Abstract
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.
Highlights
For a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy can prevent the tumor relapse and the formation of metastases
We describe a novel machine learning (ML) supervised classification method and we apply it to the task of producing prognostic predictions of survival at 5 years for Breast Cancer (BC) patients using gene expression levels measured from the samples of the tumor surgically removed
We have developed a new ML supervised classification method called Coherent Voting Networks (CVN) which is suitable for handling highly non-linear phenomena such as those prevalent in biological systems
Summary
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. Primary cancer treatment for new cases of BC is surgery (of various types), followed by adjuvant therapies (see e.g.: https://www.gov.uk/government/publications/chemotherapy-radiotherapy-and-surgical-tumour-resections-in-england/chemotherapy-radiotherapy-and-surgical-tumour-resections-in-england). For a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy can prevent the tumor relapse and the formation of metastases. To this effect, a series of measurements of several parameters (clinical, histological, molecular) are collected and evaluated by experts with the help of guidelines. In particular high-throughput sequencing technologies have been key enablers for the success of this new approach, as well as the efforts for systematic collection of molecular data
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