Publication details

On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer

Authors

MOHAMED Sameh K. WALSH Brian TIMILSINA Mohan TORRENTE Maria FRANCO Fabio PROVENCIO Mariano JANIK Adrianna COSTABELLO Luca MINERVINI Pasquale STENETORP Pontus NOVÁČEK Vít

Year of publication 2022
Type Article in Proceedings
Conference Proceedings of AMIA 2021 Annual Symposium
MU Faculty or unit

Faculty of Informatics

Citation
Keywords machine learning; lung cancer; relapse prediction
Description 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.

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