The Q.Clear is a valuable diagnostic tool, with well documented value for the evaluation of lung tumours [7, 9]. Probably it may be as useful and effective in assessment of PET/CT images in patients with other diseases, however further studies need to be performed. This retrospective study provides some new insight into the role of Q.Clear in diagnostic PET/CT in patients with lymphoma.
According to Barrington et al., Q.Clear is characterized by higher sensitivity but lower specificity as compared to OSEM [18]. Presented study shows that Q.Clear reconstruction algorithm may influence SUVmax values of both target lesions and reference regions that may subsequently lead to altered interpretation of the scans in a small proportion of patients. This impact is of particular significance when Deauville criteria are used, since upstaging from negative (DS = 3 or less) to positive scan (DS = 4 or 5) may lead to treatment escalation with administration of highly toxic and costly medication. After demonstration of the differences in DS in patients enrolled to our study, the main question arises, whether images with higher SUVmax values measured with Q.Clear in target lesions, did really represent residual lymphoproliferative disease or rather an inflammatory process. A definite answer would be possible with histopathological verification of the lesions in question that was obviously unavailable due to limited anatomic accessibility (mediastinum, abdomen) and suppressed immune competence during or after chemotherapy. We were only able to confirm one case of false positive PET result after the use of Q.Clear algorithm in a patient, who presented with suspicious inguinal lymph node, while the PET scan after OSEM showed negative result. This may confirm the assumption of lower specificity of Q.Clear algorithm. At the same time, we found a case of true positive ePET result with Q.Clear (false negative using OSEM), which may suggest slightly higher sensitivity of the Q.Clear algorithm.
Initial clinical studies of the Q.Clear algorithm demonstrated the increased SUVmax values in smaller lesions [5, 6, 19]. Therefore, it was of special interest whether the small size of lymphoma lesions influenced PET/CT interpretation while using Q.Clear. In order to briefly verify this hypothesis, we divided the target lesions into two subgroups according to their size. As proposed earlier by Kuhnert et al. [20], lesions smaller than and equal to 25 mm were defined as small. Following this arbitrary division, we have evaluated the percentage of small target lesions in the series of scans obtained at different stages of lymphoma management in our cohort. The results are presented in Table 4.
Table 4
Number of small target lesions (< 25 mm) in the subgroups
| Number of small target lesions | % of small target lesions |
s-PET | 6 out of 70 | 8.6 |
i-PET | 48 out of 70 | 68.6 |
e-PET | 47 out of 70 | 67.1 |
r-PET | 4 out of 13 | 30.1 |
As expected, in i-PET and e-PET scans, significantly higher rates of small target lesions have been observed as compared to s-PET and r-PET. Therefore, this is the higher representation of small lesions in i-PET and e-PET scans that may be responsible for the upstaging of Deauville score in a number of patients.
Another important issue related to the use of the novel reconstruction algorithm is its influence on PET/CT image interpretation in patients with lymphoma - a significant increase of SUVmax values were observed in MBPS, liver and target lesion. S. Barrington et al. [18] point out that the higher selective values of SUVmax in small lesions e.g. lymph nodes, with none or minor influence on the uptake of 18F-FDG in reference regions, i.e. MBPS and liver, may lead to false conclusions in image interpretation. In our study, however, in all four groups of PET scans, SUVmax values of MBPS and liver were rather lower while measured with Q.Clear than with OSEM. Our results are therefore slightly different than previously published observations that showed no difference or even slight increase of SUVmax in MBPS and liver regions [18, 21]. Also Matti et al. did not find any modification of the background signal in their recently published analysis with Q.Clear in different clinical conditions [6]. However, consistently with our data, they reported amplification of the signal of hypermetabolic findings, which led to an increase in signal-to-noise ratio, improving overall image quality.
It must be however pointed out that despite the slight decrease of SUVmax in reference regions with the use of Q.Clear, the increase of DS score, was caused in all analysed cases by the increase of SUVmax values in target lesions, not in reference regions.
To our knowledge, this study is the first comprehensive analysis of the clinical implications of this Bayesian penalised-likelihood reconstruction algorithm in lymphoma management. This issue is analyzed in a similar context in a study of Enilorac et al., however a different novel reconstruction algorithm was investigated there, that was based on a point spread function [22]. The authors reviewed 195 PET/CT scans in patients with diffuse large B-cell lymphoma. Despite the difference of the technique, obtained results were similar to ours. Discordant values of DS were found in 14% of patients and the classification change in terms of negativity-positivity was observed in 5% (respective values in our study were: 15.7% and 4.3%). It should be underlined, however, the authors did not exclude patients with DS = 1 as in our study – therefore these data are not quite directly comparable. Moreover, in contrast to our study, the change of interpretation was not only conversion to positivity. The algorithm analyzed by them led not only to upstaging to positivity (4 cases) but also to downstaging to negativity in one patient.
Therapeutic decisions in lymphoma patients are mostly based on the clinical guidelines, like those of National Comprehensive Cancer Network (NCCN), where PET/CT examination plays pivotal role [23–24]. There is hardly any other disease with such a strong influence of PET/CT on clinical decisions. The interpretation of PET/CT images, based on precise criteria like DS are of crucial importance. Those criteria and guidelines were developed before the introduction of new reconstruction algorithms like Q.Clear to PET/CT scanners and were based on previous PET system generations. Commonly used recommendations of scientific societies, including Lugano classification, are based on numerous large prospective clinical studies [14, 25], where the routine OSEM reconstruction algorithm were used in all scanners by all manufacturers. Novel reconstruction algorithms, like Q.Clear aiming at the improvement of tumour detection rate or improvement of spatial resolution are very helpful in various clinical conditions. We must be aware, however, of potential pitfalls caused by the new technology. The differences between OSEM and Q.Clear, which have been presented in this study, are minor and refer to only some aspects of clinical decision-making. Nevertheless, they do not allow us to unequivocally acknowledge the new technology as ready for introduction into clinical practice in lymphoma management.