Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms

Yufan Feng, Natasha McGuire, Alexandra Walton, AP-MBC Consortium, Stephen Fox, Antonella Papa, Sunil R. Lakhani, Amy E. McCart Reed

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

Metaplastic breast cancer (MpBC) is a rare and aggressive subtype of breast cancer, with data emerging on prognostic factors and survival prediction. This study aimed to develop machine learning models to predict breast cancer-specific survival (BCSS) in MpBC patients, utilizing a dataset of 160 patients with clinical, pathological, and biological variables. An in-depth variable selection process was carried out using gain ratio and correlation-based methods, resulting in 10 variables for model estimation. Five models (decision tree with bagging; logistic regression; multilayer perceptron; naïve Bayes; and, random forest algorithms) were evaluated using 10-fold cross-validation. Despite the constraints posed by the absence of therapeutic information, the random forest model exhibited the highest performance in predicting BCSS, with an ROC area of 0.808. This study emphasizes the potential of machine learning algorithms in predicting prognosis for complex and heterogeneous cancer subtypes using clinical datasets, and their potential to contribute to patient management. Further research that incorporates additional variables, such as treatment response, and more advanced machine learning techniques will likely enhance the predictive power of MpBC prognostic models.

Original languageEnglish
Article number100329
Number of pages9
JournalJournal of Pathology Informatics
Volume14
DOIs
Publication statusPublished - Jan 2023

Keywords

  • Breast cancer
  • Breast cancer-specific survival (BCSS)
  • Machine learning algorithms
  • Metaplastic breast cancer (MpBC)
  • Predictive models
  • Random forest

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