PDF(Pubmed)
Abstract：
In the pursuit of advancing cancer therapy, this study explores the predictive power of machine learning in analyzing tumor characteristics, specifically focusing on the effects of tumor stiffness and perfusion (i.e., blood flow) on treatment efficacy. Recent advancements in oncology have highlighted the significance of these physiological properties of the tumor microenvironment in determining treatment outcomes. We delve into the relationship between these tumor attributes and the effectiveness of cancer therapies in preclinical tumor models. Utilizing robust statistical methods and machine learning algorithms, our research analyzes data from 1365 cases of various cancer types, assessing how tumor stiffness and perfusion influence the efficacy of treatment protocols. We also investigate the synergistic potential of combining drugs that modulate tumor stiffness and perfusion with standard cytotoxic treatments. By incorporating these predictors into treatment planning, our study aims to enhance the precision of cancer therapy, tailoring treatment to individual tumor profiles. Our findings demonstrate a significant correlation between stiffness/perfusion and treatment efficacy, highlighting a new way for personalized cancer treatment strategies.
摘要：
为了推进癌症治疗，这项研究探讨了机器学习在分析肿瘤特征方面的预测能力，特别关注肿瘤硬度和灌注的影响(即，血流量）对治疗疗效。肿瘤学的最新进展突出了肿瘤微环境的这些生理特性在确定治疗结果中的重要性。我们深入研究了这些肿瘤属性与临床前肿瘤模型中癌症治疗有效性之间的关系。利用稳健的统计方法和机器学习算法，我们的研究分析了1365例各种癌症类型的数据，评估肿瘤僵硬度和灌注如何影响治疗方案的疗效。我们还研究了将调节肿瘤僵硬度和灌注的药物与标准细胞毒性治疗相结合的协同潜力。通过将这些预测因素纳入治疗计划，我们的研究旨在提高癌症治疗的精确度，根据个体肿瘤特征定制治疗。我们的研究结果表明，僵硬/灌注和治疗效果之间存在显著的相关性，突出个性化癌症治疗策略的新途径。
