On Predicting Recurrence in Early Stage Non-small Cell Lung
Cancer

D 2021

On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer

MOHAMED, Sameh K., Brian WALSH, Mohan TIMILSINA, Maria TORRENTE, Fabio FRANCO et. al.

Basic information

Original name

On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer

Authors

Edition

San Diego, Proceedings of AMIA 2021 Annual Symposium, p. 853-862, 10 pp. 2021

Publisher

AMIA

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/00216224:14330/21:00125683

Organization unit

Faculty of Informatics

ISSN

Keywords in English

machine learning; lung cancer; relapse prediction

Abstract

V originále

Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients. We discuss the promising results achieved, as well as the lessons we learned while developing this baseline for further, more advanced studies in this area.

Citovat

MOHAMED, Sameh K., Brian WALSH, Mohan TIMILSINA, Maria TORRENTE, Fabio FRANCO, Mariano PROVENCIO, Adrianna JANIK, Luca COSTABELLO, Pasquale MINERVINI, Pontus STENETORP and Vít NOVÁČEK. On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer. Online. In Adam B. Wilcox, Randi Foraker, Kensaku Kawamoto, Yves A. Lussier, Nadine McCleary. Proceedings of AMIA 2021 Annual Symposium. San Diego: AMIA, 2021, p. 853-862. ISSN 1942-597X.

@inproceedings{1848076,
   author = {Mohamed, Sameh K. and Walsh, Brian and Timilsina, Mohan and Torrente, Maria and Franco, Fabio and Provencio, Mariano and Janik, Adrianna and Costabello, Luca and Minervini, Pasquale and Stenetorp, Pontus and Nováček, Vít},
   address = {San Diego},
   booktitle = {Proceedings of AMIA 2021 Annual Symposium},
   editor = {Adam B. Wilcox, Randi Foraker, Kensaku Kawamoto, Yves A. Lussier, Nadine McCleary},
   keywords = {machine learning; lung cancer; relapse prediction},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {San Diego},
   pages = {853-862},
   publisher = {AMIA},
   title = {On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer},
   year = {2021}
}

TY  - JOUR
ID  - 1848076
AU  - Mohamed, Sameh K. - Walsh, Brian - Timilsina, Mohan - Torrente, Maria - Franco, Fabio - Provencio, Mariano - Janik, Adrianna - Costabello, Luca - Minervini, Pasquale - Stenetorp, Pontus - Nováček, Vít
PY  - 2021
TI  - On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer
PB  - AMIA
CY  - San Diego
KW  - machine learning
KW  - lung cancer
KW  - relapse prediction
N2  - Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients. We discuss the promising results achieved, as well as the lessons we learned while developing this baseline for further, more advanced studies in this area.
ER  -

MOHAMED, Sameh K., Brian WALSH, Mohan TIMILSINA, Maria TORRENTE, Fabio FRANCO, Mariano PROVENCIO, Adrianna JANIK, Luca COSTABELLO, Pasquale MINERVINI, Pontus STENETORP and Vít NOVÁČEK. On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer. Online. In Adam B. Wilcox, Randi Foraker, Kensaku Kawamoto, Yves A. Lussier, Nadine McCleary. \textit{Proceedings of AMIA 2021 Annual Symposium}. San Diego: AMIA, 2021, p.~853-862. ISSN~1942-597X.

Detailed Information on Publication Record