PET-Based Primary Tumor Volumetric Parameters and Survival of Patients With Non—Small Cell Lung Carcinoma
Abstract
OBJECTIVE. The purpose of the study was to assess metabolic tumor volume and total glycolytic activity of the primary tumor as prognostic parameters for outcome in patients with non–small cell lung carcinoma (NSCLC).
MATERIALS AND METHODS. Thirty-nine patients who had undergone a baseline staging PET/CT examination at our institution for the diagnosis of NSCLC were retrospectively identified. The maximum standardized uptake value (SUVmax), metabolic tumor volume, and total glycolytic activity were segmented from PET using the gradient method; 12-month survival and overall survival at the end of follow-up were used as outcome measures. Multivariate logistic regression, receiver operating characteristic curve analysis, and Kaplan-Meier curves for survival analysis were generated and compared using the Mantel-Cox log-rank test.
RESULTS. The mean gradient-based metabolic tumor volume and gradient-based total glycolytic activity were significantly greater in the patients who died (93.3 mL and 597.5 g) than in those who survived (19.3 mL and 193.9 g, respectively) (p < 0.003 and p < 0.031). There was no statistically significant difference in the mean SUVmax between the patients who survived (12.7) at 12 months and those who had died (13.1) (p = 0.85). On multivariate analysis, gradient-based metabolic tumor volume was the only variable associated with 12-month mortality when adjusted for all other factors. The area under the curve (AUC) for gradient-based metabolic tumor volume was 0.77 (p < 0.006). A significant difference in the time to survival was observed between high and low gradient-based metabolic tumor volume (log-rank p < 0.05) cohorts using the median gradient-based metabolic tumor volume (9.7 mL) as the cut point.
CONCLUSION. PET-based volumetric imaging parameters are potential prognostic markers of outcome in patients with NSCLC.
Eighty percent of lung cancers are non–small cell lung carcinomas (NSCLCs) and 20% are small cell lung carcinomas. TNM staging currently is the most important factor in determining prognosis in patients with NSCLC [1] as well as in choosing treatment strategy [2, 3]. Gross tumor volume (GTV), as determined by CT and 3D conformal radiation therapy planning, has been shown to predict overall and cause-specific survival and local tumor control [4]. However, these imaging parameters are anatomic and may not provide functional information about the biology of the tumor, which may add prognostic value to the conventional staging.
FDG PET/CT has emerged as the preferred method for noninvasive staging of NSCLC [5–8]. The degree of FDG hypermetabolism as measured by the standardized uptake value (SUV) in the primary lesion at the time of diagnosis has been shown to predict prognosis in multiple solid tumors in humans [9–17]. However, the maximum SUV (SUVmax), the most commonly used FDG imaging parameter, may be limited because it represents a single pixel value and can be influenced by statistical bias, especially in intensely FDG-avid lesions, and by partial volume effects [18]. There is increasing interest in the use of 3D volumetric parameters such as metabolic tumor volume and total glycolytic activity as oncologic imaging bio-markers. Metabolic tumor volume may be a prognostic imaging biomarker for overall survival and local control [19–23] and may add value to American Joint Committee on Cancer staging classification [24].
The purpose of this exploratory study was to assess metabolic tumor volume and total glycolytic activity of the primary tumor as prognostic imaging parameters for outcome in patients with NSCLC.
Materials and Methods
Patients and Study Design
In this retrospective study, PET/CT scans were retrieved from the institutional electronic archival system and reviewed. This study was HIPAA compliant, institutional review board approval was obtained, and the informed consent requirements were waived. Thirty-nine patients were identified who had undergone a baseline staging PET/CT examination at our institution between March 2007 and May 2009 for the diagnosis of lung cancer and had at least 1-year follow-up. Thirty-two patients had a routine PET/CT examination without IV contrast material and seven patients had a diagnostic PET/CT study with IV contrast material.
PET/CT
All PET/CT studies were performed on a PET/CT scanner (Discovery STE 16, GE Healthcare) according to the institutional standard clinical protocol. Weight, height, and blood glucose levels were recorded for all patients. The mean blood glucose level was 100.3 ± 16.1 (SD) mg/dL, and patients were injected with a mean FDG dose of 12.3 ± 2.6 mCi (455.1 ± 96.2 MBq) and the uptake time was for 84 ± 32 minutes (mean ± SD). The amount of injected radioactivity was monitored by quantification of the radioactivity of the syringe before and after injection. All patients were scanned from the skull base to the mid thigh. PET scans were obtained using 3D imaging with emission scans that ranged from 2 to 4 minutes with an FOV of 50 cm. Patients with a body mass index of greater than 30 were imaged using a 2D technique with emission scans lasting 6 minutes. PET scan slices were 3.27 mm thick and reconstructed every 3.27 mm.
