Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies

Four strategies for calculating CSH were investigated. As mentioned above, CSH is normally obtained by plotting the per cent volume of a tumour with an SUV above a certain threshold against that threshold, which is varied from 0 to 100% of SUV_max. The AUC of this plot (AUC-CSH) is a quantitative index of uptake heterogeneity, where lower values correspond with increased heterogeneity. However, in this case AUC-CSH is independent of SUV_max and volume, and therefore it may not be useful for response monitoring, where changes in SUV_max or metabolic volume should also be taken into account. To do so, three modifications of CSH were defined, taking into account changes in metabolic volume (CSH^V), SUV_max (CSH^S) or both (CSH^SV). This was achieved by not plotting relative (percentage) SUV_max or metabolic volume data, but rather absolute values or values relative to the baseline SUV_max or volume. Normalized AUC-CSH values (relative to the baseline) are calculated by:

All four strategies were evaluated with and without partial volume correction and with and without image denoising. Partial volume correction was performed using image-based Van Cittert deconvolution (CIT) [19, 20]. During each iterative deconvolution step (i.e. iteration, five in total) the update of the image was penalized using a Gibbs prior (weight of 0.25) applying a neighbourhood of one voxel in all directions [21]. The Gibbs prior calculates the summed difference between the central voxel and its direct neighbours. Inclusion of the prior reduces voxel variability in uniform areas of the image. Masks that were used to determine metabolic volumes were eroded with a single voxel in all dimensions to compensate for remaining sampling error (due to the use of voxels) at the borders of the masks. In addition, PET images were denoised using an edge-preserving bilateral filter (BF) [22].

In addition to AUC-CSH, the following parameters relating to tumour FDG uptake were also investigated: SUV_max, average SUV (SUV_mean) within a volume of interest (VOI), inverse standard deviation of SUV within a volume of interest (1/SD) and inverse coefficient of variation [1/COV, calculated as (SUV_mean/SD)×100%] within the metabolic (i.e. PET-based) or anatomical (i.e. CT-based) volume.

The purpose of the simulations was to evaluate to which extent CSH provides additional information about tumour response (and its heterogeneity) over SUV_max alone. In addition, the impact of partial volume effects and noise were evaluated by applying partial volume correction and image denoising prior to calculating AUC-CSH. To this end, two simulation studies were performed. As a starting point simulation 1 used a tumour with homogeneous FDG uptake, while for simulation 2 a tumour with heterogeneous FDG uptake was used. For both studies, various tumour responses showing different homogeneous and heterogeneous FDG uptake were simulated, as shown in Tables 1 and 2 and Fig. 1. All simulation software was implemented in-house using IDL (ITT Visual Information Solutions, Boulder, CO, USA). The effects of randoms and scatter events were not simulated in this study as this would require more complex (Monte Carlo-based) simulations. However, effects of attenuation and resolution were taken into account.

A 3-D mathematical thorax image and its corresponding μ-image, used for attenuation correction purposes, were derived from a dynamic FDG scan of a typical patient, as described in detail in Boellaard et al. [10]. In short, the procedure was as follows. For the simulated baseline (BL) scans of simulation 1 (BL1), a tumour with homogeneous FDG uptake was placed within the lungs (SUV_mean ~0.6) of the mathematical thorax image, using a tumour to background ratio of 10. In addition, various tumours having homogeneous and heterogeneous FDG uptake were placed within the lungs of the mathematical thorax image, as shown in Table 1 and Fig. 1. For the simulated baseline scans of simulation 2 (BL2), a tumour with heterogeneous FDG uptake was placed within the lungs of the mathematical thorax image, using a tumour (outer rim) to background ratio of 10. In addition, various tumours with homogeneous and heterogeneous FDG uptake were placed within the lungs of the mathematical thorax image, as shown in Table 2 and Fig. 1. For both simulations, these tumours were also placed in the lungs of the μ-image, having a μ-value equal to that observed in soft tissue at 511 keV (0.095 cm⁻¹). All of these images were then forward-projected. Partial volume effects were introduced by smoothing the obtained sinograms using a 5-mm full-width at half-maximum (FWHM) Gaussian kernel. Poisson noise was added to the emission sinograms obtained. These sinograms were then reconstructed using normalization and attenuation weighted ordered subsets expectation maximization (NAW-OSEM) with 4 iterations and 16 subsets, and post-smoothed using a 5-mm FWHM Gaussian kernel. The resulting noise level (COV of voxel values within a VOI ~15%) was similar to that observed in PET studies acquired on a PET/CT scanner (Gemini TF 64, Philips Healthcare, Cleveland, OH, USA) [23]. All reconstructed images consisted of 30 planes of 256×256 voxels with a voxel size of 2.56×2.56×2.56 mm³. For each tumour type, 100 noisy simulation images were generated to investigate accuracy and precision of the various parameters as described above.

Four different human FDG scans were included (Fig. 2). Two subjects were acquired on a whole-body PET/CT scanner (Gemini TF 64, Philips Healthcare, Cleveland, OH, USA). One subject (male, 78 kg, 59 years, 188 MBq FDG injection) had a primary lung tumour with heterogeneous uptake, whilst the other (female, 70 kg, 75 years, 184 MBq FDG injection) had a primary lung tumour with relative homogeneous uptake. In addition, one subject with a primary lung tumour (female, 60 years; baseline scan: 57 kg, 163 MBq FDG injection; response scan: 54 kg, 163 MBq FDG injection) was acquired twice (one scan before and one scan after one course of chemotherapy) on a whole-body PET scanner (ECAT EXACT HR+, CTI/Siemens, Knoxville, TN, USA). Furthermore, one subject with advanced liver metastases of a gastrointestinal malignancy (male, 65 years; baseline scan: 71 kg, 566 MBq FDG injection; response scan: 73 kg, 551 MBq FDG injection) was acquired twice (one scan before and one scan after three courses of chemotherapy) on a whole-body PET/CT scanner (Biograph, CTI/Siemens, Knoxville, TN, USA). All PET and CT data were collected as part of ongoing clinical studies, which were approved by an authorized medical Ethics Review Committee, and informed consent was obtained from each patient prior to inclusion in the study.

For all simulations and human scans, mean ± SD were calculated for all parameters under investigation. In the case of the simulations, VOIs were taken from baseline (VOI^BL) scans or redrawn on response (VOI^R) scans. For the human scans, VOIs were drawn using a 50% threshold isocontour method on baseline PET(/CT) scans and these were then transformed manually to response scans to obtain a repositioned VOI^BL. In addition, VOIs were drawn using a 50% threshold isocontour method on the response scans (VOI^R). All uptake (heterogeneity) parameters were calculated for all VOIs.