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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F24%3A00079388" target="_blank" >RIV/00209805:_____/24:00079388 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14330/24:00135372

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0957417423016299" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417423016299</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2023.121127" target="_blank" >10.1016/j.eswa.2023.121127</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;s prognosis. (C) 2023 The Author(s)

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;s prognosis. (C) 2023 The Author(s)

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30204 - Oncology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

—

Svazek periodika

235

Číslo periodika v rámci svazku

January 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

121127

Kód UT WoS článku

001063276900001

EID výsledku v databázi Scopus

2-s2.0-85168419455