectWomenen_US
dc.subjectKi67en_US
dc.subjectManagementen_US
dc.titleA Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factorsen_US
dc.typeArticleen_US

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