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A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-07
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Dirican, E.& Kiliç, E.. 2018. A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors. Journal of Oncology،Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1195744
Modern Language Association (MLA)
Dirican, E.& Kiliç, E.. A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors. Journal of Oncology No. 2018 (2018), pp.1-7.
https://search.emarefa.net/detail/BIM-1195744
American Medical Association (AMA)
Dirican, E.& Kiliç, E.. A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors. Journal of Oncology. 2018. Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1195744
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195744