Spieler Benjamin, Giret Teresa M, Welford Scott, Totiger Tulasigeri M, Mihaylov Ivaylo B
Department of Radiation Oncology, Leonard M. Miller School of Medicine, University of Miami, 1475 NW 12th Ave., Suite 1500, Miami, FL 33136, USA.
Biomedicines. 2022 May 19;10(5):1173. doi: 10.3390/biomedicines10051173.
Combined radiotherapy (RT) and immune checkpoint-inhibitor (ICI) therapy can act synergistically to enhance tumor response beyond what either treatment can achieve alone. Alongside the revolutionary impact of ICIs on cancer therapy, life-threatening potential side effects, such as checkpoint-inhibitor-induced (CIP) pneumonitis, remain underreported and unpredictable. In this preclinical study, we hypothesized that routinely collected data such as imaging, blood counts, and blood cytokine levels can be utilized to build a model that predicts lung inflammation associated with combined RT/ICI therapy.
This proof-of-concept investigational work was performed on Lewis lung carcinoma in a syngeneic murine model. Nineteen mice were used, four as untreated controls and the rest subjected to RT/ICI therapy. Tumors were implanted subcutaneously in both flanks and upon reaching volumes of ~200 mm the animals were imaged with both CT and MRI and blood was collected. Quantitative radiomics features were extracted from imaging of both lungs. The animals then received RT to the right flank tumor only with a regimen of three 8 Gy fractions (one fraction per day over 3 days) with PD-1 inhibitor administration delivered intraperitoneally after each daily RT fraction. Tumor volume evolution was followed until tumors reached the maximum size allowed by the Institutional Animal Care and Use Committee (IACUC). The animals were sacrificed, and lung tissues harvested for immunohistochemistry evaluation. Tissue biomarkers of lung inflammation (CD45) were tallied, and binary logistic regression analyses were performed to create models predictive of lung inflammation, incorporating pretreatment CT/MRI radiomics, blood counts, and blood cytokines.
The treated animal cohort was dichotomized by the median value of CD45 infiltration in the lungs. Four pretreatment radiomics features (3 CT features and 1 MRI feature) together with pre-treatment neutrophil-to-lymphocyte (NLR) ratio and pre-treatment granulocyte-macrophage colony-stimulating factor (GM-CSF) level correlated with dichotomized CD45 infiltration. Predictive models were created by combining radiomics with NLR and GM-CSF. Receiver operating characteristic (ROC) analyses of two-fold internal cross-validation indicated that the predictive model incorporating MR radiomics had an average area under the curve (AUC) of 0.834, while the model incorporating CT radiomics had an AUC of 0.787.
Model building using quantitative imaging data, blood counts, and blood cytokines resulted in lung inflammation prediction models justifying the study hypothesis. The models yielded very-good-to-excellent AUCs of more than 0.78 on internal cross-validation analyses.
联合放疗（RT）与免疫检查点抑制剂（ICI）治疗可协同作用，增强肿瘤反应，其效果超过单一治疗所能达到的水平。除了ICI对癌症治疗产生的革命性影响外，诸如检查点抑制剂诱导的（CIP）肺炎等危及生命的潜在副作用，仍未得到充分报道且难以预测。在这项临床前研究中，我们假设常规收集的数据，如图像、血细胞计数和血液细胞因子水平，可用于构建一个预测与RT/ICI联合治疗相关的肺部炎症的模型。
这项概念验证性研究工作是在同基因小鼠模型中的Lewis肺癌上进行的。使用了19只小鼠，4只作为未治疗的对照，其余小鼠接受RT/ICI治疗。在双侧皮下植入肿瘤，当肿瘤体积达到约200立方毫米时，对动物进行CT和MRI成像，并采集血液。从双侧肺部图像中提取定量放射组学特征。然后仅对右侧腹部肿瘤进行RT，采用三个8 Gy分次的方案（在3天内每天1个分次），在每天的RT分次后腹腔注射PD-1抑制剂。跟踪肿瘤体积的变化，直到肿瘤达到机构动物护理和使用委员会（IACUC）允许的最大尺寸。处死动物，收获肺组织进行免疫组织化学评估。统计肺部炎症的组织生物标志物（CD45），并进行二元逻辑回归分析，以创建预测肺部炎症的模型，纳入治疗前的CT/MRI放射组学、血细胞计数和血液细胞因子。
根据肺部CD45浸润的中位数对治疗动物队列进行二分。四个治疗前放射组学特征（3个CT特征和1个MRI特征）以及治疗前中性粒细胞与淋巴细胞（NLR）比值和治疗前粒细胞-巨噬细胞集落刺激因子（GM-CSF）水平与二分的CD45浸润相关。通过将放射组学与NLR和GM-CSF相结合创建预测模型。两倍内部交叉验证的受试者操作特征（ROC）分析表明，纳入MR放射组学的预测模型的曲线下平均面积（AUC）为0.834，而纳入CT放射组学的模型的AUC为0.787。
使用定量成像数据、血细胞计数和血液细胞因子进行模型构建，得出了证实研究假设的肺部炎症预测模型。在内部交叉验证分析中，这些模型的AUC在0.78以上，表现非常好至优秀。