The CT scans were obtained to match the PET scans' FOV and slice thickness. Two types of CT scans were obtained: either scans for only attenuation correction and lesion localization of the PET data or scans for attenuation correction and lesion localization and a diagnostic level CT scan with IV contrast material. Both scans were obtained using a 512 × 512 matrix. The pitch was approximately 1.75 and collimation was 10 mm. Slices were reconstructed at a 3.75 mm thickness with 3.27-mm spacing. The peak voltage of the x-ray beam was set at 120 kVp and the tube current–exposure time product was modulated but ranged from low (range, 80–150 mAs) to full dose levels. The diagnostic level CT scans were obtained with 120 mL of ioversol (Optiray 320, Tyco Health-care) at a rate of 3 mL/s and followed by a saline chase of 30 mL at the same rate. SmartPrep (GE Healthcare) was used to trigger imaging once the celiac trunk reached a density of 180–200 HU.
Standardized Uptake Value Measurements
All PET/CT studies were retrieved from the electronic archival system and were then reviewed on a workstation (MIMVista software, version 4.1, MIM Software) by a board-certified radiologist with a nuclear radiology fellowship and 3 years of experience as junior faculty. PET, CT, and fused PET/CT images were reviewed in axial, coronal, and sagittal planes. For the purposes of this study, the relevant imaging biomarker measurements were SUVmax, metabolic tumor volume, and total glycolytic activity obtained from PET. The SUVmax was defined as the maximum SUV within the tumor. The metabolic tumor volume was defined as the FDG-avid tumor volume. The total glycolytic activity was defined as follows:
where MTV is metabolic tumor volume and SUVmean is the mean SUV. Once the primary tumor was segmented, the software automatically calculated the SUVmax, SUVmean, metabolic tumor volume, and total glycolytic activity.
Tumor Volume Segmentation: Gradient Method
There are many methods to segment the metabolic tumor volume [25]. Gradient segmentation of tumor volume depends on the identification of tumor on the basis of a change in count level at the tumor border. Complex methods have been previously proposed that include denoising, deblurring, gradient estimation, and watershed transformation [26].
The gradient segmentation method used in MIMVista (version 4.1) calculates spatial derivatives along the tumor radii and then defines the tumor edge on the basis of derivative levels and the continuity of the tumor edge. The software relies on an operator-defined starting point near the center of the lesion. As the operator drags the cursor out from the center of the lesion, six axes extend out, providing visual feedback for the starting point of gradient segmentation. Spatial gradients are calculated along each axis interactively, and the length of an axis is restricted when a large spatial gradient is detected along that axis. The six axes define an ellipsoid that is then used as an initial bounding region for gradient detection. The reader adds regions until visually satisfied that the entire primary tumor is included in the contour [24, 27].
Volumetric parameters assessed in this study were the gradient-based metabolic tumor volume and threshold-based metabolic tumor volume using 38% SUVmax, 50% SUVmax, and 60% SUVmax to define the lesion border. The total glycolytic activity was similarly assessed with gradient-based total glycolytic activity and threshold-based total glycolytic activity using 38% SUVmax, 50% SUVmax, and 60% SUVmax (Fig. 1).
Statistical Methods
We present our summary statistics as the mean ± SD for continuous variables or frequency and percentage for categoric variables. We used an unpaired Student t test for comparing means and the chi-square test for categoric data for between-group analyses. Logistic regression analysis was performed with an outcome of 12-month survival as the dependent variable to identify the clinical and imaging parameters associated with 12-month survival. Because there was multicollinearity between metabolic tumor volume and total glycolytic activity, two separate logistic regression models for survival at 12 months were developed. Analyses were also performed with a bootstrap method using 1000 simple bootstrap samples with a 95% CI to minimize overfitting the model. Receiver operating characteristic (ROC) curve analysis was used to determine the area under the curve (AUC) to estimate the accuracy of the predictive ability of various imaging parameters. The optimum cut points for 12-month and overall survival based on optimal sensitivity and specificity were established. Kaplan-Meier curves for survival analysis were generated and compared using the Mantel-Cox log-rank test. Prism 5 (GraphPad Software) and SPSS 19 (SPSS) statistical packages were used for all analyses. All hypothesis tests are two-sided with a significance level of ≤ 0.05.
