相关实验视频

Updated: May 9, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

肺癌免疫预后得分预测了接受新辅助免疫化学疗法的NSCLC患者的病理完整反应和生存率

Yuyan Xie¹, Zhihao Shi¹, Tong Chen¹

¹Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China.

Frontiers in immunology

|May 1, 2025

概括

一个新的肺癌免疫预后得分 (LCIPS) 预测了接受新辅助免疫化学疗法 (nICT) 的非小细胞肺癌 (NSCLC) 患者的治疗反应和生存率. 这个分数有助于指导个性化治疗策略,以获得更好的结果.

科学领域:

在瘤学瘤学.
免疫治疗是一种免疫疗法.
生物标志物发现发现

背景情况:

新辅助免疫化学疗法 (nICT) 改善了可切除非小细胞肺癌 (NSCLC) 的结果.
缺乏用于nICT疗效和生存的预测生物标志物.
个性化治疗策略需要经过验证的预后工具.

研究的目的:

开发和验证一种新的炎症和营养指数,即肺癌免疫预后得分 (LCIPS).
评估LCIPS在接受nICT的NSCLC患者中预测病理完整反应 (pCR) 和生存的能力.

主要方法:

对131名用nICT治疗的NSCLC患者的回顾性分析.
拉索回归以确定关键的血液学指数 (白蛋白与血球蛋白比,绝对单细胞和淋巴细胞计数).
使用ROC曲线,物流/Cox回归和名ograms开发LCIPS公式和验证.

主要成果:

在LCIPS中包含了白蛋白与血球蛋白的比例,绝对单细胞数和绝对淋巴细胞数.
与其他标志物相比,LCIPS对pCR的预测效率 (AUC=0.68) 更高.
LCIPS和白与球蛋白比率是PCR的独立预测指标;LCIPS和吸烟状态预测了DFS和OS.

结论:

LCIPS是一种新的,独立的pCR预测器,用于接受nICT的NSCLC患者.
LCIPS与改善的无病存活率 (DFS) 和整体存活率 (OS) 相关.
该LCIPS为NSCLC的个性化治疗策略提供了宝贵的指导.

关键词:

炎症指数和营养指数.新辅助免疫化学疗法新辅助免疫化学疗法非小细胞肺癌的肺癌.病理学的完整反应.预后因素预后因素

更多相关视频

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Published on: August 14, 2019

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Published on: February 7, 2021

相关概念视频

Tumor Immunotherapy

Tumor Immunotherapy

Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...