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.

翻译：癌症是全球最普遍的疾病之一，每年新增数百万患者。膀胱癌是最常见的癌症类型之一，影响所有个体，且无明显典型患者特征。当前膀胱癌的标准治疗方案采用每周规律性卡介苗免疫治疗规程，对所有患者统一施治。由于免疫系统、治疗方案与癌细胞之间相互作用的生物学及临床复杂性，不同患者接受卡介苗治疗的临床结局存在显著差异。本研究利用患者的社会人口学特征，构建了个性化数学模型以描述基于卡介苗治疗的临床动力学过程。为此，我们采用经过充分验证的卡介苗治疗模型，并集成机器学习组件以动态调整和重构模型关键参数，从而提升其个性化程度。基于真实临床数据，我们证明该个性化模型在预测治疗结束时癌细胞数量方面，较原始模型平均提升14.8%。