ArticleJournal of oncology2018
A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors.
Article in Journal of oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques.IEEE journal of translational engineering in health and medicine · 2023Article
- Lymphovascular invasion in hormone-positive, human epidermal growth factor-negative, low-burden axillary disease in early breast cancer patients tested for oncotype DX recurrence score.Contemporary oncology (Poznan, Poland) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
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.
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Registered trials
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