A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors
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Dosyalar
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Hindawi Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
ki-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.
Açıklama
Anahtar Kelimeler
International Expert Consensus, Breast-Cancer Highlights, Lymph-Node Ratio, Neoadjuvant Chemotherapy, Lymphovascular Invasion, Molecular Subtypes, Primary Therapy, Women, Ki67, Management
Kaynak
Journal of Oncology
WoS Q DeÄŸeri
Q3
Scopus Q DeÄŸeri
N/A
Cilt
2018
