ArticleScientific reports2023
Mathematical modeling of BCG-based bladder cancer treatment using socio-demographics.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- BCGitis and BCGosis: Clinical Spectrum, Immunological Mechanisms, and Risk Management.Vaccines · 2025Review
- Examining Patient Characteristics in Bladder Cancer Clinical Trials Involving Immunotherapy.Journal of clinical medicine · 2025Review
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model.Frontiers in immunology · 2024Article
- Asymptotic analysis of mathematical model describing a new treatment of breast cancer using AZD9496 and palbociclib.Frontiers in oncology · 2024Article
- Predicting lung cancer's metastats' locations using bioclinical model.Frontiers in medicine · 2024Article
- Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer.Frontiers in medicine · 2024Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 [Formula: see text] improvement, on average.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.