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Clinical Investigation |
1 Department of Radiology, H. Mondor Hospital, AP-HP/Paris 12 University, Créteil, France; 2 Department of Nuclear Medicine, H. Mondor Hospital, AP-HP/Paris 12 University, Créteil, France; 3 Department of Hematology, H. Mondor Hospital, AP-HP/Paris 12 University, Créteil, France; and 4 Department of Nuclear Medicine, Tenon Hospital, AP-HP/Paris 6 University, Paris, France
Correspondence: For correspondence or reprints contact: Michel Meignan, MD, PhD, Service de Médecine Nucléaire, Hôpital Henri Mondor, 51 Ave. du Marechal de Lattre de Tassigny, 94010 Créteil, France. E-mail: michel.meignan{at}hmn.aphp.fr
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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65.7% compared with 79% (95% CI, 69%–88%) in those with reduction > 65.7% (P < 0.0001). Fourteen patients considered as positive on visual analysis could have been reclassified as good responders. Conclusion: SUV-based assessment of therapeutic response during first-line chemotherapy improves the prognostic value of early 18F-FDG PET compared with visual analysis in DLBCL.
Key Words: early PET • standardized uptake value • lymphoma • prognosis • response
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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Assessment of early response relies most often on visual analysis, which is subjective to the dichotomous interpretation of an observer or a panel of observers. In contrast to staging studies, where qualitative assessment of 18F-FDG uptake is usually sufficient, treatment monitoring may require objective quantification of 18F-FDG uptake changes. Standardized uptake value (SUV) is currently a popular semiquantitative, easy-to-calculate and noninvasive index of 18F-FDG metabolic rate (16,17). However, sufficient confidence in the technical aspects of SUV calculation is not established yet, and clinical evidence of whether SUV is superior to visual analysis for outcome prediction is still lacking (18,19). Furthermore, a clear cutoff for an adequate SUV reduction during treatment remains to be defined (20).
The purpose of our study was to assess the prognostic value of early 18F-FDG PET during first-line chemotherapy, using SUV semiquantification and comparison with visual analysis, in a homogeneous series of patients with diffuse large B-cell lymphoma (DLBCL).
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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7 mM. Before February 2004, the first 81 patients were scanned on a dedicated C-PET camera (ADAC). They were injected with 2 MBq/kg 18F-FDG and sat at rest for 83 ± 22 min before imaging. The acquisition consisted of 5–7 overlapping bed shifts, to cover a volume from the upper thigh to the skull base (25-cm field of view). For each bed position, a 6-min emission scan was acquired in a 3-dimensional (3D) coincidence mode, followed by a 1-min transmission scan (137Cs source). Images (144 x 144 matrix; voxel size, 4 x 4 x 4 mm3) were reconstructed using an iterative ordered-subsets expectation maximization (OSEM) algorithm with attenuation correction. The last 11 patients were scanned on a Gemini PET/CT system (Philips). They were injected with 5 MBq/kg 18F-FDG and rested for 69 ± 9 min before imaging. The acquisition featured a low-dose transmission CT scan (100 kV; 40 mAs; slice thickness, 5 mm), followed by the emission scan in 9–11 overlapping bed shifts (18-cm field of view), each for 3-min duration. Images (144 x 144 matrix; voxel size, 4 x 4 x 4 mm3) were reconstructed with a 3D row-action maximum-likelihood algorithm (RAMLA). All patients also underwent a concurrent diagnostic CT scan of the chest, abdomen, and pelvis before, during midtherapy, and after treatment completion and then every 6 mo for follow-up based on the criteria of Cheson et al. (21).
Visual Analysis of 18F-FDG Uptake
PET images were analyzed by a consensus of 2 experienced observers who were unaware of clinical, radiologic, and follow-up data. All foci were scored for their extent and intensity using a 3-point scale (1 = low, 2 = moderate, 3 = high) (3,7). The extent was scored within each lymphatic area, organ, or skeletal region, depending on the number of nodes or volume involved; the intensity was scored compared with surrounding tissues after upper thresholding of the data to have the liver activity around 30% of the gray scale. Then, PET2 was scored as positive or negative in comparison with PET1. Negative was defined as having either no residual abnormal uptake or having a unique residual site (with an extent score of 1) associated with an intensity score of 1, whereas all other previously hypermetabolic sites had disappeared. Positive was defined as having at least 1 residual site (with an extent score of 1) associated with an intensity score of 2, or as having 
2 residual sites with any extent and intensity scores.
SUV-Based Assessment of 18F-FDG Uptake
For each PET dataset, the tumor (T) with the most intense 18F-FDG uptake among all foci was carefully identified, relying on a graded color-scaled parametric analysis (Fig. 1). From the activity profile crossing the hottest point, an isocontour threshold was determined halfway between the background and the maximal pixel value (22) and was automatically propagated on adjacent slices to encompass the entire tumor volume. Maximal and mean counts-per-pixel were computed within the volumetric region of interest (ROI). If present, a central cold area was included. In addition, 2 large ROIs were manually drawn over gluteal muscle regions (Fig. 1), from which the counts-per-pixel were averaged to define the normal background (N).
