Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F23%3A00079321" target="_blank" >RIV/00209805:_____/23:00079321 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14330/23:00131336

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1532046423001454?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1532046423001454?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jbi.2023.104424" target="_blank" >10.1016/j.jbi.2023.104424</a>

Result language

angličtina

Original language name

Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer

Original language description

OBJECTIVE: Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency. This study aims to impute and integrate specific types of genomic data with clinical data to improve the accuracy of machine learning models for predicting relapse in early-stage, non-small cell lung cancer patients. METHODS: The study utilized a publicly available TCGA lung cancer cohort and imputed genetic pathway scores into the Spanish Lung Cancer Group (SLCG) data, specifically in 1348 early-stage patients. Initially, tumor recurrence was predicted without imputed pathway scores. Subsequently, the SLCG data were augmented with pathway scores imputed from TCGA. The integrative approach aimed to enhance relapse risk prediction performance. RESULTS: The integrative approach achieved improved relapse risk prediction with the following evaluation metrics: an area under the precision-recall curve (PR-AUC) score of 0.75, an area under the ROC (ROC-AUC) score of 0.80, an F1 score of 0.61, and a Precision of 0.80. The prediction explanation model SHAP (SHapley Additive exPlanations) was employed to explain the machine learning model&apos;s predictions. CONCLUSION: 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 specific patients.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Journal of biomedical informatics

ISSN

1532-0464

e-ISSN

1532-0480

Volume of the periodical

144

Issue of the periodical within the volume

August 2023

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

104424

UT code for WoS article

001030137400001

EID of the result in the Scopus database

2-s2.0-85163481485