Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies

Patients with early-stage or locally advanced non-small cell lung cancer (NSCLC) are potential candidates for curatively intended therapy; however, management decisions are primarily based on the clinical tumor stage as a single factor only [1]. In the average of patients, adjuvant chemotherapy only showed modest survival benefits [2‐4], and therefore, more effective methods of treatment selection are highly warranted.

Consequently, numerous additional prognostic or predictive factors [5‐7], among image-derived parameters [8‐12], have been investigated aiming at more differentiated outcome prediction and more differentiated management decisions. Among parameters from positron emission tomography/computed tomography with [¹⁸F]fluorodeoxyglucose (FDG-PET/CT), asphericity (ASP) is a parameter that reflects shape irregularity of the primary tumor’s metabolic tumor volume (MTV), combining metric and metabolic features of the primary tumor. Three retrospective studies confirmed its independent prognostic value for progression-free (PFS) and overall survival (OS) in patients with NSCLC [13‐15]. The largest study (311 patients, UICC stage I–III) further showed that ASP, with a cutoff of > 19.5%, could identify patients with UICC stage II treated by surgery and adjuvant chemotherapy with high ASP and reduced PFS (median 11 months vs. not reached) and OS (22 months vs. not reached) [15]. ASP was superior for survival prediction compared to primary tumor’s maximum standardized uptake value (SUVmax) and MTV, two other previously proposed and common PET parameters [8, 9, 16, 17].

Studies on quantitative PET parameters have mostly been monocentric, but the main limitation of any PET parameter is its dependence on numerous technical factors including image reconstruction algorithms. Therefore, results may fail to reproduce in a multicenter approach unless harmonization between centers is ensured [18‐20]. SUVmax and MTV may vary by > 30% if basic ordered subset expectation maximization (OSEM) reconstruction is combined with time-of-flight (TOF) information and/or scanner-specific compensation for the point spread function (PSF) [19‐22].

Variability of ASP has not been investigated so far, but an impact of different reconstruction methods and resulting levels of image noise can be expected. The definition of ASP includes the MTV and its surface; therefore, a variability of MTV will cause variability of ASP. Since MTV also varies notably depending on the applied delineation algorithm [20, 23‐25], there are two potential sources of variability of ASP: image generation and lesion delineation.

The goal of the current study was to investigate differences in ASP resulting from variability in image generation (common reconstruction methods and acquisition times). The focus was on the assessment if the resulting variation is acceptable for application in multicenter studies and on defining the range of acceptable variation of the influencing factors. Specifically, the goal was not to investigate the trueness of ASP itself, to identify a ground truth or to define a highly optimized reconstruction protocol for a specific PET scanner. To the contrary, this study investigated whether ASP could still be used in multicenter studies under imperfect clinical conditions with different scanners and a certain variation in acquisition protocols (uptake time, acquisition time). Such variability introduced by image generation should be separated from variations in image post-processing, the software for image feature extraction [26] or variation in lesion delineation. Therefore, data were not post-processed (unless specified), and the same software and delineation method were used as in the preceding studies on ASP in NSCLC [13‐15]. To facilitate interpretation, SUVmax and MTV were investigated analogously for comparison.

This study found that ASP differences between reconstruction algorithms were significantly higher than corresponding SUVmax and MTV differences (Table 2). This may be explained by a combined effect of changes in SUVmax (suppression of local maxima and therefore a decreasing absolute threshold and increasing MTV size) and changes in MTV surface (smoothed, smaller MTV surface) on the ASP. Coarseness of the MTV surface is likely to differ with variation in reconstructed spatial resolution, which—in conventional iterative reconstruction algorithms—is mainly determined by the width of the in-plane filter. Therefore, if threshold-based MTV delineation is applied, wider filters can be expected to result in lower ASP. In Bayesian-penalized likelihood reconstruction (e.g., GE’s Q.Clear), post-processing is not applied, and smoother images are generated by increasing the penalization factor β.

However, since ASP is supposed to serve as part of prognostic/predictive models based on a predefined cutoff, even substantial inter-method differences may be clinically irrelevant if classification of individual patients into groups of high versus low ASP remains concordant. Applying a strict cutoff for ASP of > 19.5% [15], discordantly classified cases compared to the reference algorithm accounted for 2% (TOF_4/8) or 4% (PSF + TOF_2/17) at spatial resolution of approx. 7-mm FWHM. This could be acknowledged as acceptably low for application of ASP in a multicenter study. If a less strict cutoff with ± 5% tolerance (ASP between 18.53% and 20.48%) was applied, no discordant cases at 7-mm FWHM were observed for TOF_4/8 and PSF + TOF_2/17. This underlines that inter-method ASP differences at comparable spatial resolution are clinically relevant only if ASP is close to the predefined cutoff. Furthermore, this range of tolerance is well covered by the range of possible ASP cutoffs (17% to 39%) within which ASP remained significantly prognostic for PFS in previously reported patients with UICC stage II NSCLC [15].

Relative differences and discordant proportions tended to be higher with Q.Clear. Notably, Q.Clear showed systematically lower image noise at any level of spatial resolution (Table 1 and Fig. 2). In contrast to conventional algorithms, relative ASP differences with Q.Clear compared to the reference algorithm were higher at 7 versus 7 mm than at 5 versus 7 mm (Table 2) or at 7 versus 9 mm (Additional file 2: Table S8). Simultaneously, noise levels at 5 versus 7 mm and 7 versus 9 mm were also more comparable to the reference algorithm than at 7 versus 7 mm. However, the same observation was not true for SUVmax and MTV or with the conventional algorithms. Consequently, similar reconstructed spatial resolution rather than the noise level should guide the choice of reconstruction algorithms for harmonization for multicenter purposes. Furthermore, Q.Clear, or Bayesian-penalized likelihood reconstruction in general, may not be optimal to achieve minimal ASP deviations if the reference is a conventional algorithm.

