Approval of human data exemption was obtained from the Institutional Review Board for this Health Insurance Portability and Accountability Act (HIPAA) compliant study.
Study population
Subjects were identified through the institutional tumor registry.
Patients were included in the study if they had a preoperative 18F-FDG PET done from 5 February 1999 through 21 March 2007 before undergoing attempted curative resection for pathologically documented stage I and II NSCLC and histological diagnosis was available.
Patients were excluded from the study if they had received any adjuvant or neoadjuvant chemotherapy or radiation therapy, had any prior history of lung cancer, or if SUVmax was not available due to the non-availability of SUVmax from the reports and the non-availability of PET images.
PET
Scans performed since July 2004 were obtained by a dedicated 16-slice whole-body PET/CT scanner (GE Discovery DST, GE Medical Systems, Milwaukee, WI, USA). All patients with 4-h fasting before the examination received an average of 560 MBq 18F-FDG intravenous injections. PET images were obtained 1 h after injection. The PET images were obtained at each bed position for 3 min with six to eight bed positions to cover the entire body. The PET images were obtained using a two-dimensional high-sensitivity mode with an axial field of view of 15 cm in a 256 × 256 matrix. A 3-slice overlap was utilized between the bed positions. The PET images were reconstructed iteratively on a 128 × 128 matrix using an ordered subsets expectation maximization algorithm for 30 subsets and two iterations, with a 7.0-mm post-reconstruction filter. In-plane resolution of 6.2 mm and axial resolution of 5.0 mm was obtained. Concomitant CT data were used for attenuation correction of all PET images in the quantitative analysis of SUV. The CT component of image acquisition used the following imaging parameters: 140 kVp, 120–200 mA, 0.8 s per CT rotation, pitch 1.75:1, detector configuration of 16 × 1.25 mm, and 3-mm slice thickness with oral contrast only.
PET and CT images were merged (fusion analysis) for functional and anatomic correlation. CT/PET images were displayed on AW/Xeleris and Medview workstations (General Electric Medical Systems, Milwaukee, WI, USA and Medimage, Ann Arbor, MI, USA). Scans performed before July 2004 were obtained on a dedicated whole-body PET only scanner (Advance, General Electric Medical Systems, Milwaukee, WI, USA) 1 h after injection of 18F-FDG (370 MBq, on average) and after the patients had fasted about 4 h. PET images were reconstructed using an iterative reconstruction algorithm with segmented attenuation correction. All PET data were visually examined and compared to the patient’s recent CT. The decision was finally made by correlating PET with CT to make sure the non-tumor regions were excluded from analysis. The SUV from both cameras were validated and correlated with phantom studies.
SUV was calculated using the following formula:
$$ {\text{SUV}} = {{{\text{lung}}\;{\text{cancer}}\;{\text{activity}}} \mathord{\left/{\vphantom {{{\text{lung}}\;{\text{cancer}}\;{\text{activity}}} {\left( {{{\text{dose}} \mathord{\left/{\vphantom {{\text{dose}} {{\text{body}}\;{\text{mass}}}}} \right.} {{\text{body}}\;{\text{mass}}}}} \right)}}} \right.} {\left( {{{\text{dose}} \mathord{\left/{\vphantom {{\text{dose}} {{\text{body}}\;{\text{mass}}}}} \right.} {{\text{body}}\;{\text{mass}}}}} \right)}} $$
The SUV
max was obtained by selecting volumetric regions of interest (VOIs) within the primary cancer site to include all tumor tissue but not any non-tumor tissue with potentially higher SUV than that of the tumor. The glucose concentration was also recorded for each patient before the injection of the
18F-FDG radiotracer in each PET scan.
Data collection
In 325 subjects SUVmax of the primary tumor was obtained from the initial PET reports. PET study interpretation had been independently performed by five experienced nuclear medicine physicians. In 38 subjects SUVmax was calculated from the PET images as it was not reported in the initial PET reports.
Baseline demographic, clinical, and tumor characteristics, treatment, follow-up, and survival data were obtained from the electronic medical record system and institutional tumor registry records.
The histological type was categorized according to the WHO classification system [
17].
Statistical methods
Variables studied included age, race, gender, preoperative SUVmax, pathological stage, tumor size, tumor laterality, type of surgery, histology subtype, and cytologic grade.
The continuous variables SUVmax, tumor size, and age were examined for normality and skewness. SUV and tumor size needed log transformations (with base 2).
Each variable was analyzed using univariate proportional hazards (Cox) regression analysis. Multivariate proportional hazards (Cox) regression analyses were applied to test the SUVmax’s independency of other prognostic factors for the prediction of overall survival.
In the initial multivariate Cox regression model, all variables that on univariate analysis were found to have a p value of less than 0.10 were included as covariates. SUVmax, tumor size, and age were treated as continuous variables. Variables were retained in the subsequent Cox regression modeling if they met the p value of less than 0.05 in the model. Nonsignificant variables were removed by stepwise backward elimination. Pathological staging was excluded from multivariate analysis due to potential interaction with tumor size.
The continuous variables SUVmax, tumor size, and age were then dichotomized by a median split. Survival curves stratified by median SUVmax were estimated by the Kaplan-Meier method and statistical differences were assessed using the log-rank test. Multivariate analyses were repeated after replacing continuous variables with median SUVmax, median tumor size, and median age.
Receiver-operating characteristic (ROC) curves were plotted to find out the optimal cutoff values of SUVmax yielding the maximal sensitivity plus specificity for predicting the overall survival. Survival curves stratified by optimal cutoff SUVmax were estimated by the Kaplan-Meier method, and statistical differences were assessed using the log-rank test. Multivariate analyses were performed again after replacing median SUVmax with optimal cutoff SUVmax.
The data were then stratified according to the pathological stage. The median SUVmax for each specific stage was calculated. For specific stages, survival curves stratified by median SUVmax were estimated by the Kaplan-Meier method, and statistical differences were assessed using the log-rank test. By plotting the ROC curves, we attempted to find out the optimal cutoff values of SUVmax for specific stages yielding the maximal sensitivity plus specificity for predicting the overall survival, but none could be established. No stage-specific analysis was performed for stage IIA due to the small number of subjects.
Statistical analyses were performed using SPSS® version 13.0 (SPSS Inc., Chicago, IL, USA).
In order to address the effects of the partial volume effects, the recovery coefficient (RC) was determined, and the SUV
max was corrected using the diameters of the tumor as the following:
$$ SU{V_{measured}} = \frac{{Counts.CF\left( {{{kBq} \mathord{\left/{\vphantom {{kBq} {ml}}} \right.} {ml} /{kg}}} \right)}}{{{{ID\left( {{{kBq} \mathord{\left/{\vphantom {{kBq} {ml}}} \right.} {ml}}} \right)} \mathord{\left/{\vphantom {{ID\left( {{{kBq} \mathord{\left/{\vphantom {{kBq} {ml}}} \right.} {ml}}} \right)} {Mass\left( {kg} \right)}}} \right.} {Mass\left( {kg} \right)}}}} $$
where CF = calibration factor and ID = injected dose.
The partial volume corrected SUV (SUVpvc) was given by:
$$ SU{V_{pvc}} = {{SU{V_{bkg}} + \left( {SU{V_{measured}} - SU{V_{bkg}}} \right)} \mathord{\left/{\vphantom {{SU{V_{bkg}} + \left( {SU{V_{measured}} - SU{V_{bkg}}} \right)} {RC}}} \right.} {RC}} $$
where SUVbkg = background SUV.