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Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer
A. Janik, M. Torrente, L. Costabello, V. Calvo, B. Walsh, C. Camps, SK. Mohamed, AL. Ortega, V. Nováček, B. Massutí, P. Minervini, MRG. Campelo, E. Del Barco, J. Bosch-Barrera, E. Menasalvas, M. Timilsina, M. Provencio
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
37428988
DOI
10.1200/cci.22.00062
Knihovny.cz E-zdroje
- MeSH
- lidé MeSH
- lokální recidiva nádoru diagnóza MeSH
- nádory plic * diagnóza terapie MeSH
- nemalobuněčný karcinom plic * diagnóza terapie MeSH
- prognóza MeSH
- senioři MeSH
- strojové učení MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
Complejo Hospitalario Universitario A Coruña A Coruña Spain
Data Science Institute University of Galway Galway Ireland
Faculty of Informatics Masaryk University Brno Czech Republic
Hospital General de Valencia Valencia Spain
Hospital General Universitario de Alicante Alicante Spain
Hospital Universitario de Jaén Jaén Spain
Hospital Universitario de Salamanca Salamanca Spain
Insight Centre for Data Analytics University of Galway Galway Ireland
Institut Català d'Oncologia Hospital Universitari Dr Josep Trueta Girona Spain
Masaryk Memorial Cancer Institute Brno Czech Republic
Medical Oncology Department Hospital Universitario Puerta de Hierro Majadahonda Madrid Spain
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