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Relating Macroscopic PET Radiomics Features to Microscopic Tumor Phenotypes Using a Stochastic Mathematical Model of
Hailey S H Ahn1, Yas Oloumi Yazdi1,2, Brennan J Wadsworth2
1Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada.
This study introduces a novel computational model to simulate tumor growth and PET images, linking microscopic tumor traits to imaging phenotypes. Radiomics features like "cluster shade" effectively distinguish subtle differences, even in small tumors.
Area of Science:
- * Computational biology and medical imaging.
- * Development of advanced mathematical models for tumor microenvironment simulation.
Background:
- * Cancer phenotypes vary due to genetic and microenvironmental factors, necessitating in vivo classification.
- * Positron emission tomography (PET) aids in tumor metabolic profiling but faces limitations in resolution, noise, and data availability.
- * Existing digital PET phantoms lack biological links and realistic complexity.
Purpose of the Study:
- * To develop a novel computational method for investigating the relationship between microscopic tumor parameters and PET image characteristics.
- * To simulate tumor growth and generate realistic PET images from a multiscale mathematical model.
- * To identify radiomics features capable of discriminating distinct tumor phenotypes under varying noise conditions.
Main Methods:
- * Utilized a hybrid, multiscale, stochastic mathematical model for simulating cellular metabolism and proliferation.
- * Generated longitudinal tumor growth sequences and converted them into PET images with realistic resolution and noise.
- * Simulated six distinct tumor phenotypes by altering biological parameters like blood vessel density and necrosis conditions.
- * Computed 22 standardized Haralick texture features and assessed their noise resilience.
Main Results:
- * Simulated phenotypes, including those with varying hypoxic and necrotic regions, were generated and compared to real histology.
- * Identified "cluster shade" and "difference entropy" as the most effective and noise-resilient radiomics features for phenotype discrimination.
- * Demonstrated the utility of radiomics analysis for small lesions (8.7-10.0 mm) on modern PET scanners.
- * Observed non-monotonic changes in certain radiomics features during tumor growth.
Conclusions:
- * The proposed computational simulation method effectively links microscopic tumor parameters to PET imaging phenotypes.
- * Radiomics analysis, particularly using specific texture features, can differentiate subtle tumor phenotypes and is valuable even for small lesions.
- * Findings have implications for selecting radiomics features for tracking cancer progression and therapy response.
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