A Survival Analysis Approach to Predicting Severe Leukopenia in Advanced Breast Cancer Patients

Vodenčarević A, Kreuzeder J, Wöckel A, Fasching P (2024)

Publication Type: Conference contribution

Publication year: 2024

Journal

Communications in Computer and Information Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 2105 CCIS

Pages Range: 127-140

Conference Proceedings Title: Communications in Computer and Information Science

Event location: Rome IT

ISBN: 9783031689185

DOI: 10.1007/978-3-031-68919-2_6

Abstract

Modern therapies for advanced breast cancer can significantly prolong survival and improve patient’s quality of life. Yet, in some patients they can be associated with different, sometimes severe toxicities. Predicting these toxicities in individual patients would enable more targeted monitoring, toxicity prevention and could help saving scarce healthcare resources. Previously we have shown that individual prediction of a cardio toxicity called QT prolongation using survival modelling algorithms is feasible. In this work we target the individual prediction of a blood toxicity called severe leukopenia using the same modelling approach. Seven statistical and machine learning survival modelling algorithms are trained, optimized, validated and compared on both clinical trial and real-world data. The statistical Cox proportional hazards model reached rather modest performance of about 0.6 (Uno’s concordance index) and slightly outperformed its machine learning counterparts. The strongest predictors include variables related to prior therapies, location of metastasis, histologic grade as well as several quality of life scores.

Authors with CRIS profile

Peter Fasching Professur für Translationale Frauenheilkunde und Geburtshilfe

Involved external institutions

Novartis AG CH Switzerland (CH) Universitätsklinikum Würzburg DE Germany (DE)

How to cite

APA:

Vodenčarević, A., Kreuzeder, J., Wöckel, A., & Fasching, P. (2024). A Survival Analysis Approach to Predicting Severe Leukopenia in Advanced Breast Cancer Patients. In Oleg Gusikhin, Slimane Hammoudi, Alfredo Cuzzocrea (Eds.), Communications in Computer and Information Science (pp. 127-140). Rome, IT: Springer Science and Business Media Deutschland GmbH.

MLA:

Vodenčarević, Asmir, et al. "A Survival Analysis Approach to Predicting Severe Leukopenia in Advanced Breast Cancer Patients." Proceedings of the 12th International Conference on Data Management Technologies and Applications, DATA 2023, Rome Ed. Oleg Gusikhin, Slimane Hammoudi, Alfredo Cuzzocrea, Springer Science and Business Media Deutschland GmbH, 2024. 127-140.

BibTeX: Download