Detailed Information on Publication Record
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.Basic information
Original name
Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient
Authors
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 and Vít NOVÁČEK (203 Czech Republic, belonging to the institution)
Edition
Broadbeach, Australia, 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, p. 1-8, 8 pp. 2023
Publisher
IEEE
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
RIV identification code
RIV/00216224:14330/23:00133943
Organization unit
Faculty of Informatics
ISBN
978-1-6654-8867-9
ISSN
UT WoS
001046198700044
Keywords in English
survival; time; event; prediction; cancer; explanation
Tags
International impact, Reviewed
Změněno: 8/4/2024 11:32, RNDr. Pavel Šmerk, Ph.D.
Abstract
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.