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Tumor Progression

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

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Updated: Jun 3, 2025

Measuring Progressive Neurological Disability in a Mouse Model of Multiple Sclerosis

Measuring Progressive Neurological Disability in a Mouse Model of Multiple Sclerosis

Published on: November 14, 2016

在渐进的多发性硬化症中,残疾进展的数据驱动模型.

Sara Garbarino^1,2, Carmen Tur³, Marco Lorenzi⁴

¹Life Science Computational laboratory, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy.

Brain communications

|January 8, 2025

概括

这项研究使用贝叶斯模型揭示了明显的初级渐进性多发性硬化症 (PPMS) 进展率. 它确定了三个患者子组:规范性,加速性和减速性,有助于个性化治疗策略.

关键词:

贝叶斯式学习是贝叶斯式学习.PPMS子组中的PPMS子组.数据驱动的疾病进展建模.多式联运数据是多式联运数据.原发性进展性多发性硬化症.

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Last Updated: Jun 3, 2025

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科学领域:

神经科学是一个神经科学.
生物统计学生物统计学
医疗成像医学成像

背景情况:

初级渐进性多发性硬化症 (PPMS) 的特征是持续的神经衰退.
了解个体进展变异性对于有效的管理和临床试验至关重要.
现有的模型可能无法完全捕捉PPMS进展的异质性.

研究的目的:

应用贝叶斯数据驱动的疾病进展模型 (高斯过程进展模型) 来分析PPMS演变.
根据个体进展率来确定不同的患者子组.
调查这些子组的预后影响.

主要方法:

利用了1521名PPMS参与者 (国际渐进性多发性硬化症联盟项目) 的数据,其中包括纵向残疾和MRI指标.
应用了高斯过程进展模型来推断人口水平进展和个体速率.
在SPI2试验数据上使用Cox比例危险建模进行预后分析和外部验证.

主要成果:

确定了三个PPMS子组:规范性 (76%),加速性 (13%),减缓性 (11%) 的进展.
快速进展的患者表现出更早的症状发作,更高的男性患病率和更大的病变体积.
快速进展的患者的预后更差,确认残疾进展的风险增加了一倍,达到扩展残疾状态量表6的时间更短.

结论:

高斯的过程进展模型有效地描述了PPMS的异质性.
不同的子组 (快速,规范,缓慢的进展者) 具有显著的预后差异.
研究结果支持临床试验中的患者分层和PPMS个性化干预策略.