Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients. We discuss the promising results achieved, as well as the lessons we learned while developing this baseline for further, more advanced studies in this area.

Accenture Labs Dublin Ireland

Data Science Institute NUI Galway Galway Ireland

Faculty of Informatics Masaryk University Brno Czech Republic

Insight Centre for Data Analytics NUI Galway Galway Ireland

Medical Oncology Department Hospital Universitario Puerta de Hierro Majadahonda Madrid Spain

University College London London United Kingdom

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Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer