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

Warning

This publication doesn't include Faculty of Economics and Administration. It includes Faculty of Informatics. Official publication website can be found on muni.cz.

Authors	JANIK Adrianna TORRENTE Maria COSTABELLO Luca CALVO Virginia WALSH Brian CAMPS Carlos MOHAMED Sameh K ORTEGA Ana L NOVÁČEK Vít MASSUTÍ Bartomeu MINERVINI Pasquale GARCIA CAMPELO M Rosario BARCO Edel del BOSCH-BARRERA Joaquim MENASALVAS Ernestina TIMILSINA Mohan PROVENCIO Mariano
Year of publication	2023
Type	Article in Periodical
Magazine / Source	JCO CLINICAL CANCER INFORMATICS
MU Faculty or unit	Faculty of Informatics
Citation
Web	https://doi.org/10.1200/CCI.22.00062
Doi	http://dx.doi.org/10.1200/CCI.22.00062
Keywords	relapse prediction; machine learning; explainability; health informatics; lung cancer
Description	PURPOSE Stratifying patients with cancer according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to use machine learning to estimate probability of relapse in patients with early-stage non–small-cell lung cancer (NSCLC)? MATERIALS AND METHODS For predicting relapse in 1,387 patients with early-stage (I-II) NSCLC from the Spanish Lung Cancer Group data (average age 65.7 years, female 24.8%, male 75.2%), we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHapley Additive exPlanations local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients. RESULTS Machine learning models trained on tabular data exhibit a 76% accuracy for the random forest model at predicting relapse evaluated with a 10-fold cross-validation (the model was trained 10 times with different independent sets of patients in test, train, and validation sets, and the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a held-out test set of 200 patients, calibrated on a held-out set of 100 patients. CONCLUSION Our results show that machine learning models trained on tabular and graph data can enable objective, personalized, and reproducible prediction of relapse and, therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer.
Related projects:	AIcope - AI support for Clinical Oncology and Patient Empowerment