Background Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45–60 minutes post-injection of 10 mCi of [18F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.

18 F-NaF, a PET radiotracer of bone turnover, has shown potential as an imaging biomarker for assessing the response of bone metastases to therapy. This study aimed to evaluate the repeatability of 18 F-NaF PET-derived SUV imaging metrics in individual bone lesions from patients in a multicenter study. Methods: Thirty-five castration-resistant prostate cancer patients with multiple metastases underwent 2 whole-body (test-retest) 18 F-NaF PET/CT scans 3 ± 2 d apart from 1 of 3 imaging sites. A total of 411 bone lesions larger than 1.5 cm 3 were automatically segmented using an SUV threshold of 15 g/mL. Two levels of analysis were performed: lesion-level, in which measures were extracted from individual-lesion regions of interest (ROI), and patient-level, in which all lesions within a patient were grouped into a patient ROI for analysis. Uptake was quantified with SUV max , SUV mean , and SUV total . Test-retest repeatability was assessed using BlandAltman analysis, intraclass correlation coefficient (ICC), coefficient of variation, critical percentage difference, and repeatability coefficient. The 95% limit of agreement (LOA) of the ratio between test and retest measurements was calculated. Results: At the lesion level, the coefficient of variation for SUV max , SUV mean , and SUV total was 14.1%, 6.6%, and 25.5%, respectively. At the patient level, it was slightly smaller: 12.0%, 5.3%, and 18.5%, respectively. ICC was excellent (.0.95) for all SUV metrics. Lesion-level 95% LOA for SUV max, SUV mean , and SUV total was (0.76, 1.32), (0.88, 1.14), and (0.63, 1.71), respectively. Patient-level 95% LOA was slightly narrower, at (0.79, 1.26), (0.89, 1.10), and (0.70, 1.44), respectively. We observed significant differences in the variance and sample mean of lesion-level and patient-level measurements between imaging sites. Conclusion: The repeatability of SUV max , SUV mean , and SUV total for 18 F-NaF PET/CT was similar between lesion-and patient-level ROIs. We found significant differences in lesion-level and patient-level distributions between sites. These results can be used to establish 18 F-NaF PET-based criteria for assessing treatment response at the lesion and patient levels. 18 F-NaF PET demonstrates repeatability levels useful for clinically quantifying the response of bone lesions to therapy.

The role of CT in PET/CT imaging includes acquisition techniques for diagnostic, anatomic localization, and attenuation correction purposes. Diagnostic reference levels of the volumetric CT dose index (CTDI vol ) are available for dedicated CT procedures on selected body regions, but similar reference levels for whole-body CT used in PET/CT examinations are limited. This work reports CTDI vol values from sites that conduct whole-body oncologic PET/CT examinations and participated in the scanner validation program of the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network. Methods: From 2010 to 2014, a total of 154 sites submitted CT acquisition parameters used in their clinical 18 F-FDG PET/CT oncology protocols. From these parameters, the CTDI vol was estimated using the ImPACT CTDI dosimetry tables. Histograms of CTDI vol values were created for each year, and descriptive statistics, including mean, median, and 75th percentile, were reported. Repeated-measures ANOVA was performed to determine whether significant differences occurred between reporting years. Results: A wide range of technical parameters was reported, most notably in tube current. Between 2010 and 2014, the median CTDI vol ranged from 4.9 to 6.2 mGy and the 75th percentile from 9.7 to 10.2 mGy. There was no significant change in CTDI vol between reporting years (repeated-measures ANOVA, P 5 0.985). Conclusion: The 75th percentile CTDI vol reported in this work was 9.8 mGy averaged over all reporting years. These data provide a resource for establishing CTDI vol reference values specific to performing CT in PET/CT whole-body examinations. The wide ranges of CT acquisition parameters reported by sites suggest that CTDI vol reference levels may be beneficial for optimization of CT protocols.

