OBJECTIVE: Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency. This study aims to impute and integrate specific types of genomic data with clinical data to improve the accuracy of machine learning models for predicting relapse in early-stage, non-small cell lung cancer patients. METHODS: The study utilized a publicly available TCGA lung cancer cohort and imputed genetic pathway scores into the Spanish Lung Cancer Group (SLCG) data, specifically in 1348 early-stage patients. Initially, tumor recurrence was predicted without imputed pathway scores. Subsequently, the SLCG data were augmented with pathway scores imputed from TCGA. The integrative approach aimed to enhance relapse risk prediction performance. RESULTS: The integrative approach achieved improved relapse risk prediction with the following evaluation metrics: an area under the precision-recall curve (PR-AUC) score of 0.75, an area under the ROC (ROC-AUC) score of 0.80, an F1 score of 0.61, and a Precision of 0.80. The prediction explanation model SHAP (SHapley Additive exPlanations) was employed to explain the machine learning model's predictions. CONCLUSION: We conclude that our explainable predictive model is a promising tool for oncologists that addresses an unmet clinical need of post-treatment patient stratification based on the relapse risk while also improving the predictive power by incorporating proxy genomic data not available for specific patients.

Accenture Labs Dublin Ireland

Catalan Institute of Oncology Hospital Universitari Germans Trias i Pujol B ARGO IGTP Badalona Spain

Data Science Institute Insight Centre for Data Analytics University of Galway Ireland

Data Science Institute Insight Centre for Data Analytics University of Galway Ireland; Faculty of Informatics Masaryk University Brno Czech Republic; Masaryk Memorial Cancer Institute Brno Czech Republic

Hospital Clínico Universitario de Valladolid Spain

Hospital Universitario Insular de Gran Canaria Las Palmas de Gran Canaria Spain

Hospital Universitario Virgen del Rocio Sevilla Spain

Medical Oncology Department Hospital Universitario Puerta de Hierro Majadahonda Madrid Spain

Medical Oncology Intercenter Unit Regional and Virgen de la Victoria University Hospitals IBIMA Málaga Spain

Systems Biology Ireland University College Dublin Ireland

University College London London United Kingdom

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