With the PET scanner used in the present study, variation of image noise between algorithms was especially prominent at spatial resolution of 5-mm FWHM (Table 1, Fig. 1). This partly explains high inter-method differences, which exceeded 100% for TOF_4/8 and TOF_4/16 (Table 2), and frequent discordant cases even if pairs of algorithms with 5 versus 5 mm FWHM were compared. In addition to higher noise, Gibbs artifacts (edge elevations) caused by PSF + TOF and Q.Clear reconstruction increase with narrower in-plane filters or lower β [40]. Consequently, SUVmax differences will be more prominent than at 7 mm or 9 mm FWHM. In contrast, in substantially smoothed data with 9-mm FWHM, PET parameters that are reflective of heterogeneity or irregularity of tracer accumulation, such as ASP may lose discriminatory power to detect “real” and clinically relevant differences between tumors/patients. Therefore, under the conditions of the current analysis, 7-mm FWHM could be a feasible and reasonable target for harmonization in a multicenter approach. This is underlined by the observation that the MTV threshold for correlation between ASP and MTV was lowest for TOF_4/16/6.4 compared to TOF_4/16/9.5 and especially TOF_4/16/2.

If reconstructed spatial resolution is better than the target resolution (e.g., 5 mm instead of 7-mm FWHM), retrospective smoothing of data using formula (1) can be performed to achieve the anticipated resolution. This enabled inter-method differences and discordant proportions far closer to those observed with the original 7-mm data, irrespective of TOF, PSF + TOF or Q.Clear. Consequently, in a multicenter analysis, retrospective smoothing of data with better spatial resolution would be a valid option to ensure comparability. It is important to note that here the effective reconstructed spatial resolution is relevant [28], which can differ notably from the resolution determined via point sources.

A similar approach by the EANM Research Ltd. (EARL) harmonization project was reported by Kaalep et al. who analyzed SUV and MTV in FDG-PET data of NSCLC and lymphoma patients. Only after applying an additional Gaussian post-reconstruction filter of 6- to 7-mm FWHM to PET data reconstructed with PSF + TOF (compliant with the current EARL 2 standard) could SUV and MTV differences be reduced from approx. 30% to < 10% compared to reconstruction compliant with the former EARL 1 standard [41]. In a different approach to harmonization, Tsutsui et al. examined OSEM + TOF data of a NEMA IEC phantom obtained with a Siemens Biograph mCT and showed that errors compared to a simulated reference phantom were lowest with an in-plane filter of approx. 7- to 8-mm FWHM [42]. In a different study, the group achieved harmonization between 12 different PET scanners using contrast recovery (CR) of NEMA IEC phantom spheres by applying a scanner-specific Gaussian filter of up to 8-mm FWHM [43]. The current results of low SUVmax differences < 5% and MTV differences ≤ 6% at 7 versus 7 mm FWHM imply that both CR and reconstructed spatial resolution may be suitable surrogates for harmonization.

Shorter acquisition times of 120 s, 90 s or 60 s increased inter-method differences compared to 180 s with TOF_4/8/6 and TOF_4/16/6.4, while the increase was insignificant or less prominent with PSF + TOF_2/17/7 and Q.Clear₁₇₅₀. More importantly, proportions of discordantly classified cases by ASP, SUVmax or MTV remained similar or did not increase significantly—especially between 180 and 90 s. Therefore, equal acquisition times between PET systems/centers may be of secondary importance to achieve comparability in the investigated parameters, and differences as high as 180 s versus 90 s might be tolerable.

Voxel sizes may also vary between PET systems in a multicenter study. However, due to technical restrictions voxel size could not be freely varied during image reconstruction in this study. Therefore, the influence on ASP, SUVmax and MTV and the correcting effect of retrospective reslicing to the original voxel size could not be assessed. A further limitation of the current analysis is that the variation in reconstruction algorithms and acquisition time may not fully reflect differences between PET scanners beyond these factors. This would require comparative examinations with different scanners in each patient under identical conditions [20, 44]. For methodological consistency with the previous studies [13‐15], the same threshold-based algorithm [29] was used to delineate all lesions. Consequently, the presented results are not necessarily valid when lesions are delineated differently. Furthermore, although the current study demonstrated that the reconstructed spatial resolution can be used as a surrogate for scanner harmonization and showed lowest inter-method ASP differences and the lowest MTV threshold for correlation between ASP and MTV for 7.0 FWHM, this is not sufficient for a general recommendation of this specific spatial resolution for future studies regarding the ASP. This decision should also consider the performance of all PET scanners used in a specific study (best achievable reconstructed spatial resolution) and—if available—comparative clinical results on the value of ASP at different reconstructed spatial resolution.

Differences in ASP, SUVmax and MTV resulting from TOF_4/8, PSF + TOF_2/17 or Q.Clear compared to the reference algorithm TOF_4/16 were mainly determined by differences in reconstructed spatial resolution. Therefore, harmonization for ASP in multicenter studies should aim at comparable reconstructed spatial resolution between PET systems, which is determined by either in-plane filter width or the penalization factor β. With the PET scanner used in the present study, a resolution of 7-mm FWHM ensured that discordantly classified cases of high versus low ASP were at an acceptable proportion for TOF and PSF + TOF of < 5% (Q.Clear: 10%). Retrospectively smoothing data with better spatial resolution (i.e., lower FWHM) to the desired FWHM resulted in comparable results. These results require confirmation in a multicenter study.