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

J 2023

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

TIMILSINA, Mohan, Dirk FEY, Samuele BUOSI, Adrianna JANIK, Luca COSTABELLO et. al.

Základní údaje

Originální název

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

Autoři

Vydání

Journal for Biomedical Informatics, Elsevier, 2023, 1532-0464

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: 4.500 v roce 2022

Kód RIV

RIV/00216224:14330/23:00131336

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1016/j.jbi.2023.104424

UT WoS

001030137400001

Klíčová slova anglicky

Regression; Classification; Imputation; Recurrence; Supervised; Explanation

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 8. 4. 2024 01:11, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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’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.

Citovat

TIMILSINA, Mohan, Dirk FEY, Samuele BUOSI, Adrianna JANIK, Luca COSTABELLO, Enric CARCERENY, Delvys Rodrıguez ABREU, Manuel COBO, Rafael López CASTRO, Reyes BERNABÉ, Pasquale MINERVINI, Maria TORRENTE, Mariano PROVENCIO a Vít NOVÁČEK. Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer. Journal for Biomedical Informatics. Elsevier, 2023, roč. 144, č. 104424, s. 1-12. ISSN 1532-0464. Dostupné z: https://dx.doi.org/10.1016/j.jbi.2023.104424.

@article{2300101,
   author = {Timilsina, Mohan and Fey, Dirk and Buosi, Samuele and Janik, Adrianna and Costabello, Luca and Carcereny, Enric and Abreu, Delvys Rodrıguez and Cobo, Manuel and Castro, Rafael López and Bernabé, Reyes and Minervini, Pasquale and Torrente, Maria and Provencio, Mariano and Nováček, Vít},
   article_number = {104424},
   doi = {http://dx.doi.org/10.1016/j.jbi.2023.104424},
   keywords = {Regression; Classification; Imputation; Recurrence; Supervised; Explanation},
   language = {eng},
   issn = {1532-0464},
   journal = {Journal for Biomedical Informatics},
   title = {Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer},
   url = {https://www.sciencedirect.com/science/article/pii/S1532046423001454},
   volume = {144},
   year = {2023}
}

TY  - JOUR
ID  - 2300101
AU  - Timilsina, Mohan - Fey, Dirk - Buosi, Samuele - Janik, Adrianna - Costabello, Luca - Carcereny, Enric - Abreu, Delvys Rodrıguez - Cobo, Manuel - Castro, Rafael López - Bernabé, Reyes - Minervini, Pasquale - Torrente, Maria - Provencio, Mariano - Nováček, Vít
PY  - 2023
TI  - Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer
JF  - Journal for Biomedical Informatics
VL  - 144
IS  - 104424
SP  - 1-12
EP  - 1-12
PB  - Elsevier
SN  - 15320464
KW  - Regression
KW  - Classification
KW  - Imputation
KW  - Recurrence
KW  - Supervised
KW  - Explanation
UR  - https://www.sciencedirect.com/science/article/pii/S1532046423001454
N2  - 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’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.
ER  -

TIMILSINA, Mohan, Dirk FEY, Samuele BUOSI, Adrianna JANIK, Luca COSTABELLO, Enric CARCERENY, Delvys Rodrıguez ABREU, Manuel COBO, Rafael López CASTRO, Reyes BERNABÉ, Pasquale MINERVINI, Maria TORRENTE, Mariano PROVENCIO a Vít NOVÁČEK. Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer. \textit{Journal for Biomedical Informatics}. Elsevier, 2023, roč.~144, č.~104424, s.~1-12. ISSN~1532-0464. Dostupné z: https://dx.doi.org/10.1016/j.jbi.2023.104424.

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