Prediction of distant metastasis in non-small cell lung cancer based on CT radiomics combined prediction model

WANG Xianqi; LUO Haoran; LI Ke; YANG Jing; JING Yang; CHEN Wei

doi:10.19732/j.cnki.2096-6210.2022.04.003

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Prediction of distant metastasis in non-small cell lung cancer based on CT radiomics combined prediction model

更新时间：2025-12-15

- Prediction of distant metastasis in non-small cell lung cancer based on CT radiomics combined prediction model
- Vol. 31, Issue 4, Pages: 357-366(2022)
- 作者机构：
  
  1. 陆军军医大学第一附属医院放射科,重庆,400030
  2. 武警四川省总队医院医学影像科,四川,乐山,614000
  3. 慧影医疗科技（北京）股份有限公司,北京,100089
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2022.04.003
  CLC：
- Published Online：28 August 2022，
  
  Published：28 August 2022
- 稿件说明：
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王显棋,罗浩然,李可,等. 基于CT影像组学联合预测模型对非小细胞肺癌远处转移的预测[J]. 肿瘤影像学, 2022, 31(4): 357-366 https://doi.

org/10.19732/j.cnki.2096-6210.2022.04.003
王显棋,罗浩然,李可,等. 基于CT影像组学联合预测模型对非小细胞肺癌远处转移的预测[J]. 肿瘤影像学, 2022, 31(4): 357-366 https://doi. DOI： 10.19732/j.cnki.2096-6210.2022.04.003.

org/10.19732/j.cnki.2096-6210.2022.04.003 DOI：

摘要

目的：

探讨基于计算机体层成像（computed tomography，CT）影像组学联合临床特征预测模型对非小细胞肺癌（non-small cell lung cancer，NSCLC）远处转移的预测价值。

方法：

回顾并分析140例NSCLC患者（74例未发生远处转移和66例发生远处转移），从每例患者的治疗前CT图像上勾画2个感兴趣区（region of interest，ROI），包括瘤周微浸润区域（记为ME）和原发肿瘤区域（记为tumor），再分别提取影像组学特征，计算影像组学评分（radiomics score，RS）。通过计算组内相关系数（intraclass correlation coefficient，ICC）来检验特征勾画的一致性。对所有数据进行分组，训练组和验证组分别有97例和43例。采用

检验或Fisher精确概率检验评价转移组和未转移组特征差异有无统计学意义。构建4个NSCLC远处转移预测模型，分别为肿瘤（tumor）影像组学模型、含瘤周（tumor+ME）影像组学模型及其分别联合临床特征的综合模型。所有模型性能通过受试者工作特征（receiver operating characteristic，ROC）曲线的曲线下面积（area under curve，AUC）量化，使用DeLong检验对不同模型的诊断能力进行显著性检验，同时构建含瘤周（tumor+ME）影像组学联合临床特征综合模型的诺模图，并评价诺模图的校准和鉴别能力。

结果：

在单独的肿瘤（tumor）影像组学模型中筛选出9个组学特征，在含瘤周（tumor+ME）影像组学模型中筛选出13个组学特征。临床因素癌胚抗原（carcinoembryonic antigen，CEA）对于预测NSCLC远处转移差异有统计学意义（

＜0.05）。肿瘤（tumor）、含瘤周（tumor+ME）影像组学模型的AUC分别为0.779、0.854，综合模型中肿瘤组、含瘤周组的AUC分别为0.795、0.858。

结论：

影像组学特征联合临床因素所构建的模型可用于NSCLC远处转移的预测，含瘤周（tumor+ME）影像组学模型可以提高NSCLC远处转移的预测能力。

Abstract

Objective:

To explore the predict

ive value of computed tomography (CT) radiomics combined with clinical prediction model for distant metastasis of non-small cell lung cancer (NSCLC).

Methods:

Retrospective analysis of 140 lung cancer patients (74 without distant metastasis and 66 with distant metastasis)

two regions of interest (ROI)

including the peritumoral region and the tumor parenchyma region

were delineated from the pre-treatment CT images of each patient. The radiomics features were extracted from the two ROI

respectively

and the radiomics score (RS) was calculated. Consistency of feature delineation was checked by calculating the intraclass correlation coefficient (ICC). All data were grouped

with training and validation cohorts of 97 and 43

respectively. Statistical differences in the characteristics of the metastatic and non-metastatic groups were evaluated using the chi- square test or Fishers exact test. Four prediction models were constructed

namely tumor radiomics model

including peritumoral radiomics model and two comprehensive models combined with clinical features respectively. All model performance was quantified by the area under curve (AUC) of receiver operating characteristic (ROC) curve

the diagnostic ability of different models was tested by DeLong test

and a nomogram was constructed containing a comprehensive model of peritumoral radiomics combined with clinical features

and the calibration and discriminative ability of the nomogram was evaluated.

Results:

Nine radiomics signatures were screened in the tumor radiomics model alone

and thirteen radiomics signatures were screened in the including peritumoral radiomics model. The clinical factor carcinoembryonic antigen (CEA) had statistical significance in the prediction of distant metastasis of NSCLC (

＜0.05). The AUC values of the radiomics model alone were 0.779 and 0.854. Combining clinical factor

the combined models were 0.795 and 0.858

respectively.

Conclusion:

The model constructed by imaging c

haracteristics combined with clinical factor can be used to predict the DM of NSCLC

and the including peritumoral model can improve the ability to predict distant metastasis of NSCLC.

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