A Population-Based Gaussian Mixture Model Incorporating ¹⁸F-FDG PET and Diffusion-Weighted MRI Quantifies Tumor Tissue Classes

Abstract

The aim of our study was to create a novel Gaussian mixture modeling (GMM) pipeline to model the complementary information derived from¹⁸F-FDG PET and diffusion-weighted MRI (DW-MRI) to separate the tumor microenvironment into relevant tissue compartments and follow the development of these compartments longitudinally. Methods: Serial ¹⁸F-FDG PET and apparent diffusion coefficient (ADC) maps derived from DW-MR images of NCI-H460 xenograft tumors were coregistered, and a population-based GMM was implemented on the complementary imaging data. The tumor microenvironment was segmented into 3 distinct regions and correlated with histology. ANCOVA was applied to gauge how well the total tumor volume was a predictor for the ADC and ¹⁸F-FDG, or if ADC was a good predictor of ¹⁸F-FDG for average values in the whole tumor or average necrotic and viable tissues. Results: The coregistered PET/MR images were in excellent agreement with histology, both visually and quantitatively, and allowed for validation of the last-time-point measurements. Strong correlations were found for the necrotic (r = 0.88) and viable fractions (r = 0.87) between histology and clustering. The GMM provided probabilities for each compartment with uncertainties expressed as a mixture of tissues in which the resolution of scans was inadequate to accurately separate tissues. The ANCOVA suggested that both ADC and ¹⁸F-FDG in the whole tumor (P = 0.0009, P = 0.02) as well as necrotic (P = 0.008, P = 0.02) and viable (P = 0.003, P = 0.01) tissues were a positive, linear function of total tumor volume. ADC proved to be a positive predictor of ¹⁸F-FDG in the whole tumor (P = 0.001) and necrotic (P = 0.02) and viable (P = 0.0001) tissues. Conclusion: The complementary information of ¹⁸F-FDG and ADC longitudinal measurements in xenograft tumors allows for segmentation into distinct tissues when using the novel GMM pipeline. Leveraging the power of multiparametric PET/MRI in this manner has the potential to take the assessment of disease outcome beyond RECIST and could provide an important impact to the field of precision medicine.