Purpose: Immunotherapy has made breakthroughs in the treatment of non-small-cell lung cancer (NSCLC); however, only a subset of patients achieved long-term survival, so it is of great importance to find a biomarker of lung cancer thus guide immunotherapy. Studies have shown that the infiltration level of tissue resident memory CD8⁺ T cells (CD8⁺ TRMs) is positively correlated with lung cancer prognosis and can be an ideal biomarker for assessing the tumor local immune status. We screened the radiomic features associated with CD8⁺ TRMs as targets in NSCLC surgical specimens by radiomic approaches, and established a radiomic predictive model to assess the local immune status, which may provide a scientific reference for lung cancer treatment strategies.
Patients and Methods: We retrospectively analyzed the NSCLC surgical specimens immune cell database and extracted CD8⁺ TRMs cell data, preoperative CT scan data were achieved. A total of 97 patients containing complete preoperative data were included, radiomic features were extracted from the preoperative CT image data. All the patients were divided into two groups, namely high-CD8⁺ TRMs infiltrated group and low-CD8⁺ TRMs infiltrated group, based on the proportion of CD8⁺ TRMs cells subset in the immune cell population. The most valuable radiomic features and semantic features were extracted and selected, and a neural network model was established to predict the level of CD8⁺ TRMs cell infiltration level to assess the tumor local immune status.
Results: The NSCLC tumor immune status predictive model was built to discriminate high- from low-CD8⁺ TRMs with an area under the curve (AUC) of 0.788 (95% CI) in the training set and 0.753 (95% CI) in the validation set.
Conclusion: The radiomic models using CT image data showed a good predictive performance for accessing NSCLC immune status thus has great potential for personalized therapeutic decision making.

Keywords: non-small-cell lung cancer, NSCLC, radiomic, tumor immune status, tissue resident memory CD8⁺ T cells, CD8⁺ TRMs


中文翻译：

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通过 CD8+TRMs 在手术切除的非小细胞肺癌中获取肿瘤免疫状态的放射学方法

目的：免疫疗法在非小细胞肺癌（NSCLC）的治疗上取得突破；然而，只有一部分患者获得了长期生存，因此寻找肺癌的生物标志物从而指导免疫治疗具有重要意义。研究表明，组织驻留记忆CD8 ⁺ T细胞（CD8 ⁺ TRMs）的浸润水平与肺癌预后呈正相关，可作为评估肿瘤局部免疫状态的理想生物标志物。我们筛选了与 CD8 ^{+相关的放射组学特征}通过放射组学方法将TRMs作为NSCLC手术标本的靶点，建立放射组学预测模型来评估局部免疫状态，可为肺癌治疗策略提供科学参考。
患者与方法：回顾性分析NSCLC手术标本免疫细胞数据库，提取CD8 ⁺ TRMs细胞数据，获得术前CT扫描数据。共纳入包含完整术前数据的 97 例患者，从术前 CT 图像数据中提取放射组学特征。^{根据CD8 +} TRMs的比例将所有患者分为两组，即高CD8 ⁺ TRMs浸润组和低CD8 ⁺ TRMs浸润组。免疫细胞群中的 TRM 细胞亚群。提取并选择最有价值的放射组学特征和语义特征，建立神经网络模型预测CD8 ⁺ TRMs细胞浸润水平，评估肿瘤局部免疫状态。
结果：建立 NSCLC 肿瘤免疫状态预测模型以区分高和低 CD8 ⁺ TRM，训练集的曲线下面积 (AUC) 为 0.788 (95% CI)，而训练集的曲线下面积 (AUC) 为 0.753 (95% CI)验证集。
结论：使用 CT 图像数据的放射组学模型在获取 NSCLC 免疫状态方面显示出良好的预测性能，因此在个性化治疗决策制定方面具有巨大潜力。

关键词：非小细胞肺癌、非小细胞肺癌、放射组学、肿瘤免疫状态、组织驻留记忆 CD8 ⁺ T 细胞、CD8 ⁺ TRM