Mathematical Modeling of BCG-based Bladder Cancer Treatment Using Socio-Demographics
This work addresses the need for personalized treatment in bladder cancer patients, offering an incremental improvement over existing methods.
The study tackled the problem of variable clinical outcomes in BCG-based bladder cancer treatment by developing a personalized mathematical model that integrates patient socio-demographics and machine learning, resulting in a 14.8% average improvement in predicting cancer cell counts compared to the original model.
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-Guerin (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.