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SUVs were calculated from the counts-per-pixel and normalized to body weight (BW) and body surface area, defined as BSA (m2) = 0.007184 weight (kg)0.425 x height (cm)0.725 (23,24), using the following formulas:
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 | (Eq. 2) |
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Statistical Analysis
To evaluate the prognostic value of early PET, event-free survival (EFS) and overall survival (OS) were chosen as endpoints. EFS was defined as the date of enrollment to first evidence of progression, relapse, or death from any cause. Data were censored if the patients were alive and free of progression or relapse at last follow-up. OS was defined as the date of enrollment to death from any cause. Data were censored if the patients were alive at last follow-up. Receiver-operating-characteristic (ROC) analysis was performed to determine an optimal cutoff value of uptake on PET2 or an optimal cutoff value of uptake reduction from PET1 to PET2 in predicting EFS—event versus no event—and OS—dead versus alive. Differences in SUVs between groups were analyzed with an unpaired Student t test, and significance was obtained when the 2-sided P value was < 0.05. Survival according to visual analysis and SUV-based assessment of early PET was depicted using the Kaplan–Meier plots and compared using the log-rank test.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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Visual Analysis of Survival
All patients demonstrated intense foci of uptake on PET1, as expected from DLBCL. At midtherapy, PET2 was interpreted as positive in 34 patients and negative in 58 patients. The 2-y estimate for EFS was 51% (95% confidence interval [CI], 34%–68%) in PET2-positive patients compared with 79% (95% CI, 68%–90%) in PET2-negative patients (P = 0.009; Fig. 2A). Positive and negative predictive values (PPVs and NPVs, respectively), as well as accuracies in predicting EFS and OS (65.2% and 68.5%, respectively) are listed in Table 2. Of the 34 PET2-positive patients, 17 remained free of an event at last follow-up.
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5.0, and these patients (n = 79) tended to have lower baseline SUVBWmax (12.8 ± 4.7) than those (n = 13) with SUVBWmax > 5.0 (15.4 ± 5.0; P = 0.073). Furthermore, SUVBWmax at PET1 failed to demonstrate a significant predictive value for EFS (area under ROC curve = 0.525, P = 0.7).
The percentage of SUVBWmax reduction from PET1 to PET2 averaged 60.7% ± 32.6% in the 32 patients whose disease progressed or who died versus 77.5% ± 12.4% in the 60 patients who remained free of disease (P < 0.0006). ROC analysis yielded an optimal cutoff value of 65.7% SUVBWmax reduction at midtherapy for predicting EFS. The overall accuracy increased to 76.1% (Table 2). In patients with SUVBWmax reduction 65.7% (n = 16), the 2-y estimate for EFS was only 21% (95% CI, 0%–42%) compared with 79% (95% CI, 69%–88%) in those with SUVBWmax reduction > 65.7% (n = 76) (P < 0.0001; Fig. 2B). Results obtained from both the reduction of SUVBWmean and T/N ratio showed slightly poorer accuracies and PPVs—that is, more false-positive scans. SUVmax reduction, whether normalized to BW or BSA, also gave slightly better PPVs and accuracies for OS (Table 2).
When considering the 18F-FDG uptake change with regard to the most active focus on the baseline scan only, ROC analysis led to an 83.3% PPV, 72.5% NPV, and 73.9% accuracy for EFS. The optimal cutoff value was 65.7%.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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A negative interim scan based on visual analysis during first-line chemotherapy has proven to be an independent indicator of favorable outcome among patients with low-risk or high-risk disease based on the International Prognostic Index (7). Moreover, a more recent study showed that a significant survival difference exists between patient groups on the basis of early PET results but not on the gene-expression profiles (25). However, visual analysis of PET may be improved because some PET-positive patients still have a good outcome (7,26,27). If response on interim 18F-FDG PET is to be used to guide second-line risk-adapted therapeutic strategies in the future, efforts should be made to decrease the false-positive scans so that patients are not overtreated—that is, to improve the PPV of PET (27). In the present series, 17 of 34 patients with residual 18F-FDG uptake, considered visually positive on PET2, remained free of an event. The same issue was also raised in a recent study with advanced-stage Hodgkin's disease (28). Such patients could have already shown significant reduction of 18F-FDG uptake after first-line chemotherapy but still presented with an increased activity compared with the surrounding normal tissue by visual analysis.
