Abstract
Cancer is one of the most widespread diseases around the world with millions of new patients each year. Bladder cancer is one of the most prevalent types of cancer affecting all individuals alike with no obvious “prototypical patient”. The current standard treatment for BC follows a routine weekly Bacillus Calmette-Guérin (BCG) immunotherapy-based therapy protocol which is applied to all patients alike. The clinical outcomes associated with BCG treatment vary significantly among patients due to the biological and clinical complexity of the interaction between the immune system, treatments, and cancer cells. In this study, we take advantage of the patient’s socio-demographics to offer a personalized mathematical model that describes the clinical dynamics associated with BCG-based treatment. To this end, we adopt a well-established BCG treatment model and integrate a machine learning component to temporally adjust and reconfigure key parameters within the model thus promoting its personalization. Using real clinical data, we show that our personalized model favorably compares with the original one in predicting the number of cancer cells at the end of the treatment, with 14.8 % improvement, on average.
| Original language | English |
|---|---|
| Article number | 18754 |
| Journal | Scientific Reports |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| State | Published - 31 Oct 2023 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s).
Funding
The authors wish to thank Sarel Halachmi for providing the data used in this work. Elizaveta Savchenko wishes to thank Ariel University’s financial support during this research. The authors wish to thank Sarel Halachmi for providing the data used in this work. Elizaveta Savchenko wishes to thank Ariel University’s financial support during this research.
| Funders | Funder number |
|---|---|
| Sarel Halachmi | |
| Ariel University |
