# Mathematical Modeling of BCG-based Bladder Cancer Treatment Using Socio-Demographics

arXiv: https://arxiv.org/abs/2307.15084 · HTML: https://mlsanity.com/paper/2307.15084/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 72 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Optimizing SMS Reminder Campaigns for Pre- and Post-Diagnosis Cancer Check-Ups using Socio-Demographics: An In-Silco Investigation Into Bladder Cancer](https://mlsanity.com/paper/2305.03126/) (2023)

## Built on by (most cited first, 2 of 2)

- [Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review](https://mlsanity.com/paper/2401.06406/) (2024)
- [Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift](https://mlsanity.com/paper/2412.09035/) (2024)

## Abstract (arXiv)

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.