To be able to quantify the 18F-FDG metabolic rate instead of interpreting images only as positive or negative, Römer et al. demonstrated in 11 lymphoma patients that Patlak analysis of 18F-FDG kinetics may provide superior information in therapy monitoring (1). However, quantification of glucose metabolic rate, which requires dynamic imaging on a restricted field of view and measurement of the arterial input function, is generally regarded as too complex in routine practice (23,24). Moreover, it may not be suitable in DLBCL when the most active lesion indicating tumor viability can be outside the field of view (18–25 cm). In Hodgkin's lymphoma, Hutchings et al. demonstrated that SUV analysis of interim PET may help in patient stratification (29). Our study also shows that an easy-to-calculate semiquantitative index, such as the SUVmax, is adapted to assess early response and predict long-term outcome. The PPV of early PET for EFS can be improved from 50% with visual analysis to 81.3% when using SUVBWmax reduction of 65.7% from PET1 to PET2 as a cutoff value. Fourteen patients could have been considered good responders in this case without altering NPV (at the expense of 4 more false-negative scans), among whom 11 were in complete remission at the end of first-line therapy and remain free of an event at the last follow-up (Fig. 3). The overall accuracy for EFS prediction based on SUVBWmax reduction compared with visual analysis is 76.1% versus 65.2%, with even higher performance in predicting OS, as shown in Table 2. More importantly, Kaplan–Meier analysis demonstrates much higher statistical significance between EFS curves using the SUV approach.
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With regard to the imaging delay, we generally performed image acquisition >60 min after injection, which is considered the time required for the 18F-FDG uptake to reach a plateau (16). Although the time interval varied slightly between the C-PET and Gemini acquisitions for logistics reasons inherent to a multicenter study, no scan was performed earlier than 48 min.
The partial-volume effect is related primarily to the limited spatial resolution of PET (32) and results in an underestimation of 18F-FDG uptake in small lesions. Previous studies have shown that SUV measurement is more reliable for an object with a diameter at least 2.5-fold the PET intrinsic spatial resolution (31,32). For the same reason, SUVmax is regarded as better than SUVmean as a metabolic index, especially in small lesions (31). In our study, SUVmax, normalized either to BW or to BSA, demonstrated PPVs and accuracies for outcome prediction that were superior to those of SUVmean and T/N ratio, and we recommend the use of SUVmax. The method of ROI definition may also have an impact on the partial-volume effect: ROIs obtained from the PET activity profiles, as it was done in our study, correspond most closely to the actual tumor size (22). Moreover, we have investigated the differences in SUVs between PET-based and CT-based ROI definitions on coregistered PET/CT images of a simple phantom (a series of syringes of increasing diameters ranging from 0.4 to 2.5 cm, filled with a homogenated 18F-FDG solution) and showed that CT-based ROIs would not improve SUV measurements (33).
As to the correction scheme, SUV normalized to BW for many tissues was found to have a strong positive correlation with weight (34,35) because 18F-FDG uptake is lower in fat than in other tissues and, consequently, SUVs tend to be overestimated in heavier patients (24). Because body weight often changes during treatment, BSA normalization was proposed. In our study, SUVBSAmax values on PET2 alone showed a better PPV than SUVBWmax for EFS and OS prediction; however, when considering the percentage of SUV reduction, SUVBSAmax and SUVBWmax gave identical values and accuracies for EFS and OS prediction.
For all of the reasons discussed, it seems difficult to rely on one single SUV at a given time point to appreciate the therapeutic response and to predict outcome. Indeed, because a cutoff value for an absolute SUV can vary greatly between different institutions (here, SUVBWmax of 5.0 on PET2), the measurement of an interscan SUV reduction within the same institution is probably a better and more reproducible approach (here, a 65.7% SUVBWmax reduction). This is confirmed by the higher accuracy obtained for EFS from ROC analysis with the second analysis, for 18F-FDG uptake change, over the first one.
One could regret that most patients were scanned on the C-PET and few on the Gemini, which could introduce quantification bias. This limitation is due to the prospective nature of our study, with subsequent evolution of the technology. Even though the calibration factors could vary slightly between scanners related to different attenuation correction methods or reconstruction algorithms, univariate analysis has proven that SUVs obtained from these 2 systems showed no statistical difference. Most important, each patient had his or her 2 PET scans on the same machine; therefore, the potential systemic bias could have been eliminated by computing the SUVBWmax differences. Another limitation of our study is the use of a post hoc response criterion for SUV-based analysis (obtained from the same patient population), instead of a predefined response criterion, as we did for visual analysis.
It must be pointed out that in our study, when a lesion different from the baseline tumor showed the most intense activity on PET2, which happened in 17 patients (18% of 92), we used its SUV as the index of 18F-FDG uptake at midtherapy. When only the change of SUVBWmax within the initial tumor was considered in the analysis, more false-negative scans were noted in predicting EFS. Indeed, in this case the overall accuracy was slightly inferior (73.9% instead of 76.1%).
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CONCLUSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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References |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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