Results
Patients
Thirty-nine patients with NSCLC and at least 1-year follow-up who underwent an initial staging PET/CT examination at our institution between March 2007 and May 2009 were included in the study. There were 18 women and 21 men; the mean age ± SD was 67.6 ± 10.6 years, and the mean follow-up time ± SD was 16.9 ± 11.4 months. Twenty patients (51%) were dead and 19 patients (49%) were alive at the end of follow-up. The oncologic stages consisted of stage I, 10 patients (26%); stage II, six (15%); stage III, 16 (41%); and stage IV, seven (18%). A summary of patient and tumor characteristics can be found in Table 1.
| Characteristic | Value |
|---|---|
| Total no. of patients | 39 |
| Sex, no. (%) of patients | |
| Male | 21 (54) |
| Female | 18 (46) |
| Age (y) | |
| Mean ± SD | 67.6 ± 10.6 |
| Stage, no. (%) of patients | |
| I | 10 (26) |
| II | 6 (15) |
| III | 16 (41) |
| IV | 7 (18) |
| Follow-up time (mo) | |
| Mean ± SD | 16.9 ± 11.4 |
Imaging Parameters and Survival Status
When stratified by survival status at 12 months from the baseline PET scan, the mean gradient-based metabolic tumor volume was significantly greater in those who died (93.3 mL) than in those who survived (19.3 mL; p < 0.003). The mean gradient-based total glycolytic activity was also significantly higher in those dead at 12 months (597.5 g) than in those still alive (193.9 g; p < 0.031) (Fig. 2). There was no statistically significant difference in SUVmax between those who survived (12.7) at 12 months and those who had died (13.1) (p = 0.85).
Fig. 2A —Gradient-based metabolic tumor volume (MTV) and total glycolytic activity (TGA) values of patients with non–small cell lung carcinoma at baseline PET/CT.
A, Scatterplots show there were significant differences in gradient-based metabolic tumor volume (A) and gradient-based total glycolytic activity (B) between patients who were alive at 12 months and those who were dead at 12 months. Long horizontal line marks mean values, and whiskers show the standard error of measure (SEM). A, MTV mean 19.3 ml (SEM 6.72) for those who survived versus 93.3 (SEM 27.95) for those who died. B, TGA mean 193.9 (SEM 80.16) for those who survived versus 597.5 (SEM 189.4) for those who died.
Fig. 2B —Gradient-based metabolic tumor volume (MTV) and total glycolytic activity (TGA) values of patients with non–small cell lung carcinoma at baseline PET/CT.
B, Scatterplots show there were significant differences in gradient-based metabolic tumor volume (A) and gradient-based total glycolytic activity (B) between patients who were alive at 12 months and those who were dead at 12 months. Long horizontal line marks mean values, and whiskers show the standard error of measure (SEM). A, MTV mean 19.3 ml (SEM 6.72) for those who survived versus 93.3 (SEM 27.95) for those who died. B, TGA mean 193.9 (SEM 80.16) for those who survived versus 597.5 (SEM 189.4) for those who died.
When stratified by overall survival status at the end of follow-up, the mean gradient-based metabolic tumor volume was significantly greater in those who died (75.0 mL) than in patients who survived at the end of follow-up (21.9 mL; p < 0.035). There was no signifi-cant difference in the gradient-based total glycolytic activity and SUVmax of those alive or dead (p < 0.16 and p = 0.88, respectively).
Receiver Operating Characteristic Curve Analysis
ROC curve analysis was performed to determine the accuracy of volumetric parameters to differentiate those living at 12 months from those who died. The AUC for gradient-based metabolic tumor volume was 0.77 (95% CI, 0.60–0.93; p < 0.006) in differentiating those who had died at 12 months from those who survived. The median cut point of 9.7 mL of metabolic tumor volume had a sensitivity, specificity, and likelihood ratio (LR) of 80%, 66.7%, and 2.7, respectively. An optimum threshold of 79 mL had 47% sensitivity and 92% specificity and an LR of 5.6 in identifying patients who died within 1 year. The AUC for gradient-based total glycolytic activity was 0.76 (95% CI, 0.60–0.92; p < 0.007). The median cut point of 74-g gradient-based total glycolytic activity had a sensitivity, specificity, and LR of 80%, 66.7%, and 2.4 and the optimum cut point of 349 g had a sensitivity of 47%, specificity of 88%, and LR of 3.7.
