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宫颈癌放化疗期间肿瘤浸润 CD8+ T 细胞和巨噬细胞动态变化的影像组学特征

英文原题:Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy.

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Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy.

PubMed 2024/04/23(内容时间) Cancer Imaging Q1 · IF 4.8(JCR 2025)

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研究概要

影像组学特征可作为 CCRT 诱导的 CD8+ T 细胞和巨噬细胞动态变化的潜在预测因子,与组织活检相比,这可能为评估宫颈癌 CCRT 期间肿瘤免疫状态提供一种侵入性较小的途径。

研究思路结论见上方概要

我们先前的研究表明,在宫颈癌患者同步放化疗(CCRT)期间,肿瘤CD8+ T细胞和巨噬细胞(定义为CD68+细胞)浸润发生了动态且异质性的变化,这些变化与其短期肿瘤反应相关。本研究旨在开发一种基于CT图像的放射组学特征,用于评估这种动态变化。

本研究纳入了30例接受CCRT后行近距离放疗的宫颈鳞状细胞癌患者。获取治疗前CT图像。并在基线(0分次(F))和10F后立即对原发部位进行肿瘤活检及免疫组化检测。使用Matlab从CT图像的感兴趣区(ROI)中提取放射组学特征。采用十折交叉验证的LASSO回归模型筛选特征并构建免疫标志物分类器和放射组学特征。通过曲线下面积(AUC)评估其性能。

与基线相比,10F放疗后肿瘤浸润CD8+T细胞和巨噬细胞的变化被用于生成免疫标志物分类器(AUC=0.842,95% CI:0.680-1.000)。此外,利用4个关键放射组学特征开发了一个放射组学标签,用于预测免疫标志物分类器(AUC=0.875,95% CI:0.753-0.997)。根据该标签进行分层的患者在治疗反应方面表现出显著差异(p = 0.004)。

展开英文摘要原文

Our previous study suggests that tumor CD8+ T cells and macrophages (defined as CD68+ cells) infiltration underwent dynamic and heterogeneous changes during concurrent chemoradiotherapy (CCRT) in cervical cancer patients, which correlated with their short-term tumor response. This study aims to develop a CT image-based radiomics signature for such dynamic changes.

Thirty cervical squamous cell carcinoma patients, who were treated with CCRT followed by brachytherapy, were included in this study. Pre-therapeutic CT images were acquired. And tumor biopsies with immunohistochemistry at primary sites were performed at baseline (0 fraction (F)) and immediately after 10F. Radiomics features were extracted from the region of interest (ROI) of CT images using Matlab. The LASSO regression model with ten-fold cross-validation was utilized to select features and construct an immunomarker classifier and a radiomics signature. Their performance was evaluated by the area under the curve (AUC).

The changes of tumor-infiltrating CD8+T cells and macrophages after 10F radiotherapy as compared to those at baseline were used to generate the immunomarker classifier (AUC= 0.842, 95% CI:0.680-1.000). Additionally, a radiomics signature was developed using 4 key radiomics features to predict the immunomarker classifier (AUC=0.875, 95% CI:0.753-0.997). The patients stratified based on this signature exhibited significant differences in treatment response (p = 0.004).

The radiomics signature could be used as a potential predictor for the CCRT-induced dynamic alterations of CD8+ T cells and macrophages, which may provide a less invasive approach to appraise tumor immune status during CCRT in cervical cancer compared to tissue biopsy.

论文信息

作者
Huang K、Huang X、Zeng C、Wang S、Zhan Y、Cai Q、Peng G、Yang Z
第一作者单位
Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China.China
通讯作者单位
Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China. czchen2@stu.edu.cn.China
期刊
Cancer imaging : the official publication of the International Cancer Imaging Society2024 Apr 23
原文标识
PubMed 38654284 · DOI 10.1186/s40644-024-00680-0