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

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

Základní údaje

• Originální název: On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer
• Autoři: MOHAMED, Sameh K., Brian WALSH, Mohan TIMILSINA, Maria TORRENTE, Fabio FRANCO, Mariano PROVENCIO, Adrianna JANIK, Luca COSTABELLO, Pasquale MINERVINI, Pontus STENETORP a Vít NOVÁČEK (203 Česká republika, garant, domácí).
• Vydání: San Diego, Proceedings of AMIA 2021 Annual Symposium, od s. 853-862, 10 s. 2021.
• Nakladatel: AMIA

Další údaje

• Originální 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/00216224:14330/21:00125683
• Organizační jednotka: Fakulta informatiky
• ISSN: 1942-597X
• Klíčová slova anglicky: machine learning; lung cancer; relapse prediction
• Štítky: Artificial Intelligence, firank_A, machine learning, medical informatics, relapse prediction
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.