Machine Learning Survival Models for Relapse Prediction in a
Early Stage Lung Cancer Patient

D 2023

Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient

TIMILSINA, Mohan, Samuele BUOSI, Adrianna JANIK, Pasquale MINERVINI, Luca COSTABELLO et. al.

Základní údaje

Originální název

Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient

Autoři

Vydání

Broadbeach, Australia, 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, od s. 1-8, 8 s. 2023

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/23:00133943

Organizační jednotka

Fakulta informatiky

ISBN

978-1-6654-8867-9

ISSN

DOI

http://dx.doi.org/10.1109/IJCNN54540.2023.10191078

UT WoS

001046198700044

Klíčová slova anglicky

survival; time; event; prediction; cancer; explanation

Štítky

core_B, firank_A

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 8. 4. 2024 11:32, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Lung cancer is one of the leading health complications causing high mortality worldwide. The relapsing behavior of medically treated early-stage lung cancer makes this disease even more complicated. Thus predicting such relapse using a data-centric approach provides a complementary perspective for clinicians to understand the disease. In this preliminary work, we explored off-the-shelf survival models to predict the relapse of early-stage lung cancer patients. We analyzed the survival models on a cohort of 1348 early-stage non-small cell lung cancer (NSCLC) patients in different timestamps. Using the prediction explanation model SHAP (SHapley Additive exPlanations), we further explained the best-performing survival model's predictions. Our explainable predictive model is a potential tool for oncologists that address an unmet clinical need for post-treatment patient stratification based on the relapse hazard.

Citovat

TIMILSINA, Mohan, Samuele BUOSI, Adrianna JANIK, Pasquale MINERVINI, Luca COSTABELLO, Maria TORRENTE, Mariano PROVENCIO, Virginia CALVO, Carlos CAMPS, Ana L ORTEGA, Bartomeu MASSUTI, Rosario Garcia M. CAMPELO, del Barco EDEL, Joaquim BOSCH-BARRERA a Vít NOVÁČEK. Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient. Online. In 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN. Broadbeach, Australia: IEEE, 2023, s. 1-8. ISBN 978-1-6654-8867-9. Dostupné z: https://dx.doi.org/10.1109/IJCNN54540.2023.10191078.

@inproceedings{2392298,
   author = {Timilsina, Mohan and Buosi, Samuele and Janik, Adrianna and Minervini, Pasquale and Costabello, Luca and Torrente, Maria and Provencio, Mariano and Calvo, Virginia and Camps, Carlos and Ortega, Ana L and Massuti, Bartomeu and Campelo, Rosario Garcia M. and Edel, del Barco and BoschandBarrera, Joaquim and Nováček, Vít},
   address = {Broadbeach, Australia},
   booktitle = {2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN},
   doi = {http://dx.doi.org/10.1109/IJCNN54540.2023.10191078},
   keywords = {survival; time; event; prediction; cancer; explanation},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Broadbeach, Australia},
   isbn = {978-1-6654-8867-9},
   pages = {1-8},
   publisher = {IEEE},
   title = {Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient},
   year = {2023}
}

TY  - JOUR
ID  - 2392298
AU  - Timilsina, Mohan - Buosi, Samuele - Janik, Adrianna - Minervini, Pasquale - Costabello, Luca - Torrente, Maria - Provencio, Mariano - Calvo, Virginia - Camps, Carlos - Ortega, Ana L - Massuti, Bartomeu - Campelo, Rosario Garcia M. - Edel, del Barco - Bosch-Barrera, Joaquim - Nováček, Vít
PY  - 2023
TI  - Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient
PB  - IEEE
CY  - Broadbeach, Australia
SN  - 9781665488679
KW  - survival
KW  - time
KW  - event
KW  - prediction
KW  - cancer
KW  - explanation
N2  - Lung cancer is one of the leading health complications causing high mortality worldwide. The relapsing behavior of medically treated early-stage lung cancer makes this disease even more complicated. Thus predicting such relapse using a data-centric approach provides a complementary perspective for clinicians to understand the disease. In this preliminary work, we explored off-the-shelf survival models to predict the relapse of early-stage lung cancer patients. We analyzed the survival models on a cohort of 1348 early-stage non-small cell lung cancer (NSCLC) patients in different timestamps. Using the prediction explanation model SHAP (SHapley Additive exPlanations), we further explained the best-performing survival model's predictions. Our explainable predictive model is a potential tool for oncologists that address an unmet clinical need for post-treatment patient stratification based on the relapse hazard.
ER  -

TIMILSINA, Mohan, Samuele BUOSI, Adrianna JANIK, Pasquale MINERVINI, Luca COSTABELLO, Maria TORRENTE, Mariano PROVENCIO, Virginia CALVO, Carlos CAMPS, Ana L ORTEGA, Bartomeu MASSUTI, Rosario Garcia M. CAMPELO, del Barco EDEL, Joaquim BOSCH-BARRERA a Vít NOVÁČEK. Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient. Online. In \textit{2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN}. Broadbeach, Australia: IEEE, 2023, s.~1-8. ISBN~978-1-6654-8867-9. Dostupné z: https://dx.doi.org/10.1109/IJCNN54540.2023.10191078.

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