Tumor radiomic heterogeneity: Multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy

Abstract

Background: Robust approaches to quantify tumor heterogeneity are needed to provide early decision support for precise individualized therapy.

Purpose: To conduct a technical exploration of longitudinal changes in tumor heterogeneity patterns on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI) and FDG positron emission tomography / computed tomography (PET/CT), and their association to radiation therapy (RT) response in cervical cancer.

Study type: Prospective observational study with longitudinal MRI and PET/CT pre-RT, early-RT (2 weeks), and mid-RT (5 weeks).

Population: Twenty-one FIGO IB₂ -IVA cervical cancer patients receiving definitive external beam RT and brachytherapy.

Field strength/sequence: 1.5T, precontrast axial T₁ -weighted, axial and sagittal T₂ -weighted, sagittal DWI (multi-b values), sagittal DCE MRI (<10 sec temporal resolution), postcontrast axial T₁ -weighted.

Assessment: Response assessment 1 month after completion of treatment by a board-certified radiation oncologist from manually delineated tumor volume changes.

Statistical tests: Intensity histogram (IH) quantiles (DCE SI_10% and DWI ADC_10% , FDG-PET SUV_max ) and distribution moments (mean, variance, skewness, kurtosis) were extracted. Differences in IH features between timepoints and modalities were evaluated by Skillings-Mack tests with Holm's correction. Area under receiver-operating characteristic curve (AUC) and Mann-Whitney testing was performed to discriminate treatment response using IH features.

Results: Tumor IH means and quantiles varied significantly during RT (SUV_mean : ↓28-47%, SUV_max : ↓30-59%, SI_mean : ↑8-30%, SI_10% : ↑8-19%, ADC_mean : ↑16%, P < 0.02 for each). Among IH heterogeneity features, FDG-PET SUV_CoV (↓16-30%, P = 0.011) and DW-MRI ADC_skewness decreased (P = 0.001). FDG-PET SUV_CoV was higher than DCE-MRI SI_CoV and DW-MRI ADC_CoV at baseline (P < 0.001) and 2 weeks (P = 0.010). FDG-PET SUV_kurtosis was lower than DCE-MRI SI_kurtosis and DW-MRI ADC_kurtosis at baseline (P = 0.001). Some IH features appeared to associate with favorable tumor response, including large early RT changes in DW-MRI ADC_skewness (AUC = 0.86).

Data conclusion: Preliminary findings show tumor heterogeneity was variable between patients, modalities, and timepoints. Radiomic assessment of changing tumor heterogeneity has the potential to personalize treatment and power outcome prediction.

Level of evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1388-1396.

Keywords: DCE; DWI; MRI; PET; radiomics; tumor heterogeneity.

Figure 1

MRI-PET fusion, DCE MR plateau (post-contrast), and DW MR ADC images for a representative patient at three different time points: prior to radiation therapy, early during RT (2 weeks), and midway during RT (5 weeks). Gross tumor was delineated at each time point (magenta contour), from which voxel distribution histogram features were extracted. Tumor voxels suffering from bladder artifact on FDG PET or distortion artifacts on DW MRI were excluded by threshold from calculation of SUV and ADC histogram features, respectively. Note the variable heterogeneity in image intensity between modalities (FDG PET, DCE MRI, DW MRI) and variable changes during therapy.

Figure 2

Intensity histograms of FDG-PET SUV, DCE-MRI SI ratio, and DW-MRI ADC at time points prior to, 2 weeks during, and 5 weeks during external beam radiation therapy for the same patient as in Figure 1, from which quantitative features of heterogeneity can be extracted. Note the variable histogram shapes and distributions between modalities and across time points. This patient is characterized by increasing FDG-PET SUV skewness, decreasing DCE-MRI SI ratio skewness, and decreasing DW-MRI ADC skewness with therapy.

Figure 3

Statistical box-whisker plots of tumor radiomic heterogeneity features of all patients compared between time points for each modality. (A) mean, (B) quantiles, (C) coefficient of variation, (D) skewness, (E) kurtosis of FDG PET standardized uptake value (SUV), DCE MRI plateau signal intensity ratio (SI), and DW MRI apparent diffusion coefficient (ADC) tumor voxel distributions. Mean (open marker), median (line), inter-quartile range (box) and range (whisker) across the patient cohort are shown for each imaging modality. Distributions of radiomic features were compared between imaging time points for each modality using the Skillings-Mack non-parametric test. P values were adjusted for multiple comparisons: *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 4

Statistical box-whisker plots of tumor radiomic heterogeneity features of all patients compared between modalities at each time point. (A) coefficient of variation, (B) skewness, (C) kurtosis of FDG PET standardized uptake value (SUV), DCE MRI plateau signal intensity ratio (SI), and DW MRI apparent diffusion coefficient (ADC) tumor voxel distributions. Mean (open marker), median (line), inter-quartile range (box) and range (whisker) across the patient cohort are shown for each time point. Distributions of radiomic features at the same time point were compared between modalities using the Skillings-Mack non-parametric test. P values were adjusted for multiple comparisons: *p < 0.05, **p < 0.01, ***p < 0.001.