Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling

For a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy is able to prevent tumor relapse and formation of metastases. A prediction of the outcome of adjuvant therapy tailored for the patient is hard, due to the heterogeneous nature of the disease. We devised a methodology for predicting 5-years survival based on the new machine learning paradigm of coherent voting networks, with improved accuracy over state-of-the-art prediction methods. The 'coherent voting communities' metaphor provides a certificate justifying the survival prediction for an individual patient, thus facilitating its acceptability in practice, in the vein of explainable Artificial Intelligence. The method we propose is quite flexible and applicable to other types of cancer.

Automated summary

For breast cancer patients, choosing the right adjuvant therapy for preventing tumor relapse and metastases can be challenging. A new machine learning method based on coherent voting networks has been developed to predict 5-year survival rates with improved accuracy and explainability, applicable to other types of cancer as well.