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@article{2300102,
author = {Janik, Adrianna and Torrente, Maria and Costabello, Luca and Calvo, Virginia and Walsh, Brian and Camps, Carlos and Mohamed, Sameh K and Ortega, Ana L and Nováček, Vít and Massutí, Bartomeu and Minervini, Pasquale and Garcia Campelo, M Rosario and Barco, Edel del and BoschandBarrera, Joaquim and Menasalvas, Ernestina and Timilsina, Mohan and Provencio, Mariano},
article_location = {UNITED STATES},
article_number = {e2200062},
doi = {http://dx.doi.org/10.1200/CCI.22.00062},
keywords = {relapse prediction; machine learning; explainability; health informatics; lung cancer},
language = {eng},
issn = {2473-4276},
journal = {JCO CLINICAL CANCER INFORMATICS},
title = {Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer},
url = {https://doi.org/10.1200/CCI.22.00062},
volume = {7},
year = {2023}
}
TY - JOUR
ID - 2300102
AU - 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
PY - 2023
TI - Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer
JF - JCO CLINICAL CANCER INFORMATICS
VL - 7
IS - e2200062
SP - 1-11
EP - 1-11
PB - LIPPINCOTT WILLIAMS & WILKINS
SN - 24734276
KW - relapse prediction
KW - machine learning
KW - explainability
KW - health informatics
KW - lung cancer
UR - https://doi.org/10.1200/CCI.22.00062
N2 - 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.
ER -
JANIK, Adrianna, Maria TORRENTE, Luca COSTABELLO, Virginia CALVO, Brian WALSH, Carlos CAMPS, Sameh K MOHAMED, Ana L ORTEGA, Vít NOVÁČEK, Bartomeu MASSUTÍ, Pasquale MINERVINI, M Rosario GARCIA CAMPELO, Edel del BARCO, Joaquim BOSCH-BARRERA, Ernestina MENASALVAS, Mohan TIMILSINA and Mariano PROVENCIO. Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer. \textit{JCO CLINICAL CANCER INFORMATICS}. UNITED STATES: LIPPINCOTT WILLIAMS \&{} WILKINS, 2023, vol.~7, e2200062, p.~1-11. ISSN~2473-4276. Available from: https://dx.doi.org/10.1200/CCI.22.00062.
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