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基于CT的影像组学特征作为克唑替尼治疗IV期阳性非小细胞肺癌的预后因素:一项概念验证研究

CT-Based Radiomic Signature as a Prognostic Factor in Stage IV -Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study.

作者信息

Li Hailin, Zhang Rui, Wang Siwen, Fang Mengjie, Zhu Yongbei, Hu Zhenhua, Dong Di, Shi Jingyun, Tian Jie

机构信息

School of Automation, Harbin University of Science and Technology, Harbin, China.

CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

出版信息

Front Oncol. 2020 Feb 18;10:57. doi: 10.3389/fonc.2020.00057. eCollection 2020.

Abstract

To identify a computed tomography (CT)-based radiomic signature for predicting progression-free survival (PFS) in stage IV anaplastic lymphoma kinase ()-positive non-small-cell lung cancer (NSCLC) patients treated with tyrosine kinase inhibitor (TKI) crizotinib. This retrospective proof-of-concept study included a cohort of 63 stage IV -positive NSCLC patients who had received TKI crizotinib therapy for model construction and validation. Another independent cohort including 105 stage IV -positive NSCLC patients was also used for external validation in -TKI treatment. We initially extracted 481 quantitative three-dimensional features derived from manually segmented tumor volumes of interest. Pearson's correlation analysis along with the least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression was successively performed to select critical radiomic features. A CT-based radiomic signature for PFS prediction was obtained using multivariate Cox regression. The performance evaluation of the radiomic signature was conducted using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, and Kaplan-Meier survival analysis. A radiomic signature containing three features showed significant prognostic performance for -positive NSCLC patients in both the training cohort (C-index, 0.744; time-dependent AUC, 0.895) and the validation cohort (C-index, 0.717; time-dependent AUC, 0.824). The radiomic signature could significantly risk-stratify -positive NSCLC patients (hazard ratio, 2.181; < 0.001) and outperformed other prognostic factors. However, no significant association with PFS was captured for the radiomic signature in the -positive NSCLC cohort (log-rank tests, = 0.41). The CT-based radiomic features can capture valuable information regarding the tumor phenotype. The proposed radiomic signature was found to be an effective prognostic factor in stage IV mutated nonsynchronous nodules in NSCLC patients treated with a TKI.

摘要

为了识别一种基于计算机断层扫描(CT)的放射组学特征,用于预测接受酪氨酸激酶抑制剂(TKI)克唑替尼治疗的IV期间变性淋巴瘤激酶(ALK)阳性非小细胞肺癌(NSCLC)患者的无进展生存期(PFS)。这项回顾性概念验证研究纳入了一组63例接受TKI克唑替尼治疗的IV期ALK阳性NSCLC患者,用于模型构建和验证。另一个包含105例IV期ALK阳性NSCLC患者的独立队列也用于ALK-TKI治疗的外部验证。我们最初从手动分割的感兴趣肿瘤体积中提取了481个定量三维特征。相继进行Pearson相关性分析以及最小绝对收缩和选择算子(LASSO)惩罚的Cox比例风险回归,以选择关键的放射组学特征。使用多变量Cox回归获得用于PFS预测的基于CT的放射组学特征。使用一致性指数(C-index)、时间依赖性受试者工作特征(ROC)分析和Kaplan-Meier生存分析对放射组学特征进行性能评估。包含三个特征的放射组学特征在训练队列(C-index,0.744;时间依赖性AUC,0.895)和验证队列(C-index,0.717;时间依赖性AUC,0.824)中对ALK阳性NSCLC患者均显示出显著的预后性能。该放射组学特征可对ALK阳性NSCLC患者进行显著的风险分层(风险比,2.181;P<0.001),并且优于其他预后因素。然而,在ALK阳性NSCLC队列中,该放射组学特征与PFS未发现显著关联(对数秩检验,P=0.41)。基于CT的放射组学特征可以捕获有关肿瘤表型的有价值信息。发现所提出的放射组学特征是接受TKI治疗的NSCLC患者IV期ALK突变非同步结节的有效预后因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf9d/7040202/535e182873f3/fonc-10-00057-g0001.jpg

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