Kinetic Modeling and Radiomics Analysis of the Heterogeneity of Tumor-Associated Macrophages and Metabolism in Head and Neck Cancer

Purpose: Tumor-associated macrophages (TAMs) play an important role in the initiation and progression of head and neck squamous cell carcinoma (HNSCC). Herein, we evaluated the tumor tissue heterogeneity uptake of a CD163+ PET tracer, [64Cu]ICT-01, and compared it with [18F]FDG using kinetic modeling and radiomics analysis in a mouse oral carcinoma 1 (MOC1) HNSCC mouse model. Methods: MOC1 cells were implanted on the flanks of four C57BL/6 mice. At 3 weeks post tumor implantation, these mice underwent a 60 min dynamic scan post-injection of [18F]FDG and then another 60 min dynamic scan the next day post-injection of [64Cu]ICT-01. Nine tumor volumes were delineated using a region-growing algorithm, which were further divided into tissue volumes based on tracer uptake using k-means clustering. Kinetic modeling was performed using compartmental models and graphical Logan/Patlak. Standardized uptake value (SUV) PET images were generated using time windows of 1–10 min and 45- 60 min. A total of 91 textural radiomic features were obtained using Pyradiomics, and key features were selected using the coefficient of variance (CV). Results: k-means clustering identified three distinct tissue uptakes for [64Cu]ICT-01, and only two tissues with high and low uptakes for [18F]FDG. The 2TC4K and Logan models were found to be suitable models for modeling the kinetics of [64Cu]ICT-01. [18F]FDG generally yielded greater variances in quantitative parameters across tissues within the tumors than those of [64Cu]ICT-01, except for K1. However, greater variances in radiomic features were obtained for [64Cu]ICT-01 than for [18F]FDG. [64Cu]ICT-01 also yielded six more features with CV > 0.7 compared to [18F]FDG. Conclusion: [64Cu]ICT-01 showed greater differences in uptake kinetics and textural information, while [18F]FDG provided greater differences in quantitative values across the tissues within the tumor. [64Cu]ICT-01 and [18F]FDG showed the heterogeneous nature of the tumor microenvironment, and their combination would enable us to understand the tumor biology and hence improve tumor diagnosis.

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Journal
Chemical & Biomedical Imaging
Published
2026-05-30
DOI
https://doi.org/10.1021/cbmi.6c00065
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Kinetic Modeling and Radiomics Analysis of the Heterogeneity of Tumor-Associated Macrophages and Metabolism in Head and Neck Cancer

Gyu Seong Heo, Ying-Hwey Nai, Deborah Sultan, Richard Laforest et al.
Chemical & Biomedical Imaging
Radiomics and Machine Learning in Medical Imaging
article

Kinetic Modeling and Radiomics Analysis of the Heterogeneity of Tumor-Associated Macrophages and Metabolism in Head and Neck Cancer

Gyu Seong Heo, Ying-Hwey Nai, Deborah Sultan, Richard Laforest, Robert Gropler, Priscella Madrid, Yongjian Liu
article en

Abstract

Purpose: Tumor-associated macrophages (TAMs) play an important role in the initiation and progression of head and neck squamous cell carcinoma (HNSCC). Herein, we evaluated the tumor tissue heterogeneity uptake of a CD163+ PET tracer, [64Cu]ICT-01, and compared it with [18F]FDG using kinetic modeling and radiomics analysis in a mouse oral carcinoma 1 (MOC1) HNSCC mouse model. Methods: MOC1 cells were implanted on the flanks of four C57BL/6 mice. At 3 weeks post tumor implantation, these mice underwent a 60 min dynamic scan post-injection of [18F]FDG and then another 60 min dynamic scan the next day post-injection of [64Cu]ICT-01. Nine tumor volumes were delineated using a region-growing algorithm, which were further divided into tissue volumes based on tracer uptake using k-means clustering. Kinetic modeling was performed using compartmental models and graphical Logan/Patlak. Standardized uptake value (SUV) PET images were generated using time windows of 1–10 min and 45- 60 min. A total of 91 textural radiomic features were obtained using Pyradiomics, and key features were selected using the coefficient of variance (CV). Results: k-means clustering identified three distinct tissue uptakes for [64Cu]ICT-01, and only two tissues with high and low uptakes for [18F]FDG. The 2TC4K and Logan models were found to be suitable models for modeling the kinetics of [64Cu]ICT-01. [18F]FDG generally yielded greater variances in quantitative parameters across tissues within the tumors than those of [64Cu]ICT-01, except for K1. However, greater variances in radiomic features were obtained for [64Cu]ICT-01 than for [18F]FDG. [64Cu]ICT-01 also yielded six more features with CV > 0.7 compared to [18F]FDG. Conclusion: [64Cu]ICT-01 showed greater differences in uptake kinetics and textural information, while [18F]FDG provided greater differences in quantitative values across the tissues within the tumor. [64Cu]ICT-01 and [18F]FDG showed the heterogeneous nature of the tumor microenvironment, and their combination would enable us to understand the tumor biology and hence improve tumor diagnosis.

Chemical & Biomedical Imaging
Mallinckrodt (Ireland) (IE)
National Institutes of Health
Good health and well-being
Openalex Percentile: Top 5%
Radiomics and Machine Learning in Medical Imaging
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