Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings

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Authors

BUOSI Samuele TIMILSINA Mohan JANIK Adriann COSTABELLO Luca TORRENTE Maria PROVENCIO Mariano FEY Dirk NOVÁČEK Vít

Year of publication 2024
Type Article in Periodical
Magazine / Source Expert Systems with Applications
MU Faculty or unit

Faculty of Informatics

Citation
Web https://doi.org/10.1016/j.eswa.2023.121127
Doi http://dx.doi.org/10.1016/j.eswa.2023.121127
Keywords Non-small-cell lung cancer; Tumor recurrence prediction; Knowledge graph embedding; Machine learning; Link prediction
Description Motivation: Low-stage lung cancer is known to recur unpredictably, and patients receiving various treatment methods like radiation, chemotherapy, and immunotherapies have been seen to respond very differently. Identifying a priori if a patient is going to relapse or not could make a difference in terms of saving lives and personalized care offered. In this work, we provide an answer to the following research question: Is it possible to enhance the machine learning (ML) of the estimated probability of relapse in early-stage non-small-cell lung cancer (NSCLC) patients with aneuploidy imputation scores? Results: To predict recurrence in 1,348 early-stage (I–II) NSCLC patients, we train graph ML models utilizing the Spanish pulmonary cancer group knowledge graph enriched with triples from pathway imputation. ML models trained on Knowledge graph data enriched with triples from pathway score imputation present an 82% Precision and 91% Specificity in predicting relapse over 200 patients from a held-out test set. ML models trained using graphs data could prove useful supplemental tool in the TNM classification systems and improve a lung cancer patient’s prognosis.
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