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

J 2024

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

BUOSI, Samuele; Mohan TIMILSINA; Adriann JANIK; Luca COSTABELLO; Maria TORRENTE et. al.

Základní údaje

Originální název

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

Autoři

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

Vydání

Expert Systems with Applications, Elsevier, 2024, 0957-4174

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 7.500

Kód RIV

RIV/00216224:14330/24:00135372

Organizační jednotka

Fakulta informatiky

DOI

https://doi.org/10.1016/j.eswa.2023.121127

UT WoS

001063276900001

EID Scopus

2-s2.0-85168419455

Klíčová slova anglicky

Non-small-cell lung cancer; Tumor recurrence prediction; Knowledge graph embedding; Machine learning; Link prediction

Štítky

Artificial Intelligence, medical informatics

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 4. 4. 2025 11:07, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

MUNI/G/1763/2020, interní kód MU

Název: AIcope - AI support for Clinical Oncology and Patient Empowerment (Akronym: AIcope)

Investor: Masarykova univerzita, AIcope - AI support for Clinical Oncology and Patient Empowerment, INTERDISCIPLINARY - Mezioborové výzkumné projekty

Citovat

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. 235, č. 121127, s. 1-8. ISSN 0957-4174. Dostupné z: https://doi.org/10.1016/j.eswa.2023.121127.

@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 = {121127},
   doi = {https://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 = {235},
   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  - 235
IS  - 121127
SP  - 1-8
EP  - 1-8
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.~235, č.~121127, s.~1-8. ISSN~0957-4174. Dostupné z: https://doi.org/10.1016/j.eswa.2023.121127.

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