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相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

453
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...
453

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Epstein-Barr virus-associated lymphoepithelioma-like intrahepatic cholangiocarcinoma with concurrent hepatitis B virus infection: case report and literature review.

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Engineered immune cells and extracellular vesicles target tumour microenvironment barriers in solid tumour immunotherapy.

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A bibliometric map of C. elegans in cancer research from 2005 to 2025.

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Predictive value of apparent diffusion coefficient, plasma levels of placental growth factor and glial fibrillary acidic protein, and their combinations for patients with recurrent glioblastoma.

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Metformin in endometrial cancer from metabolic regulation to precision therapeutics.

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Global prevalence and disability-adjusted life years of hepatoblastoma in children aged 0 to 4 years.

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相关实验视频

Updated: Sep 10, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Published on: October 23, 2020

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用TARGET数据库驱动的小儿骨髓瘤预后

Jianfeng Li1, Jiayi Li1, Jianjun Wang1

  • 1Zhuhai People's Hospital (Jinan University Zhuhai Clinical Medical College), No. 79 Kangning Road, Xiangzhou District, Zhuhai City, Guangdong Province, 519000, China.

Discover oncology
|August 20, 2025
PubMed
概括
此摘要是机器生成的。

一个新的风险预测模型准确地确定了小儿骨髓瘤患者的生存率. 这种工具有助于优化治疗策略,旨在改善骨髓瘤儿童的治疗结果和生活质量.

关键词:
一个名字骨髓瘤儿童药物风险预测模型生存分析目标数据库

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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科学领域:

  • 儿童瘤学
  • 癌症风险建模
  • 生存分析

背景情况:

  • 骨肉瘤是一种罕见但具有攻击性的骨癌,影响儿童和青少年.
  • 准确的风险分层对于定制治疗和改善患者的结果至关重要.

研究的目的:

  • 为了确定儿童骨髓瘤的危险因素.
  • 在儿童患者中开发和验证骨髓瘤特异性生存预测模型.

主要方法:

  • 来自TARGET数据库的129例儿科骨肉瘤病例 (2000 - 2013) 的回顾性分析.
  • 考克斯的比例风险建模,以确定独立的预测因素.
  • 使用C指数,ROC曲线,校准曲线和决策曲线分析构建和验证nomogram.

主要成果:

  • 一个由六个变量组成的模型 (性别,种族,瘤侧面/区域,复发地点/时间) 显示出良好的分辨能力 (C指数为0. 802,5年生存率为0. 787).
  • 该模型显示预测和实际存活之间的高度一致性,具有显著的临床实用性.
  • 卡普兰-梅尔分析证实高风险与低风险组的不同预测.

结论:

  • 开发的诺米图是预测小儿骨髓瘤存活率的有效工具.
  • 这种模式可以指导治疗优化,从而提高生存率和生活质量.