Scheduling BCG and IL-2 Injections for Bladder Cancer Immunotherapy Treatment

Abstract

Cancer is one of the most common families of diseases today with millions of new patients every year around the world. Bladder cancer (BC) is one of the most prevalent types of cancer affecting both genders, and it is not known to be associated with a specific group in the population. The current treatment standard for BC follows a standard weekly Bacillus Calmette–Guérin (BCG) immunotherapy-based therapy protocol which includes BCG and IL-2 injections. Unfortunately, due to the biological and clinical complexity of the interactions between the immune system, treatment, and cancer cells, clinical outcomes vary significantly among patients. Unfortunately, existing models are commonly developed for a non-existing average patient or pose strict, unrealistic, expectations on the treatment process. In this work, we propose the most extensive ordinary differential equation-based biological model of BCG treatment to date and a deep learning-based scheduling approach to obtain a personalized treatment schedule. Our results show that resulting treatment schedules favorably compare with the current standard practices and the current state-of-the-art scheduling approach.

Mathematical modeling of BCG-based bladder cancer treatment using socio-demographics

“…Potentially, by using more parameters, one can obtain better personalization and even further improve the model’s prediction accuracy 70 . Finally, our model focuses on the treatment protocol clinical outcome prediction rather than suggesting an optimal personalized treatment as other studies do 26 . In future work, we plan to further investigate this direction.…”

“…One promising avenue for improving BCG-based treatment protocols is changing the currently practiced “one-size-fits-all” approach with a more personalized one 26 , 27 . Specifically, by taking social and behavioral factors into account, one is likely to obtain a more favorable treatment prediction model at the individual patient’s level and lead to better clinical outcomes as treatment optimization models would be able to have an underlined more accurate outcome prediction model 28 .…”

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

“…Van Netten et al ( 46 ) investigated the future of personalized treatment for diabetic foot ulcer prevention, showing that using clinical and treatment clinical outcomes of similar patients is able to improve the treatment protocol patients obtain as both diagnosis conditions and treatment protocols are influenced by these factors. Yaniv-Rosenfeld et al ( 47 ) proposed a deep-learning-based model for BCG and IL-2 injections for BC immunotherapy treatment. The authors show that the personalized treatment protocol, which requires more often tests, outperforms the generic treatment protocol with a 12 percent higher success rate.…”

“…The authors show that the personalized treatment protocol, which requires more often tests, outperforms the generic treatment protocol with a 12 percent higher success rate. In a different work, Yaniv-Rosenfeld et al ( 47 ) show that using socio-demographic data, mental health professionals can better estimate the stay duration required by borderline personality disorder, which should improve their overall treatment, according to the authors. In this setting, the personalization requires one to fulfill a short personal questionnaire which is usually part of the administration process anyway, causing small to no additional effort to the HSPs.…”