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基于CT图像的放射组学特征预测非小细胞肺癌中CD3 T细胞和CD8 T细胞的表达水平。

Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images.

作者信息

Chen Lujiao, Chen Lulin, Ni Hongxia, Shen Liyijing, Wei Jianguo, Xia Yang, Yang Jianfeng, Yang Minxia, Zhao Zhenhua

机构信息

Department of Radiology, Shaoxing People's Hospital, Shaoxing, Zhejiang, China.

Department of Ultrasound, Affiliated hospital of Shaoxing University, Shaoxing, Zhejiang, China.

出版信息

Front Oncol. 2023 Feb 13;13:1104316. doi: 10.3389/fonc.2023.1104316. eCollection 2023.

Abstract

BACKGROUND

In this work, radiomics characteristics based on CT scans were used to build a model for preoperative evaluation of CD3 and CD8 T cells expression levels in patients with non-small cell lung cancer (NSCLC).

METHODS

Two radiomics models for evaluating tumor-infiltrating CD3 and CD8 T cells were created and validated using computed tomography (CT) images and pathology information from NSCLC patients. From January 2020 to December 2021, 105 NSCLC patients with surgical and histological confirmation underwent this retrospective analysis. Immunohistochemistry (IHC) was used to determine CD3 and CD8 T cells expression, and all patients were classified into groups with high and low CD3 T cells expression and high and low CD8 T cells expression. The CT area of interest had 1316 radiomic characteristics that were retrieved. The minimal absolute shrinkage and selection operator (Lasso) technique was used to choose components from the IHC data, and two radiomics models based on CD3 and CD8 T cells abundance were created. Receiver operating characteristic (ROC), calibration curve, and decision curve analyses were used to examine the models' ability to discriminate and their clinical relevance (DCA).

RESULTS

A CD3 T cells radiomics model with 10 radiological characteristics and a CD8 T cells radiomics model with 6 radiological features that we created both demonstrated strong discrimination in the training and validation cohorts. The CD3 radiomics model has an area under the curve (AUC) of 0.943 (95% CI 0.886-1), sensitivities, specificities, and accuracy of 96%, 89%, and 93%, respectively, in the validation cohort. The AUC of the CD8 radiomics model was 0.837 (95% CI 0.745-0.930) in the validation cohort, with sensitivity, specificity, and accuracy values of 70%, 93%, and 80%, respectively. Patients with high levels of CD3 and CD8 expression had better radiographic results than patients with low levels of expression in both cohorts (p<0.05). Both radiomic models were therapeutically useful, as demonstrated by DCA.

CONCLUSIONS

When making judgments on therapeutic immunotherapy, CT-based radiomic models can be utilized as a non-invasive way to evaluate the expression of tumor-infiltrating CD3 and CD8 T cells in NSCLC patients.

摘要

背景

在本研究中,基于CT扫描的放射组学特征被用于建立一个模型,以术前评估非小细胞肺癌(NSCLC)患者的CD3和CD8 T细胞表达水平。

方法

利用NSCLC患者的计算机断层扫描(CT)图像和病理信息,创建并验证了两个用于评估肿瘤浸润性CD3和CD8 T细胞的放射组学模型。2020年1月至2021年12月,对105例经手术和组织学确诊的NSCLC患者进行了这项回顾性分析。采用免疫组织化学(IHC)法测定CD3和CD8 T细胞表达,并将所有患者分为CD3 T细胞高表达组和低表达组以及CD8 T细胞高表达组和低表达组。从CT感兴趣区域提取了1316个放射组学特征。采用最小绝对收缩和选择算子(Lasso)技术从IHC数据中选择成分,创建了基于CD3和CD8 T细胞丰度的两个放射组学模型。采用受试者操作特征(ROC)曲线、校准曲线和决策曲线分析来检验模型的鉴别能力及其临床相关性(DCA)。

结果

我们创建的具有10个放射学特征的CD3 T细胞放射组学模型和具有6个放射学特征的CD8 T细胞放射组学模型在训练和验证队列中均表现出很强的鉴别能力。在验证队列中,CD3放射组学模型的曲线下面积(AUC)为0.943(95%CI 0.886-1),敏感性、特异性和准确性分别为96%、89%和93%。在验证队列中,CD8放射组学模型的AUC为0.837(95%CI 0.745-0.930),敏感性、特异性和准确性分别为70%、93%和80%。在两个队列中,CD3和CD8表达水平高的患者的影像学结果均优于表达水平低的患者(p<0.05)。DCA分析表明,这两个放射组学模型在治疗上均有用。

结论

在对免疫治疗进行判断时,基于CT的放射组学模型可作为一种非侵入性方法,用于评估NSCLC患者肿瘤浸润性CD3和CD8 T细胞的表达。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee93/9968855/04c1953fa490/fonc-13-1104316-g001.jpg

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