| BUOSI, Samuele, Mohan TIMILSINA, Adriann JANIK, Luca COSTABELLO, Maria TORRENTE, Mariano PROVENCIO, Dirk FEY a Vít NOVÁČEK. Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings. Expert Systems with Applications. Elsevier, 2024, roč. 2024, č. 235, s. 121-127. ISSN 0957-4174. Dostupné z: https://dx.doi.org/10.1016/j.eswa.2023.121127. |
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@article{2362601,
author = {Buosi, Samuele and Timilsina, Mohan and Janik, Adriann and Costabello, Luca and Torrente, Maria and Provencio, Mariano and Fey, Dirk and Nováček, Vít},
article_number = {235},
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},
language = {eng},
issn = {0957-4174},
journal = {Expert Systems with Applications},
title = {Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings},
url = {https://doi.org/10.1016/j.eswa.2023.121127},
volume = {2024},
year = {2024}
}
TY - JOUR
ID - 2362601
AU - Buosi, Samuele - Timilsina, Mohan - Janik, Adriann - Costabello, Luca - Torrente, Maria - Provencio, Mariano - Fey, Dirk - Nováček, Vít
PY - 2024
TI - Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings
JF - Expert Systems with Applications
VL - 2024
IS - 235
SP - 121-127
EP - 121-127
PB - Elsevier
SN - 09574174
KW - Non-small-cell lung cancer
KW - Tumor recurrence prediction
KW - Knowledge graph embedding
KW - Machine learning
KW - Link prediction
UR - https://doi.org/10.1016/j.eswa.2023.121127
N2 - 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.
ER -
BUOSI, Samuele, Mohan TIMILSINA, Adriann JANIK, Luca COSTABELLO, Maria TORRENTE, Mariano PROVENCIO, Dirk FEY a Vít NOVÁČEK. Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings. \textit{Expert Systems with Applications}. Elsevier, 2024, roč.~2024, č.~235, s.~121-127. ISSN~0957-4174. Dostupné z: https://dx.doi.org/10.1016/j.eswa.2023.121127.
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