Abstract
Objective Tumor-associated macrophages (TAMs) within the tumor immune microenvironment (TiME) of solid tumors play an important role in treatment resistance and disease recurrence. The purpose of this study was to investigate if nanoradiomics (radiomic analysis of nanoparticle contrast-enhanced images) can differentiate tumors based on TAM burden. Materials and Methods In vivo studies were performed in transgenic mouse models of neuroblastoma with low (N = 11) and high (N = 10) tumor-associated macrophage (TAM) burden. Animals underwent delayed nanoparticle contrast-enhanced CT (n-CECT) imaging at 4 days after intravenous administration of liposomal-iodine agent (1.1 g/kg). CT imaging-derived conventional tumor metrics (tumor volume and CT attenuation) were computed for segmented tumor CT datasets. Nanoradiomic analysis was performed using a PyRadiomics workflow implemented in the quantitative image feature pipeline (QIFP) server containing 900 radiomic features (RFs). RF selection was performed under supervised machine learning using a nonparametric neighborhood component method. A 5-fold validation was performed using a set of linear and nonlinear classifiers for group separation. Statistical analysis was performed using the Kruskal–Wallis test. Results N-CECT imaging demonstrated heterogeneous patterns of signal enhancement in low and high TAM tumors. CT imaging-derived conventional tumor metrics showed no significant differences (p > 0.05) in tumor volume between low and high TAM tumors. Tumor CT attenuation was not significantly different (p > 0.05) between low and high TAM tumors. Machine learning-augmented nanoradiomic analysis revealed two RFs that differentiated (p < 0.002) low TAM and high TAM tumors. The RFs were used to build a linear classifier that demonstrated very high accuracy and further confirmed by 5-fold cross-validation. Conclusions Imaging-derived conventional tumor metrics were unable to differentiate tumors with varying TAM burden; however, nanoradiomic analysis revealed texture differences and enabled differentiation of low and high TAM tumors.
Highlights
Radiomic analysis was performed using a PyRadiomics workflow implemented in the quantitative image feature pipeline (QIFP) server containing a total of 900 radiomic features (RFs) to analyze shape, size, intensity, morphology, and texture [32]. ese radiomic features are divided into three categories: (1) original radiomics features (n 86) which contain first-order statistics, shape and size descriptors, and texture classes which include gray-level cooccurrence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), gray level dependence matrix (GLDM), and neighboring gray-tone difference matrix (NGTDM); (2) logarithmic enhancement of original radiomics features with three degrees of Sigma values (n 222); and (3) wavelet representations of original radiomic features (n 592)
Tumor CT attenuation did not differ significantly (p > 0.05) between low Tumor-associated macrophages (TAMs) (54 ± 9 Hounsfield unit (HU)) and high TAM (47 ± 7 HU) tumors (Figure 5(b)). These findings suggest that CT-derived conventional tumors metrics are unable to differentiate tumors based on their TAM burden
A 5-fold validation of RFs was performed using a machine learning approach to confirm the high accuracy of the linear classifier (RF signature) that differentiated tumors based on TAM burden. e identified RFs belonged to two categories: original gray level size zone matrix (GLSZM) and second category wavelet belonging to the wavelet LHL subcategory (Figure 7)
Summary
Growing evidence suggests that TAMs carry out critical roles in essentially every stage of disease progression including tumor growth, angiogenesis, metastasis, and treatment resistance to conventional and emerging targeted therapies [7, 8]. Techniques for monitoring TAM burden in solid tumors could aid in disease prognosis, a priori identification of treatment resistance, and monitoring of tumor response to TAM-directed immunotherapies. Conventional imaging methods and standard imaging quantitative metrics based on ultrasound, contrast-enhanced CT, and MRI are insensitive to TAM burden since macrophages represent a relatively small fraction (
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