ROC analysis was also performed for overall survival. The AUC for gradient-based metabolic tumor volume was 0.68 (95% CI, 0.51–0.85; p = 0.06) and for gradient-based total glycolytic activity was 0.69 (95% CI, 0.52–0.86; p = 0.04) for differentiating those who had died at the end of follow-up from those still alive. The median cut point of 9.7 mL has a sensitivity, specificity, and LR of 63.2%, 60%, and 1.6, respectively. For a cut point of 79 mL, the sensitivity, specificity, and LR were 37%, 90%, and 3.7, respectively. The median gradient-based total glycolytic activity cut point of 74 g has a sensitivity of 68%, specificity of 65%, and LR of 1.95. The optimum gradient-based total glycolytic activity cut point of 350 g had a sensitivity, specificity, and LR of 36%, 85%, and 2.46 in identifying patients who had died at the end of follow-up.
Kaplan-Meier Survival Curves
Using threshold values with optimal diagnostic accuracy on ROC analysis for both metabolic tumor volume and total glycolytic activity, Kaplan-Meier curves were generated for patients with values above the thresholds and for those with values below these cut points. Overall survival curves were compared using the Mantel-Cox log-rank test. A significant difference in time to survival was observed between gradient-based metabolic tumor volume (p < 0.05) and gradient-based total glycolytic activity (p < 0.04) (Fig. 3).
Fig. 3A —Kaplan-Meier overall survival curves.
A, There were significant differences in time of overall survival of patients with gradient-based metabolic tumor volume (A) (p < 0.05) and gradient-based total glycolytic activity (B) above and below optimum cut points of 7.9 mL and 74 g, respectively (p < 0.04).
Fig. 3B —Kaplan-Meier overall survival curves.
B, There were significant differences in time of overall survival of patients with gradient-based metabolic tumor volume (A) (p < 0.05) and gradient-based total glycolytic activity (B) above and below optimum cut points of 7.9 mL and 74 g, respectively (p < 0.04).
Multivariate Logistic Regression: 12-Month Mortality
The prognostic significance of stage, age, SUVmax, metabolic tumor volume, and total glycolytic activity was assessed by multivariate logistic regression analyses. There was multi-collinearity between metabolic tumor volume and total glycolytic activity (Pearson r = 0.94), as expected, given that total glycolytic activity is computed using metabolic tumor volume and SUVmean. The metabolic tumor volume and total glycolytic activity were incorporated in two separate models adjusting for all other parameters. On multivariate analysis, gradient-based metabolic tumor volume was the only variable associated with 12-month mortality when adjusted for stage, age, and SUVmax (Table 2). The gradient-based total glycolytic activity was not found to be associated with 12-month survival on multivariate analysis.
| Variable | Crude Hazard Ratio | 95% CI | p | Adjusted Hazard Ratio | 95% CI | p |
|---|---|---|---|---|---|---|
| Stage IV (reference) | 0.30 | 0.49 | ||||
| Stage I | 0.00 | 0.00–0.00 | 0.99 | 0.00 | 0.00–0.00 | 0.99 |
| Stage II | 0.08 | 0.01–1.26 | 0.07 | 0.07 | 0.01–2.15 | 0.13 |
| Stage III | 0.01 | 0.01–1.30 | 0.09 | 0.10 | 0.01–2.60 | 0.17 |
| Age | 1.01 | 0.95–1.08 | 0.75 | 1.07 | 0.96–1.18 | 0.22 |
| SUVmax | 1.01 | 0.90–1.13 | 0.85 | 0.87 | 0.72–1.06 | 0.16 |
| Gradient-based metabolic tumor volume | 1.02 | 1.00–1.03 | 0.02 | 1.02 | 1.00–1.04 | 0.04 |
Note—Gradient-based metabolic tumor volume was significant in both univariate and multivariate analyses. SUVmax = maximum standardized uptake value.
Discussion
Our results show that the metabolic tumor volume measured using a gradient-based segmentation algorithm was significantly higher in patients who had died at 12 months and at the end of follow-up. The gradient-based metabolic tumor volume was additionally found to be the only factor associated with mortality at 12 months when adjusted for stage, age, and SUVmax. ROC and Kaplan-Meier curve analyses indicate that gradient-based total glycolytic activity may also be a prognostic marker of survival in patients with NSCLC. The volumetric parameter (metabolic tumor volume) is potentially a better predictor of outcome than SUVmax.
