Parametric Method Performance for Dynamic 3'-Deoxy-3'-18F-Fluorothymidine PET/CT in Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Carcinoma Patients Before and During Therapy

The objective of this study was to validate several parametric methods for quantification of 3'-deoxy-3'-18F-fluorothymidine (18F-FLT) PET in advanced-stage non-small cell lung carcinoma (NSCLC) patients with an activating epidermal growth factor receptor mutation who were treated with gefitinib or erlotinib. Furthermore, we evaluated the impact of noise on accuracy and precision of the parametric analyses of dynamic 18F-FLT PET/CT to assess the robustness of these methods. Methods: Ten NSCLC patients underwent dynamic 18F-FLT PET/CT at baseline and 7 and 28 d after the start of treatment. Parametric images were generated using plasma input Logan graphic analysis and 2 basis functions-based methods: a 2-tissue-compartment basis function model (BFM) and spectral analysis (SA). Whole-tumor-averaged parametric pharmacokinetic parameters were compared with those obtained by nonlinear regression of the tumor time-activity curve using a reversible 2-tissue-compartment model with blood volume fraction. In addition, 2 statistically equivalent datasets were generated by countwise splitting the original list-mode data, each containing 50% of the total counts. Both new datasets were reconstructed, and parametric pharmacokinetic parameters were compared between the 2 replicates and the original data. Results: After the settings of each parametric method were optimized, distribution volumes (VT) obtained with Logan graphic analysis, BFM, and SA all correlated well with those derived using nonlinear regression at baseline and during therapy (R2 ≥ 0.94; intraclass correlation coefficient > 0.97). SA-based VT images were most robust to increased noise on a voxel-level (repeatability coefficient, 16% vs. >26%). Yet BFM generated the most accurate K1 values (R2 = 0.94; intraclass correlation coefficient, 0.96). Parametric K1 data showed a larger variability in general; however, no differences were found in robustness between methods (repeatability coefficient, 80%-84%). Conclusion: Both BFM and SA can generate quantitatively accurate parametric 18F-FLT VT images in NSCLC patients before and during therapy. SA was more robust to noise, yet BFM provided more accurate parametric K1 data. We therefore recommend BFM as the preferred parametric method for analysis of dynamic 18F-FLT PET/CT studies; however, SA can also be used

Effects of tracer uptake time in non-small cell lung cancer 18F-FDG PET radiomics

Positron emission tomography (PET) radiomics applied to oncology allows the measurement of intra-tumoral heterogeneity. This quantification can be affected by image protocols hence there is an increased interest in understanding how radiomic expression on PET images is affected by different imaging conditions. To address that, this study explores how radiomic features are affected by changes in 18F-FDG uptake time, image reconstruction, lesion delineation, and radiomics binning settings.Methods: Ten non-small cell lung cancer (NSCLC) patients underwent 18F-FDG PET scans on two consecutive days. On each day, scans were obtained at 60min and 90min post-injection and reconstructed following EARL version 1 (EARL1) and with point-spread-function resolution modelling (PSF-EARL2). Lesions were delineated using thresholds at SUV=4.0, 40% of SUVmax, and with a contrast-based isocontour. PET image intensity was discretized with both fixed bin width (FBW) and fixed bin number (FBN) before the calculation of the radiomic features. Repeatability of features was measured with intraclass correlation (ICC), and the change in feature value over time was calculated as a function of its repeatability. Features were then classified on use-case scenarios based on their repeatability and susceptibility to tracer uptake time.Results: With PSF-EARL2 reconstruction, 40% of SUVmax lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (ICC>0.9), 39% being classified for dual-time-point use-case for being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with unclear dependency on time, 20% classified for cross-sectional use while being robust to tracer uptake time changes, and 6% were discarded for poor repeatability. EARL1 images had one less repeatable feature than PSF-EARL2 (Neighborhood Gray-Level Different Matrix Coarseness), the contrast-based delineation had the poorest repeatability of the delineation methods with 45% features being discarded, and FBN resulted in lower repeatability than FBW (45% and 6% features were discarded, respectively).Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUVmax, and FBW intensity discretization. Based on their susceptibility to tracer uptake time, radiomic features were classified into specific NSCLC PET radiomics use-cases

Variability and repeatability of quantitative uptake metrics in [18F]FDG PET/CT imaging of non-small cell lung cancer: impact of segmentation method, uptake interval, and reconstruction protocol

