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

The aim of our study was to create a novel Gaussian Mixture Modeling (GMM) pipeline in order to model the complementary information derived from 2-deoxy-2-(¹⁸F)-fluoro-D-glucose (¹⁸F-FDG)-Positron Emission Tomography (PET) and Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) measurements to separate the tumor microenvironment into relevant tissue compartments and follow the development of these compartments in a longitudinal manner. Methods: Serial ¹⁸F-FDG-PET and Apparent Diffusion Coefficient (ADC) maps derived from DW-MR images of NCI-H460 xenograft tumors were co-registered and a population based GMM was implemented on the complementary imaging data. The tumor microenvironment was segmented into three distinct regions and correlated with histological stainings. An analysis of covariance (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 co-registered 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 the 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 utilizing the novel GMM pipeline. Leveraging the power of multi-parametric PET/MR imaging in this manner has the potential to take the assessment of disease outcome beyond the Response Evaluation Criteria in Solid Tumors and could provide an important impact to the field of precision medicine.

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