肺部大细胞神经内分泌癌患者的预后因素和预测模型:基于SEER数据库
Wenqiang Li1, Qian Huang2, Xiaoyu He3
1Zigong First People's Hospital, Zigong City, Sichuan Province, China.
The clinical respiratory journal
|April 12, 2024
概括
这项研究开发了一种新型的诺米图,用于预测肺大细胞神经内分泌癌 (LCNEC) 患者的预后. 该模型准确地识别了风险因素,有助于为这种侵略性癌症做出个性化治疗决策.
科学领域:
- 在瘤学瘤学.
- 医学统计 医学统计
- 预测建模预测建模
背景情况:
- 肺大细胞神经内分泌癌 (LCNEC) 是一种罕见的,具有不良预后的侵袭性癌症.
- 对于LCNEC,以前没有预测模型.
研究的目的:
- 开发和验证LCNEC患者预后的预测模型.
- 确定影响LCNEC结果的独立风险因素.
主要方法:
- 在LCNEC案件中使用了SEER数据库 (2010-2018).
- 进行单变量和多变量考克斯回归分析.
- 使用ROC曲线,校准曲线和DCA构建和验证了一个名ogram.
主要成果:
- 确定了N阶段,肺内转移,骨转移,大脑转移和手术作为独立的风险因素.
- 在培训和验证队伍中,开发的诺莫图表证明了准确的预后预测.
- 通过ROC曲线,校准曲线和决策曲线分析证实了诺莫格拉姆的性能.
结论:
- 这种新型的诺莫格拉姆作为一个有价值的工具,可以在LCNEC中进行个性化的预后预测.
- 这种工具可能有助于临床医生为LCNEC患者做出明智的治疗决定.
相关概念视频
Cancer Survival Analysis
345
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...
345
lncRNA - Long Non-coding RNAs
8.6K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
8.6K


