Survival Prediction for Stage I-IIIA Non-Small Cell Lung Cancer Using Deep Learning.

Background and purpose: The aim of this study was to develop and evaluate a prediction model for 2-year overall survival (OS) in stage I-IIIA non-small cell lung cancer (NSCLC) patients who received definitive radiotherapy by considering clinical variables and image features from pre-treatment CT-scans.Materials and methods: NSCLC patients who received stereotactic radiotherapy were prospectively col-lected at the UMCG and split into a training and a hold out test set including 189 and 81 patients, respec-tively. External validation was performed on 228 NSCLC patients who were treated with radiation or concurrent chemoradiation at the Maastro clinic (Lung1 dataset). A hybrid model that integrated both image and clinical features was implemented using deep learning. Image features were learned from cubic patches containing lung tumours extracted from pre-treatment CT scans. Relevant clinical variables were selected by univariable and multivariable analyses.Results: Multivariable analysis showed that age and clinical stage were significant prognostic clinical fac-tors for 2-year OS. Using these two clinical variables in combination with image features from pre-treatment CT scans, the hybrid model achieved a median AUC of 0.76 [95 % CI: 0.65-0.86] and 0.64 [95 % CI: 0.58-0.70] on the complete UMCG and Maastro test sets, respectively. The Kaplan-Meier sur-vival curves showed significant separation between low and high mortality risk groups on these two test sets (log-rank test: p-value < 0.001, p-value = 0.012, respectively)Conclusion: We demonstrated that a hybrid model could achieve reasonable performance by utilizing both clinical and image features for 2-year OS prediction. Such a model has the potential to identify patients with high mortality risk and guide clinical decision making. (c) 2023 The Author(s). Published by Elsevier B.V. Radiotherapy and Oncology 180 (2023) 1-6 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).