Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study.

Journal: Journal of the Egyptian National Cancer Institute

PMID: 38853190

Abstract

BACKGROUND: The goal is to use three different machine learning models to predict the recurrence of breast cancer across a very heterogeneous sample of patients with varying disease kinds and stages.

Authors

Saadia Azeroual

LPHE-Modeling and Simulations, Faculty of Science, Mohammed V University in Rabat, Rabat, Morocco. azeroual.saadia1@gmail.com.
Fatima-Ezzahraa Ben-Bouazza

Faculty of Sciences and Technology, Hassan First University, Settat, Morocco.
Amine Naqi

Mohammed VI University of Sciences and Health, Casablanca, Morocco.
Rajaa Sebihi

LPHE-Modeling and Simulations, Faculty of Science, Mohammed V University in Rabat, Rabat, Morocco.

Keywords

Adult Aged Algorithms Breast Neoplasms Female Humans Machine Learning Magnetic Resonance Imaging Middle Aged Neoplasm Recurrence, Local Neoplasm Staging Prognosis Radiomics Retrospective Studies ROC Curve Support Vector Machine Triple Negative Breast Neoplasms

External Resources

View on PubMed Access via DOI PubMed (38853190)

Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study.

Abstract

Authors

Keywords

External Resources

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