A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET

Mahbubunnabi Tamal

doi:10.3390/app11020535

Abstract

Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.

Highlights

Positron Emission Tomography (PET) radiotracer uptake in tumor is often heterogeneous due to different biological characteristics of tumor cells
This study aimed to investigate the relationships of textural features with these parameters using phantom data
Energy and entropy have inverse and direct relationships with quantization levels with both of them remaining unchanged for volumes less than 5.58 cm3 and quantization level 32 and higher

Summary

Introduction

PET radiotracer uptake in tumor is often heterogeneous due to different biological characteristics of tumor cells (e.g., cell proliferation, cell death, differential metabolic activity, vascular structure, etc.). Heterogeneity defines the aggressiveness and therapeutic resistance of the tumor and makes the effective treatment strategies challenging [1]. For of this reason, accurate quantification of intra-tumor heterogeneity has the potential to be used as a person specific tumor staging and prognostic biomarker [2,3,4,5]. Artificial intelligence (AI)-assisted accurate classification of these tumor radiomic features can make tumor staging and prognostic biomarker more robust [7,8]. It is important to note that heterogeneity is dependent on image acquisition time point and can impact the radiomics features [9]

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