A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden. (14th June 2021)

• Record Type: Journal Article
• Authors: Starosolski, Zbigniew; Courtney, Amy N.; Srivastava, Mayank; Guo, Linjie; Stupin, Igor; Metelitsa, Leonid S.; Annapragada, Ananth; Ghaghada, Ketan B.
• Other Names: Kostikov Alexey P. Academic Editor.
• Is Part Of: Contrast media & molecular imaging. Volume 2021(2021)
• Publication Date: 2021-06-14
• Subjects: Diagnostic imaging -- Periodicals; Magnetic resonance imaging -- Periodicals; Contrast media (Diagnostic imaging) -- Periodicals; Contrast Media -- Periodicals; Diagnostic Imaging -- Periodicals; Substances de contraste -- Périodiques; Diagnostics moléculaires -- Périodiques; Imagerie médicale; Substance de contraste; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.0754
• DOI: 10.1155/2021/6641384
• Languages: English
• ISSNs: 1555-4309

Abstract

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.