影像组学在预测肺腺癌 EGFR、TP53 突变中的应用
摘要
实施肺癌筛查计划后,肺癌尤其是肺腺癌的检出率逐年增加,肺腺癌已成为肺癌最常见的亚型,基因突变率最高。肺癌驱
动基因的发现,尤其是表皮生长因子受体(EGFR)基因,增加了个体化靶向治疗的采用。肺腺癌患者中,EGFR 突变的患
者对 EGFR 酪氨酸激酶抑制剂(TKI)反应良好,而没有这些突变的患者在疾病任何阶段均不适合接受 EGFR-TKI 治疗 [2]。
然而,部分患者表现出对 EGFR-TKI 的原发性耐药,部分患者即便初始有反应,也存在耐药性不可避免地发展。TP53 的
治疗前改变是与一线无进展生存期和总生存期降低相关的最常见的并发基因组事件。因此,在对肺腺癌患者进行靶向药物
治疗之前,检测相关基因的表达状态极为重要。通过活检或手术切除获得的肿瘤组织的分子检测是鉴定基因突变的金标准,
但是组织样本的采集伴随着侵入性,不可避免会存在一定的采样误差。影像组学可以从图像中提取定量特征,全面分析生
物学信息,评估肿瘤异质性。在反映肿瘤基因表型等方面表现出了相应潜力,在预测肺腺癌基因突变方面表现出良好的应
用前景,为临床提供了一种无创、简便的检测手段。
关键词
全文:
PDF参考
[1] S i e g e l R L , M i l l e r K D , W a g l e N S , J e m a l A .
Cancer statistics, 2023. CA: a cancer journal for clinicians.
2023;73(1):17-48.
[2] Ettinger DS, Wood DE, Aggarwal C, Aisner DL,
Akerley W, Bauman JR, et al. NCCN Guidelines Insights: Non-
Small Cell Lung Cancer, Version 1.2020. Journal of the National
Comprehensive Cancer Network : JNCCN. 2019;17(12):1464-72.
[3] Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho
S, van Stiphout RG, Granton P, et al. Radiomics: extracting
more information from medical images using advanced feature
analysis. European journal of cancer (Oxford, England : 1990).
2012;48(4):441-6.
[4] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature.
2015;521(7553):436-44.
[5] Liu Y, Kim J, Qu F, Liu S, Wang H, Balagurunathan
Y, et al. CT Features Associated with Epidermal Growth Factor
Receptor Mutation Status in Patients with Lung Adenocarcinoma.
Radiology. 2016;280(1):271-80.
[6] Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, et al.
Exploring non-invasive precision treatment in non-small cell
lung cancer patients through deep learning radiomics across
imaging features and molecular phenotypes. Biomarker research.
2024;12(1):12.
[7] Zhu Y, Guo YB, Xu D, Zhang J, Liu ZG, Wu X, et al.
A computed tomography (CT)-derived radiomics approach for
predicting primary co-mutations involving TP53 and epidermal
growth factor receptor (EGFR) in patients with advanced lung
adenocarcinomas (LUAD). Annals of translational medicine.
2021;9(7):545.
[8] Wang XY, Wu SH, Ren J, Zeng Y, Guo LL. Predicting
Gene Comutation of EGFR and TP53 by Radiomics and Deep
Learning in Patients With Lung Adenocarcinomas. Journal of
thoracic imaging. 2024.
(3 摘要 Views, 15 PDF Downloads)
Refbacks
- 当前没有refback。