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使用多重填补法校正按组织学亚型划分的肺癌发病率趋势中的偏差。

Use of multiple imputation to correct for bias in lung cancer incidence trends by histologic subtype.

作者信息

Yu Mandi, Feuer Eric J, Cronin Kathleen A, Caporaso Neil E

机构信息

Division of Cancer Control and Population Sciences; and

Division of Cancer Control and Population Sciences; and.

出版信息

Cancer Epidemiol Biomarkers Prev. 2014 Aug;23(8):1546-58. doi: 10.1158/1055-9965.EPI-14-0130. Epub 2014 May 22.

Abstract

BACKGROUND

Over the past several decades, advances in lung cancer research and practice have led to refinements of histologic diagnosis of lung cancer. The differential use and subsequent alterations of nonspecific morphology codes, however, may have caused artifactual fluctuations in the incidence rates for histologic subtypes, thus biasing temporal trends.

METHODS

We developed a multiple imputation (MI) method to correct lung cancer incidence for nonspecific histology using data from the Surveillance, Epidemiology, and End Results Program during 1975 to 2010.

RESULTS

For adenocarcinoma in men and squamous in both genders, the change to an increasing trend around 2005, after more than 10 years of decreasing incidence, is apparently an artifact of the changes in histopathology practice and coding system. After imputation, the rates remained decreasing for adenocarcinoma and squamous in men, and became constant for squamous in women.

CONCLUSIONS

As molecular features of distinct histologies are increasingly identified by new technologies, accurate histologic distinctions are becoming increasingly relevant to more effective "targeted" therapies, and therefore, are important to track in patients. However, without incorporating the coding changes, the incidence trends estimated for histologic subtypes could be misleading.

IMPACT

The MI approach provides a valuable tool for bridging the different histology definitions, thus permitting meaningful inferences about the long-term trends of lung cancer by histologic subtype.

摘要

背景

在过去几十年中,肺癌研究和实践的进展使得肺癌的组织学诊断得到了改进。然而,非特异性形态学编码的不同使用及其后续变化可能导致组织学亚型发病率出现人为波动,从而使时间趋势产生偏差。

方法

我们开发了一种多重填补(MI)方法,利用1975年至2010年监测、流行病学和最终结果计划的数据,对非特异性组织学的肺癌发病率进行校正。

结果

对于男性腺癌和两性的鳞状细胞癌,在发病率下降超过10年后,2005年左右转为上升趋势显然是组织病理学实践和编码系统变化的人为结果。填补后,男性腺癌和鳞状细胞癌的发病率仍呈下降趋势,女性鳞状细胞癌的发病率则变得稳定。

结论

随着新技术越来越多地识别出不同组织学的分子特征,准确的组织学区分对于更有效的“靶向”治疗变得越来越重要,因此,对患者进行跟踪很重要。然而,如果不纳入编码变化,组织学亚型的发病率趋势估计可能会产生误导。

影响

MI方法为弥合不同的组织学定义提供了一个有价值的工具,从而能够对肺癌组织学亚型的长期趋势进行有意义的推断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d71/4119525/de334b1ef176/nihms597765f1.jpg

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