Machine learning model for predicting lung cancer recurrence after surgical treatment: A retrospective study using NLST and European hospital data
Published in ESMO Abstracts, 2023
Recommended citation: Valter A., Kordemets T., Gasimova A., Waterfield Price N., Freitag L., Vanakesa T., Almre I., Oselin K. (2023). "Machine learning model for predicting lung cancer recurrence after surgical treatment: A retrospective study using NLST and European hospital data." ESMO.
The rate of lung cancer recurrence following curative surgical resection is 30-55% and remains a significant challenge in patient management. Accurate prediction of recurrence risk is crucial for guiding treatment decisions, such as the use of (neo-)adjuvant chemo- or immunotherapy, the extent of lung resection, and follow-up strategies. We present a preoperative machine learning model that uses patient computed tomography (CT) images and demographic features to predict lung cancer recurrence.