ML_BCP

Machine learning techniques for personalized breast cancer risk prediction

This Collaborative research project develops methods, validates tools, and disseminates machine learning techniques for personalized breast cancer risk prediction (ML_BCP). The techniques are compared against state-of-the-art model-based approaches like Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA).

Table of contents

• Table of contents
• Overview
• Resources
• Usage
• Contact
• References

Overview

• Background: Breast cancer risk prediction modeling allows researchers to identify high-risk patients and reduce unnecessary interventions. Currently, breast cancer risk prediction models tend to exhibit low discriminatory accuracy (0.53-0.64). We are employing machine learning (ML) approaches to address current limitations and improve accuracy of outcome forecasts. This study compares the discriminatory accuracy of ML-based estimates against a pair of established methods: Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA).

• Methods: Eight simulated datasets and two retrospective samples were used to compare the performance of the proposed ML methods against state-of-the-art BCRAT and BOADICEA models.

• Results : Predictive accuracy (AU-ROC curve) reached 88.28% using ML-Adaptive Boosting and 88.89% using ML-random forest versus 62.40% with BCRAT for the U.S. population-based sample. Predictive accuracy reached 90.17% using ML-adaptive boosting and 89.32% using ML-Markov chain Monte Carlo generalized linear mixed model versus 59.31% with BOADICEA for the Swiss clinic-based sample.

• Conclusions: The significant prediction improvement of the accuracy of classification of women with and without breast cancer achieved with ML algorithms is important in personalized medicine and may suggest prevention strategies and individualized clinical management.

Resources

• Data
• Source-code (R-scripts)
• Results

Usage

Open Science

Contact

• Chang Ming and Maria C. Katapodi, University of Basel
• Ivo D. Dinov, SOCR

References

Ming, C, Viassolo, V, Probst-Hensch, N, Chappuis, PO, Dinov, ID, and Katapodi, MC. (2019) Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models, Breast Cancer Research 21(1):75, DOI 10.1186/s13058-019-1158-4.

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