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

TIMILSINA, Mohan, Dirk FEY, Adrianna JANIK, Maria TORRENTE, Mariano PROVENCIO, Alberto Cruz BERMUDEZ, Enric CARCERENY, Luca COSTABELLO, Delvys Rodrıguez ABREU, Manuel COBO, Rafael Lopez CASTRO, Reyes BERNABE, Maria GUIRADO, Pasquale MINERVINI and Vít NOVÁČEK. Integration of Medical and Genomic Information to Enhance Relapse Prediction in Early Stage Lung Cancer Patients. Online. In Proceedings of the Annual Symposium of the American Medical Informatics Association. Washington, USA: AMIA, 2022, p. 1082-1091. ISSN 1559-4076.

Basic information

• Original name: Integration of Medical and Genomic Information to Enhance Relapse Prediction in Early Stage Lung Cancer Patients
• Authors: TIMILSINA, Mohan, Dirk FEY, Adrianna JANIK, Maria TORRENTE, Mariano PROVENCIO, Alberto Cruz BERMUDEZ, Enric CARCERENY, Luca COSTABELLO, Delvys Rodrıguez ABREU, Manuel COBO, Rafael Lopez CASTRO, Reyes BERNABE, Maria GUIRADO, Pasquale MINERVINI and Vít NOVÁČEK (203 Czech Republic, guarantor, belonging to the institution).
• Edition: Washington, USA, Proceedings of the Annual Symposium of the American Medical Informatics Association, p. 1082-1091, 10 pp. 2022.
• Publisher: AMIA

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• WWW: URL
• Organization unit: Faculty of Informatics
• ISSN: 1559-4076
• Keywords in English: relapse; lung cancer; imputation; machine learning; genomic scores
• Tags: Artificial Intelligence, knowledge graphs, machine learning, medical informatics
• Tags: International impact, Reviewed

Abstract

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.