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Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based

Hasna El Haji^1,2,3, Amine Souadka⁴, Bhavik N Patel^1,2

¹Department of Radiology, Mayo Clinic, Phoenix, AZ.

JCO Clinical Cancer Informatics

|August 11, 2023

Summary

This summary is machine-generated.

This study reviews breast cancer recurrence prediction models, finding machine learning (ML) models, particularly deep learning, offer high accuracy. However, challenges remain in interpretability, data limitations, and underrepresentation of diverse populations in model development.

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Area of Science:

Oncology
Medical Informatics
Data Science

Background:

Accurate breast cancer (BC) recurrence prediction is crucial for effective adjuvant therapy selection.
Numerous statistical and machine learning (ML) models have been developed, evolving in complexity and predictive power.

Conclusions:

ML models show high accuracy for breast cancer recurrence prediction but face challenges in interpretability and generalization.
Limited variable inclusion, imbalanced datasets, and lack of open-source data hinder model development and validation.
Existing models predominantly lack diversity, being trained and validated on Caucasian and Asian populations, neglecting other ethnicities.