Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels.

IF 2.3 3区 生物学 Q2 MULTIDISCIPLINARY SCIENCES PeerJ Pub Date : 2024-12-03 eCollection Date: 2024-01-01 DOI:10.7717/peerj.18618
Jimin Hao, Man Liu, Zhigang Zhou, Chunling Zhao, Liping Dai, Songyun Ouyang
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Abstract

Background: Approximately 60% of Asian populations with non-small cell lung cancer (NSCLC) harbor epidermal growth factor receptor (EGFR) gene mutations, marking it as a pivotal target for genotype-directed therapies. Currently, determining EGFR mutation status relies on DNA sequencing of histological or cytological specimens. This study presents a predictive model integrating clinical parameters, computed tomography (CT) characteristics, and serum tumor markers to forecast EGFR mutation status in NSCLC patients.

Methods: Retrospective data collection was conducted on NSCLC patients diagnosed between January 2018 and June 2019 at the First Affiliated Hospital of Zhengzhou University, with available molecular pathology results. Clinical information, CT imaging features, and serum tumor marker levels were compiled. Four distinct models were employed in constructing the diagnostic model. Model diagnostic efficacy was assessed through receiver operating characteristic (ROC) area under the curve (AUC) values and calibration curves. DeLong's test was administered to validate model robustness.

Results: Our study encompassed 748 participants. Logistic regression modeling, trained with the aforementioned variables, demonstrated remarkable predictive capability, achieving an AUC of 0.805 (95% confidence interval (CI) [0.766-0.844]) in the primary cohort and 0.753 (95% CI [0.687-0.818]) in the validation cohort. Calibration plots suggested a favorable fit of the model to the data.

Conclusions: The developed logistic regression model emerges as a promising tool for forecasting EGFR mutation status. It holds potential to aid clinicians in more precisely identifying patients likely to benefit from EGFR molecular testing and facilitating targeted therapy decision-making, particularly in scenarios where molecular testing is impractical or inaccessible.

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背景:亚洲非小细胞肺癌(NSCLC)患者中约有 60% 存在表皮生长因子受体(EGFR)基因突变,这标志着表皮生长因子受体已成为基因型定向疗法的关键靶点。目前,确定表皮生长因子受体基因突变状态依赖于组织学或细胞学标本的 DNA 测序。本研究提出了一个综合临床参数、计算机断层扫描(CT)特征和血清肿瘤标志物的预测模型,用于预测NSCLC患者的表皮生长因子受体(EGFR)突变状态:对2018年1月至2019年6月期间在郑州大学第一附属医院确诊并有分子病理结果的NSCLC患者进行回顾性数据收集。对患者的临床信息、CT影像学特征和血清肿瘤标志物水平进行了整理。在构建诊断模型时采用了四种不同的模型。通过接收者操作特征(ROC)曲线下面积(AUC)值和校准曲线评估模型的诊断效果。为了验证模型的稳健性,还进行了德隆测试:我们的研究涵盖了 748 名参与者。利用上述变量训练的逻辑回归模型显示出卓越的预测能力,在主要队列中的 AUC 值为 0.805(95% 置信区间 (CI) [0.766-0.844]),在验证队列中的 AUC 值为 0.753(95% 置信区间 (CI) [0.687-0.818])。校准图显示模型与数据的拟合度良好:结论:所开发的逻辑回归模型是预测表皮生长因子受体突变状态的有效工具。它有望帮助临床医生更准确地识别可能从表皮生长因子受体分子检测中获益的患者,并促进靶向治疗决策,尤其是在分子检测不切实际或无法进行的情况下。
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来源期刊
PeerJ
PeerJ MULTIDISCIPLINARY SCIENCES-
CiteScore
4.70
自引率
3.70%
发文量
1665
审稿时长
10 weeks
期刊介绍: PeerJ is an open access peer-reviewed scientific journal covering research in the biological and medical sciences. At PeerJ, authors take out a lifetime publication plan (for as little as $99) which allows them to publish articles in the journal for free, forever. PeerJ has 5 Nobel Prize Winners on the Board; they have won several industry and media awards; and they are widely recognized as being one of the most interesting recent developments in academic publishing.
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