Purpose To evaluate candidate FDG-PET/CT imaging biomarkers for head and neck chemoradiotherapy outcomes in the cooperative group trial setting. Methods RTOG 0522 patients consenting to a secondary FDG-PET/CT sub-study were serially imaged at baseline and 8 weeks following radiation. Maximum standardized uptake value (SUVmax), SUV peak (mean SUV within a 1 cm sphere centered on SUVmax), and metabolic tumor volume (MTV) using 40% of SUVmax as threshold were obtained from primary tumor and involved nodes. Results Out of 940 patients entered onto RTOG 0522, 74 were analyzable for this sub-study. Neither high baseline SUVmax nor SUVpeak from primary or nodal disease were associated with poor treatment outcomes. However, primary tumor MTV above the cohort median was associated with worse LRC (HR 4.01, 95% CI [1.28, 12.52], p = 0.02) and PFS (HR 2.34, 95% CI [1.02, 5.37], p = 0.05). Although MTV and T stage appeared to correlate (mean MTV 6.4, 13.2, 26.8 for T2, T3, and T4 tumors, respectively), MTV remained a strong independent prognostic factor for PFS in bivariate analysis that included T stage. Primary MTV remained prognostic in p16-associated oropharyngeal cancer cases, although sample size was limited. Conclusion High baseline primary tumor MTV was associated with worse treatment outcomes in this limited patient subset of RTOG 0522. Additional confirmatory work will be required to validate primary tumor MTV as a prognostic imaging biomarker for patient stratification in future trials.

Purpose In recent years, multiple studies have demonstrated the value of volumetric FDG-PET/CT parameters as independent prognostic factors in patients with non-small cell lung cancer (NSCLC). We aimed to determine the optimal cut-off points of pretreatment volumetric FDG-PET/CT parameters in predicting overall survival (OS) in patients with locally advanced NSCLC, and to recommend imaging biomarkers appropriate for routine clinical applications. Methods Patients with inoperable stage IIB/III NSCLC enrolled in ACRIN 6668/RTOG 0235 were included. Pretreatment FDG-PET scans were quantified using semiautomatic adaptive contrast-oriented thresholding and local-background partial-volume-effect-correction algorithms. For each patient, the following indices were measured: metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmax, SUVmean, partial-volume-corrected TLG (pvcTLG) and pvcSUVmean for the whole-body, primary tumor and regional lymph nodes. The association between each index and patient outcome was assessed using Cox proportional hazards regression. Optimal cut-off points were estimated using recursive binary partitioning in a conditional inference framework and used in Kaplan-Meier curves with log-rank testing. The discriminatory ability of each index was examined using time-dependent receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC(t)). Results 196 patients were included. Pretreatment whole-body and primary tumor MTV, TLG and pvcTLG were independently prognostic of OS. Optimal cut-off points were 175.0, 270.9, and 35.5 cm3 for whole-body TLG, pvcTLG, and MTV, and were 168.2, 239.8, and 17.4 cm3 for primary tumor TLG, pvcTLG, and MTV, respectively. In time-dependent ROC analysis, AUC(t) for MTV and TLG were uniformly higher than that of SUV measures over all time points. Primary tumor and whole-body parameters demonstrated similar patterns of separation for those patients above versus below the optimal cut-off points in Kaplan-Meier curves, and in time-dependent ROC analysis. Conclusion We demonstrated that pretreatment whole-body and primary tumor volumetric FDG-PET/CT parameters, including MTV, TLG, and pvcTLG, are strongly prognostic for OS in patients with locally advanced NSCLC, and have similar discriminatory ability. Therefore, we believe that, after validation in future trials, the derived optimal cut-off points for primary tumor volumetric FDG-PET/CT parameters, or their more refined versions, could be incorporated into routine clinical practice, and may provide more accurate prognostication and staging based on tumor metabolic features.

Imaging Excellence (CQIE) initiative in 2010 to prequalify imaging facilities at all of the National Cancer Institute-designated comprehensive and clinical cancer centers for oncology trials using advanced imaging techniques, including PET. Here we review the CQIE PET/CT scanner qualification process and results in detail. Methods: Over a period of approximately 5 y, sites were requested to submit a variety of phantoms, including uniform and American College of Radiologyapproved phantoms, PET/CT images, and examples of clinical images. Submissions were divided into 3 distinct time periods: initial submission (T0) and 2 requalification submissions (T1 and T2). Images were analyzed using standardized procedures, and scanners received a pass or fail designation. Sites had the opportunity to submit new data for scanners that failed. Quantitative results were compared across scanners within a given time period and across time periods for a given scanner. Results: Data from 65 unique PET/CT scanners across 56 sites were submitted for CQIE T0 qualification; 64 scanners passed the qualification. Data from 44 (68%) of those 65 scanners were submitted for T2. From T0 to T2, the percentage of scanners passing the CQIE qualification on the first attempt rose from 38% for T1 to 67% for T2. The most common reasons for failure were SUV outside specifications, incomplete submission, and uniformity issues. Uniform phantom and American College of Radiology-approved phantom results between scanner manufacturers were similar. Conclusion: The results of the CQIE process showed that periodic requalification may decrease the frequency of deficient data submissions. The CQIE project also highlighted the concern within imaging facilities about the burden of maintaining different qualifications and accreditations. Finally, for quantitative imaging-based trials, further evaluation of the relationships between the level of the qualification (e.g., bias or precision) and the quality of the image data, accrual rates, and study power is needed.

We present an overview of the Centers for Quantitative Imaging Excellence (CQIE) program, which was initiated in 2010 to establish a resource of clinical trial-ready sites within the National Cancer Institute (NCI)-designated Cancer Centers (NCI-CCs) network. The intent was to enable imaging centers in the NCI-CCs network capable of conducting treatment trials with advanced quantitative imaging end points. We describe the motivations for establishing the CQIE, the process used to initiate the network, the methods of site qualification for positron emission tomography, computed tomography, and magnetic resonance imaging, and the results of the evaluations over the subsequent 3 years.

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