OBJECTIVES: There is increased interest in various new quantitative uptake metrics beyond standardized uptake value (SUV) in oncology PET/CT studies. The purpose of this study is to investigate the variability and test-retest repeatability (TRT) of metabolically active tumor volume (MATV) measurements and several other new quantitative metrics in non-small cell lung cancer (NSCLC) using [18F]FDG PET/CT with different segmentation methods, user interactions, uptake intervals, and reconstruction protocols. METHODS: Ten advanced NSCLC patients received two whole-body [18F]FDG PET/CT scans at both 60 and 90 min post-injection. PET data were reconstructed with four different protocols. Eight segmentation methods were applied to delineate lesions with and without a tumor mask. MATV, maximum and mean SUV (SUVmax, SUVmean), total lesion glycolysis (TLG), and intralesional heterogeneity features were derived. Variability and repeatability were evaluated using a generalized estimating equations statistical model with Bonferroni correction for multiple comparisons. The statistical model, including interaction between uptake interval and reconstruction protocol, was applied individually to the data obtained from each segmentation method. RESULTS: Without masking, none of the segmentation methods could delineate all lesions correctly. MATV was affected by both uptake interval and reconstruction settings for most segmentation methods. Similar observations were obtained for the uptake metrics SUVmax, SUVmean, TLG, homogeneity, entropy, and zone percentage. No effect of uptake interval was observed on TRT metrics, while the reconstruction protocol affected the TRT of SUVmax. Overall, segmentation methods showing poor quantitative performance in one condition showed better performance in other (combined) conditions. For some metrics, a clear statistical interaction was found between the segmentation method and both uptake interval and reconstruction protocol. CONCLUSION: All segmentation results need to be reviewed critically. MATV and other quantitative uptake metrics, as well as their TRT, depend on segmentation method, uptake interval, and reconstruction protocol. To obtain quantitative reliable metrics, with good TRT performance, the optimal segmentation method depends on local imaging procedure, the PET/CT system and/or reconstruction protocol. Rigid harmonization of imaging procedure and PET/CT performance will be helpful in mitigating this variability

Effects of Tracer Uptake Time in Non-Small Cell Lung Cancer 18F-FDG PET Radiomics

Positron emission tomography (PET) radiomics applied to oncology allows the measurement of intra-tumoral heterogeneity. This quantification can be affected by image protocols hence there is an increased interest in understanding how radiomic expression on PET images is affected by different imaging conditions. To address that, this study explores how radiomic features are affected by changes in 18F-FDG uptake time, image reconstruction, lesion delineation, and radiomics binning settings. Methods: Ten non-small cell lung cancer (NSCLC) patients underwent 18F-FDG PET scans on two consecutive days. On each day, scans were obtained at 60min and 90min post-injection and reconstructed following EARL version 1 (EARL1) and with point-spread-function resolution modelling (PSF-EARL2). Lesions were delineated using thresholds at SUV=4.0, 40% of SUVmax, and with a contrast-based isocontour. PET image intensity was discretized with both fixed bin width (FBW) and fixed bin number (FBN) before the calculation of the radiomic features. Repeatability of features was measured with intraclass correlation (ICC), and the change in feature value over time was calculated as a function of its repeatability. Features were then classified on use-case scenarios based on their repeatability and susceptibility to tracer uptake time. Results: With PSFEARL2 reconstruction, 40% of SUVmax lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (ICC>0.9), 39% being classified for dual-time-point use-case for being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with unclear dependency on time, 20% classified for cross-sectional use while being robust to tracer uptake time changes, and 6% were discarded for poor repeatability. EARL1 images had one less repeatable feature than PSF-EARL2 (Neighborhood Gray-Level Different Matrix Coarseness), the contrast-based delineation had the poorest repeatability of the delineation methods with 45% features being discarded, and FBN resulted in lower repeatability than FBW (45% and 6% features were discarded, respectively). Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUVmax, and FBW intensity discretization. Based on their susceptibility to tracer uptake time, radiomic features were classified into specific NSCLC PET radiomics use-cases

Accuracy and Precision of Partial-Volume Correction in Oncological PET/CT Studies

Accurate quantification of tracer uptake in small tumors using PET is hampered by the partial-volume effect as well as by the method of volume-of-interest (VOI) delineation. This study aimed to investigate the effect of partial-volume correction (PVC) combined with several VOI methods on the accuracy and precision of quantitative PET. Methods: Four image-based PVC methods and resolution modeling (applied as PVC) were used in combination with several common VOI methods. Performance was evaluated using simulations, phantom experiments, and clinical repeatability studies. Simulations were based on a whole-body F-18-FDG PET scan in which differently sized spheres were placed in lung and mediastinum. A National Electrical Manufacturers Association NU2 quality phantom was used for the experiments. Repeatability data consisted of an F-18-FDG PET/CT study on 11 patients with advanced non-small cell lung cancer and an F-18-fluoromethylcholine PET/CT study on 12 patients with metastatic prostate cancer. Results: Phantom data demonstrated that most PVC methods were strongly affected by the applied resolution kernel, with accuracy differing by about 20%-50% between full-width-at half-maximum settings of 5.0 and 7.5 mm. For all PVC methods, large differences in accuracy were seen among all VOI methods. Additionally, the image-based PVC methods were observed to have variable sensitivity to the accuracy of the VOI methods. For most PVC methods, accuracy was strongly affected by more than a 2.5-mm misalignment of true (simulated) VOI. When the optimal VOI method for each PVC method was used, high accuracy could be achieved. For example, resolution modeling for mediastinal lesions and iterative deconvolution for lung lesions were 99% +/- 1.5% and 99% +/- 0.9% accurate, respectively, for spheres 15-40 mm in diameter. Precision worsened slightly for resolution modeling and to a larger extent for some image-based PVC methods. Uncertainties in delineation propagated into uncertainties in PVC performance, as confirmed by the clinical data. Conclusion: The accuracy and precision of the tested PVC methods depended strongly on VOI method, resolution settings, contrast, and spatial alignment of the VOI. PVC has the potential to substantially improve the accuracy of tracer uptake assessment, provided that robust and accurate VOI methods become available. Commonly used delineation methods may not be adequate for this purpose