Volumetric parameters, such as the GTV on CT, have been shown to predict local tumor control as well as overall and cause-specific survival in patients with NSCLC [4]. There is increasing interest in the utilization of functional rather than anatomic parameters in tumor volume delineation because incorporation of metabolic data may significantly improve determination of the biologically relevant lesion volume and even take into account the heterogeneity within the tumor itself [26, 28]. In volumetric measurement in NSCLC, FDG PET has been shown to add significantly to CT-derived GTV and to provide more accurate determination of true lesion volume particularly in the setting of changes in surrounding lung parenchyma and with incorporation of PET-positive nodes in nodal areas with otherwise insignificant CT findings [28, 29].
The significant association between metabolic tumor volume and short-term prognosis (i.e., mortality within 12 months) probably reflects the increased mortality expected in patients with a large burden of growing tissue (metabolically active fraction). For these patients, as long as the tumor grows beyond a certain rate, it is the volume of tumor at presentation that determines mortality within 1 year after diagnosis. The association between total glycolytic activity and 12-month survival may be related to disease burden as well as tumor aggressiveness. Our findings are consistent with previous studies in which higher values for metabolic tumor volume are associated with disease progression and overall mortality in patients with tumors in numerous subsites including cervical cancers, head and neck squamous cell carcinomas, and esophageal cancers [19–22, 30–33]. Lee et al. [33] assessed the prognostic significance of the metabolic tumor volume in 19 patients with NSCLC and, similar to our results, found that an increase in metabolic tumor volume of 25 mL (difference between 75th and 25th percentiles) was associated with disease progression and death after controlling for stage, intent of treatment (definitive or palliative), age, Karnofsky performance status, and weight loss. However, these previous studies did not explore additional methods of tumor volume segmentation, which may be important because metabolic tumor volume and total glycolytic activity values are dependent on the segmentation method.
We sought to further examine the relationship between overall survival and the segmentation algorithm used to delineate metabolic tumor volume and total glycolytic activity. The choice of segmentation tool has, in fact, been shown to influence both volume and shape of the resulting GTV [26, 28, 34]. Various methods for FDG PET–based volume delineation are currently used. Visual interpretation can be applied [35, 36] but is susceptible to subjective window level settings of the images, and accurate, reproducible segmentation is not possible when assessing complex shapes and nonhomogeneous uptake [35]. More objective methods such as isocontouring based on a set SUV [35, 37], a fixed threshold of the maximum intensity [29, 31, 38, 39], adaptive approaches incorporating the background activity [35, 40, 41], and gradient-based segmentation algorithms [26] have been used. Studies have attempted to identify the segmentation method that best correlates with true tumor volume [39, 42, 43], but controversy exists as to the optimal approach.
In our study, both threshold-based and gradient-based methods were used. Values for metabolic tumor volume and total glycolytic activity obtained via gradient-based and threshold-based segmentation algorithms for tumor volume delineation showed excellent correlation. On multivariate analysis, only gradient-based metabolic tumor volume was significantly associated with survival at 12 months. Previous reports examining the impact of metabolic volumes on survival in patients with NSCLC and in patients with other solid malignancies have used threshold intensity values of 50% of SUVmax [22, 33], based on data from phantom studies [38]. Our results suggest that a gradient-based method for metabolic tumor volume delineation may have an advantage over most arbitrarily chosen threshold values.
This study had several limitations including retrospective design and a relatively small sample size of 39 patients. In addition, there was a heterogeneity of tumor histopathologies included, which may vary in degree of FDG uptake. Because of the small numbers within each stage, we did not stratify our analysis by individual stage but we performed logistic regression analysis adjusting for stage, age, and SUVmax. Given the small sample and event size, we may have overfitted the model. We used the segmentation algorithms from only one commercial vendor of software workstations for image analysis and need to investigate multiple vendor-provided segmentation algorithms.
In conclusion, our exploratory study suggests that PET-based volumetric imaging parameters with gradient-based segmentation are potential prognostic markers of survival at 12 months and overall survival in patients with NSCLC. Further investigation with a larger study population is needed to validate our results.
Footnote
R. M. Subramaniam was supported by a GE-AUR Research Fellowship and received research grants from Siemens Molecular Imaging and the M. J. Fox Foundation and received research support from MimVista Software, Inc.
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© American Roentgen Ray Society.
History
Submitted: April 27, 2012
Accepted: July 3, 2012
First published: February 27, 2013
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