Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study

Abstract

Studying early response to cancer treatment is significant for patient treatment stratification and follow-up. Although recent advances in positron emission tomography (PET) and magnetic resonance imaging (MRI) allow for evaluation of tumor response, a quantitative objective assessment of treatment-related effects offers localization and quantification of structural and functional changes in the tumor region. Radiomics, the process of computerized extraction of features from radiographic images, is a new strategy for capturing subtle changes in the tumor region that works by quantifying subvisual patterns which might escape human identification. The goal of this study was to demonstrate feasibility for performing radiomics analysis on integrated PET/MRI to characterize early treatment response in metastatic renal cell carcinoma (RCC) undergoing sunitinib therapy. Two patients with advanced RCC were imaged using an integrated PET/MRI scanner. [18 F] fluorothymidine (FLT) was used as the PET radiotracer, which can measure the degree of cell proliferation. Image acquisitions included test/retest scans before sunitinib treatment and one scan 3 weeks into treatment using [18 F] FLT-PET, T2-weighted (T2w), and diffusion-weighted imaging (DWI) protocols, where DWI yielded an apparent diffusion coefficient (ADC) map. Our framework to quantitatively characterize treatment-related changes involved the following analytic steps: 1) intraacquisition and interacquisition registration of protocols to allow voxel-wise comparison of changes in radiomic features, 2) correction and pseudoquantification of T2w images to remove acquisition artifacts and examine tissue-specific response, 3) characterization of information captured by T2w MRI, FLT-PET, and ADC via radiomics, and 4) combining multiparametric information to create a map of integrated changes from PET/MRI radiomic features. Standardized uptake value (from FLT-PET) and ADC textures ranked highest for reproducibility in a test/retest evaluation as well as for capturing treatment response, in comparison to high variability seen in T2w MRI. The highest-ranked radiomic feature yielded a normalized percentage change of 63% within the RCC region and 17% in a spatially distinct normal region relative to its pretreatment value. By comparison, both the original and postprocessed T2w signal intensity appeared to be markedly less sensitive and specific to changes within the tumor. Our preliminary results thus suggest that radiomics analysis could be a powerful tool for characterizing treatment response in integrated PET/MRI.

Figure 1

Overview of the methodology and overall workflow.

Figure 2

Transformation of RCC volumes into a voxel-wise correspondence across acquisitions. (Top row) Annotations of RCC region on original T2w volumes. Note slight differences in annotated region between each column. (Middle row) Results of 3D rigid plus deformable coregistration shown as checkerboard images. Note contiguity of structures across checkerboard, indicating accuracy of registration. (Bottom row) Creation of minimum overlapping volume mask from intersection of transformed annotations of coregistered RCC region across acquisitions to ensure voxel-wise comparison within tumor region. Note that annotated region and structures are consistent across all three columns. Dice similarity coefficients between test/retest and test/midtreatment volumes were calculated as 0.90 and 0.83, respectively (note ideal Dice value between two volumes should be 1).

Figure 3

Box plots showing %Δ between test/midtreatment acquisitions for patient 1 in both the RCC and healthy tissue regions for top-ranked PET/MRI radiomic features (based on scoring function S_overall) as well as the original signal intensities.

Figure 4

Percent difference maps based on treatment-related changes between corresponding voxels in test/midtreatment acquisitions for patient 1 in the (A) healthy tissue and (B) RCC regions for PET/MRI radiomic features. (C) Difference maps of SUV, ADC energy, and T2w difference average are combined based on each feature’s weighted contribution factor to compute an integrated MP-PET/MRI map, reflecting a comprehensive characterization of early treatment-related changes and response.