Wang Yuhang, Lin Xuefeng, Sun Daqiang
Graduate School, Tianjin Medical University, Tianjin, China.
Tianjin Medical College, Tianjin, China.
Ann Transl Med. 2021 Oct;9(20):1597. doi: 10.21037/atm-21-4733.
To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).
Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.
PubMed and the Cochrane Library were searched using the items "NSCLC", "prognostic model", "prognosis prediction", and "survival prediction" from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.
The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.
通过总结非小细胞肺癌（NSCLC）现有的预后预测模型，发现潜在的预测因子并探索如何构建更好的模型。
近年来，NSCLC临床预测模型的研究呈爆发式增长。随着更多预后预测因子的发现，构建模型时预测因子的选择尤为重要，且在下一代测序技术应用增多的背景下，基因相关预测因子被广泛使用。由于获取样本和随访数据更加便捷，预后模型受到研究者的青睐。
使用“NSCLC”“预后模型”“预后预测”和“生存预测”等关键词，检索1980年1月1日至2021年5月5日期间的PubMed和Cochrane图书馆。对文章的参考文献列表进行审查并确定相关文章。
基因相关模型的性能并未明显改善。相对于预测因子的创新性和多样性，建立一个便于临床应用的高度稳定的模型更为重要。大多数流行模型存在高度偏倚，在研究开始时参考PROBAST可能能够显著控制偏倚。现有模型应在大型外部数据集中进行验证，以进行有意义的比较。
