| 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. |
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@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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