Application of radiomics based on chest CT-enhanced dual-phase imaging in the immunotherapy of non-small cell lung cancer.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-01-01 DOI:10.3233/XST-230189

Ze-Peng Ma, Xiao-Lei Li, Kai Gao, Tian-Le Zhang, Heng-Di Wang, Yong-Xia Zhao

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引用次数: 0

Abstract

Objective: To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC).

Methods: 106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness.

Results: LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05).

Conclusion: The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.

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基于胸部CT增强双相成像的放射组学在癌症免疫治疗中的应用。

目的：探讨基于不同CT增强期的计算机断层扫描（CT）放射组学在判断癌症（NSCLC）免疫治疗效果中的价值。方法：将106例接受免疫治疗的NSCLC患者随机分为训练组（74例）和验证组（32例）。收集患者治疗前的CT增强动脉和静脉期图像。在CT增强图像上分割感兴趣区域（ROI），并提取放射学特征。单向方差分析和最小绝对收缩选择算子（LASSO）用于筛选最佳放射组学特征，并分析放射组学特性与免疫治疗疗效之间的关系。计算受试者操作特征曲线下面积（AUC）以及灵敏度和特异性，以评估诊断有效性。结果：LASSO回归分析分别在动脉期和静脉期图像中筛选出6个和8个最佳特征。应用于训练组，基于CT增强动脉和静脉期图像的AUCs分别为0.867（95%CI:0.82-0.94）和0.880（95%CI:0.86-0.91），敏感性分别为73.91%和76.19%，特异性分别为66.67%和72.19%，而在验证组，动脉期和静脉期图像的AUC分别为0.732（95%可信区间：0.71-0.78）和0.832（95%置信区间：0.78-0.91），敏感性分别为75.00%和76.00%，特异性分别为73.07%和75.00%。在训练组和验证组中，根据动脉期和静脉期图像计算的AUC值之间没有显著差异（P < 结论：从CT增强的不同相位图像中计算出的最佳放射组学特征可以为评估靶向治疗NSCLC的疗效提供新的成像标记，具有较高的诊断价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.