# Coalitions of AI-based Methods Predict 15-Year Risks of Breast Cancer Metastasis Using Real-World Clinical Data with AUC up to 0.9

arXiv: https://arxiv.org/abs/2408.16256 · HTML: https://mlsanity.com/paper/2408.16256/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Learning Deep Structured Models](https://mlsanity.com/paper/1407.2538/) (2014)
- [Deep Learning to Predict Late-Onset Breast Cancer Metastasis: the Single Hyperparameter Grid Search (SHGS) Strategy for Meta Tuning Concerning Deep Feed-forward Neural Network](https://mlsanity.com/paper/2408.15498/) (2024)

## Abstract (arXiv)

Breast cancer is one of the two cancers responsible for the most deaths in women, with about 42,000 deaths each year in the US. That there are over 300,000 breast cancers newly diagnosed each year suggests that only a fraction of the cancers result in mortality. Thus, most of the women undergo seemingly curative treatment for localized cancers, but a significant later succumb to metastatic disease for which current treatments are only temporizing for the vast majority. The current prognostic metrics are of little actionable value for 4 of the 5 women seemingly cured after local treatment, and many women are exposed to morbid and even mortal adjuvant therapies unnecessarily, with these adjuvant therapies reducing metastatic recurrence by only a third. Thus, there is a need for better prognostics to target aggressive treatment at those who are likely to relapse and spare those who were actually cured. While there is a plethora of molecular and tumor-marker assays in use and under-development to detect recurrence early, these are time consuming, expensive and still often un-validated as to actionable prognostic utility. A different approach would use large data techniques to determine clinical and histopathological parameters that would provide accurate prognostics using existing data. Herein, we report on machine learning, together with grid search and Bayesian Networks to develop algorithms that present a AUC of up to 0.9 in ROC analyses, using only extant data. Such algorithms could be rapidly translated to clinical management as they do not require testing beyond routine tumor evaluations.