Lung Inflammation Predictors in Combined Immune Checkpoint-Inhibitor and Radiation Therapy—Proof-of-Concept Animal Study

Abstract

Purpose: 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. Materials and Methods: 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 mm3 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. Results: 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. Conclusions: 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.

Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside

“…The application of radiomics to tumor immune microenvironment (TIME) dynamics is a further development of the habitat concept. Our prior preclinical work demonstrated that radiomics of pretreatment MR and CT imaging can predict lymphoid and myeloid infiltration of tissue in regions of interest ( 34 ). In that study and the current one, the texture feature “ CT average gray ” emerged as predictive of treatment effect.…”

“…Radiomic analysis assumes that patterns (known as “ features ” ) below the threshold of visual detection are present within medical imaging and reflect underlying pathophysiology; converts medical imaging into mineable data and extracts clinically relevant features to improve cancer diagnosis, prognosis, prediction, and assessment of treatment response; and has augmented predictive models in diverse cancer types ( 32 , 33 ) using conventional imaging studies such as computed tomography (CT) and magnetic resonance imaging (MRI). Advanced radiomics can quantify and analyze subregions within tumors which reflect differences in underlying tumor pathophysiology ( 31 ), such as lymphoid infiltration within highlighted areas ( 34 ). CT radiomics are of particular interest as these studies are commonly obtained for patients with aNSCLC, and CT texture features from pretreatment imaging have been incorporated in biomarker studies predicting for response to ICI ( 35 , 36 ).…”

Effect of argon plasma abutment activation on soft tissue healing: RCT with histological assessment

“…The regions containing inflammatory cells were outlined to obtain the value of the area occupied in terms of pixels, and this value was then normalized to the total area of the examined sample, also expressed in pixels (Figure 2C). This procedure allowed obtaining a percentage value that could be compared across different samples, as it is relative rather than absolute and independent of the size of the sample itself 39 . Collagen stained by picrosirius red staining, was quantified through color deconvolution.…”

“…This procedure allowed obtaining a percentage value that could be compared across different samples, as it is relative rather than absolute and independent of the size of the sample itself. 39 Collagen stained by picrosirius red staining, was quantified through color deconvolution. Collagen quantification was expressed as a proportion of collagen-occupied surface and the total sample surface.…”

Advances in CT features and radiomics of checkpoint inhibitor-related pneumonitis: A short review

“…Mice were bilaterally imaged with CT and MRI after subcutaneous tumor formation and blood collection, and then treated with RT of the right abdominal tumor only (3*8Gy) followed by intraperitoneal injection of PD-1 inhibitors. They found that 3 CT radiomic features (mean grayscale, histogram kurtosis and co-occurrence matrix entropy) and 1 MRI feature (histogram kurtosis) together with baseline neutrophil-to-lymphocyte ratio (NLR) and granulocyte-macrophage colony-stimulating factor (GMSF) levels were positively correlated with CD45 infiltration ( 22 ). However, it is important to note that this model only assessed the CD45 infiltration levels to indicate the occurrence of pneumonia is not sufficiently reasonable and could not distinguish between RIP and CIP.…”