Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer.
Adrianna JanikMaría TorrenteLuca CostabelloVirginia CalvoBrian WalshCarlos CampsSameh K MohamedAna Laura OrtegaVít NováčekBartomeu MassutíPasquale MinerviniM Rosario Garcia CampeloEdel Del BarcoJoaquim Bosch-BarreraErnestina MenasalvasMohan TimilsinaMariano Provencio PullaPublished in: JCO clinical cancer informatics (2023)
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.
Keyphrases
- early stage
- machine learning
- big data
- sentinel lymph node
- electronic health record
- artificial intelligence
- small cell lung cancer
- free survival
- deep learning
- advanced non small cell lung cancer
- data analysis
- radiation therapy
- convolutional neural network
- epidermal growth factor receptor
- rectal cancer
- neural network
- neoadjuvant chemotherapy