在治疗期间拦截癌症:使用生长动力学来创建评估疾病反应的持续变量
Mengxi Zhou1, Antonion T Fojo1, Lawrence H Schwartz2
1Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center, New York, NY and James J Peters VA Medical Centre, Bronx, NY, USA.
The oncologist
|October 22, 2025
概括
一个简单的数学模型准确地描述了大多数患者的瘤生长动力学,生长率与整体存活率相反相关. 这一发现为治疗各种癌症的疗效提供了一个新的标志物.
科学领域:
- 在瘤学瘤学.
- 数学建模的数学建模
- 生物统计学 生物统计学
背景情况:
- 11个瘤生长的数学模型被应用于广泛的临床试验数据.
- 一种比特指数模型以前在描述瘤生长动力学和与生存相关性方面表现出有效性.
- 这项研究旨在将分析扩展到更多的癌症类型,并评估对具有挑战性的数据集的替代模型.
研究的目的:
- 验证和扩展一个简单的数学模型用于瘤生长动力学的应用.
- 在主模型失败时评估替代模型的实用性.
- 评估不同类型癌症瘤生长率与整体存活率之间的相关性.
主要方法:
- 分析了来自17140名患者的数据,包括成像和血清瘤标志物.
- 应用比特指数模型来确定瘤生长率 (g) 和回归率 (d).
- 对数据集的七种替代模型的评估,这些模型不符合比率模型.
- 检查连续增长率 (g率) 与整体存活率之间的关联.
主要成果:
- 双指数模型成功地描述了86%的患者的瘤生长和回归.
- 另一种模型适应了另外7%的患者的数据.
- 瘤生长率与整体存活率呈反相关性,在不同组织学上一致.
- 即使在具有足够数据点的净回归阶段,也可以估计增长率.
结论:
- 一个简单的数学模型有效量化了各种癌症的瘤生长.
- 估计的瘤生长率作为治疗疗效的强有力的标志物.
- 这种方法可以估计耐治疗的癌细胞亚群.
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