Publication details

Integration of Medical and Genomic Information to Enhance Relapse Prediction in Early Stage Lung Cancer Patients

Authors

TIMILSINA Mohan FEY Dirk JANIK Adrianna TORRENTE Maria PROVENCIO Mariano BERMUDEZ Alberto Cruz CARCERENY Enric COSTABELLO Luca ABREU Delvys Rodriguez COBO Manuel CASTRO Rafael Lopez BERNABE Reyes GUIRADO Maria MINERVINI Pasquale NOVÁČEK Vít

Year of publication 2022
Type Article in Proceedings
Conference Proceedings of the Annual Symposium of the American Medical Informatics Association
MU Faculty or unit

Faculty of Informatics

Citation
Web the web of the conference
Keywords relapse; lung cancer; imputation; machine learning; genomic scores
Description Early-stage lung cancer is crucial clinically due to its insidious nature and rapid progression. Most of the prediction models designed to predict tumour recurrence in the early stage of lung cancer rely on the clinical or medical history of the patient. However, their performance could likely be improved if the input patient data contained genomic information. Unfortunately, such data is not always collected. This is the main motivation of our work, in which we have imputed and integrated specific type of genomic data with clinical data to increase the accuracy of machine learning models for prediction of relapse in early-stage, non-small cell lung cancer patients. Using a publicly available TCGA lung adenocarcinoma cohort of 501 patients, their aneuploidy scores were imputed into similar records in the Spanish Lung Cancer Group (SLCG) data, more specifically a cohort of 1348 early-stage patients. First, the tumor recurrence in those patients was predicted without the imputed aneuploidy scores. Then, the SLCG data were enriched with the aneuploidy scores imputed from TCGA. This integrative approach improved the prediction of the relapse risk, achieving area under the precision-recall curve (PR-AUC) score of 0.74, and area under the ROC (ROC-AUC) score of 0.79. Using the prediction explanation model SHAP (SHapley Additive exPlanations), we further explained the predictions performed by the machine learning model. 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 the actual specific patients.

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