Intratumor heterogeneity in biologic properties and in relationships between various phenotypes may present a challenge for biologically targeted therapies. Understanding the relationships between different phenotypes in individual tumor types could help inform treatment selection. The goal of this study was to characterize spatial correlations of glucose metabolism, proliferation, and hypoxia in 2 histologic types of tumors. Methods Twenty canine veterinary patients with spontaneously occurring sinonasal tumors (13 carcinomas and 7 sarcomas) were imaged with 18F-FDG, 18F-labeled 39-deoxy-39-fluorothymidine (18F-FLT), and 61Cu-labeled diacetyl-bis(N4-methylthiosemicarbazone) (61Cu-ATSM) PET/CT on 3 consecutive days. Precise positioning and immobilization techniques coupled with anesthesia enabled motionless scans with repeatable positioning. Standardized uptake values (SUVs) of gross sarcoma and carcinoma volumes were compared by use of Mann– Whitney U tests. Patient images were rigidly registered together, and intratumor tracer uptake distributions were compared. Voxel-based Spearman correlation coefficients were used to quantify intertracer correlations, and the correlation coefficients of sarcomas and carcinomas were compared. The relative overlap of the highest uptake volumes of the 3 tracers was quantified, and the values were compared for sarcomas and carcinomas. Results Large degrees of heterogeneity in SUV measures and phenotype correlations were observed. Carcinoma and sarcoma tumors differed significantly in SUV measures, with carcinoma tumors having significantly higher 18F-FDG maximum SUVs than sarcoma tumors (11.1 vs. 5.0; P = 0.01) as well as higher 61Cu-ATSM mean SUVs (2.6 vs. 1.2; P = 0.02). Carcinomas had significantly higher population-averaged Spearman correlation coefficients than sarcomas in comparisons of 18F-FDG and 18F-FLT (0.80 vs. 0.61; P = 0.02), 18F-FLT and 61Cu-ATSM (0.83 vs. 0.38; P < 0.0001), and 18F-FDG and 61Cu-ATSM (0.82 vs. 0.69; P = 0.04). Additionally, the highest uptake volumes of the 3 tracers had significantly greater overlap in carcinomas than in sarcomas. Conclusion The relationships of glucose metabolism, proliferation, and hypoxia were heterogeneous across different tumors, with carcinomas tending to have high correlations and sarcomas having low correlations. Consequently, canine carcinoma tumors are robust targets for therapies that target a single biologic property, whereas sarcoma tumors may not be well suited for such therapies. Histology-specific PET correlations have far-reaching implications for the robustness of biologic target definition.

Purpose In dose painting, in which functional imaging is used to define biological targets for radiation therapy dose escalation, changes in spatial distributions of biological properties during treatment can compromise the quality of therapy. The goal of this study was to assess the spatiotemporal stability of 2 potential dose painting target—dhypoxia and proliferation—in canine tumors during radiation therapy. Methods and Materials Twenty-two canine patients with sinonasal tumors (14 carcinoma and 8 sarcoma) were imaged before hypofractionated radiation therapy with copper(II)-diacetyl-bis(N4-methylthiosemicarbazone) (Cu-ATSM) positron emission tomography/computed tomography (PET/CT) for hypoxia and 3′-deoxy-3′-18F-fluorothymidine (FLT) PET/CT for proliferation. The FLT scans were repeated after 2 fractions and the Cu-ATSM scans after 3 fractions. Midtreatment PET/CT images were deformably registered to pretreatment PET/CT images. Voxel-based Spearman correlation coefficients quantified the spatial stability of Cu-ATSM and FLT uptake distributions between pretreatment and midtreatment scans. Paired t tests determined significant differences between the patients’ respective Cu-ATSM and FLT correlations coefficients. Standardized uptake value measures were also compared between pretreatment and midtreatment scans by use of paired t tests. Results Spatial distributions of Cu-ATSM and FLT uptake were stable through mid-treatment for both sarcomas and carcinomas: the population mean ± standard deviation in Spearman correlation coefficient was 0.88 ± 0.07 for Cu-ATSM and 0.79 ± 0.13 for FLT. The patients’ Cu-ATSM correlation coefficients were significantly higher than their respective FLT correlation coefficients (P=.001). Changes in Cu-ATSM SUV measures from pretreatment to midtreatment were histology dependent: carcinomas experienced significant decreases in Cu-ATSM uptake (P<.05), whereas sarcomas did not (P>.20). Both histologies experienced significant decreases in FLT uptake (P<.05). Conclusions Spatial distributions of Cu-ATSM were very stable after a few fractions of radiation therapy. FLT spatial distributions were generally stable early in therapy, although they were significantly less stable than Cu-ATSM distributions. Canine tumors had significantly lower proliferative activity at midtreatment than at pretreatment, and they experienced histology-dependent changes in Cu-ATSM uptake.

Purpose: In dose painting, it is uncertain which of a tumor's biological properties should be targeted, and if plans for different tumor histologies are equally sensitive to the choice of biological target. This study characterizes the relationships between three potential biological targets ‐ glucose metabolism, proliferation, and hypoxia — in two different tumor histologies using PET/CT imaging. Methods: Twenty canine patients with sinonasal tumors (7 sarcomas and 13 carcinomas) were imaged using FDG, FLT, and Cu‐ATSM PET/CT on three consecutive days. Patients were immobilized and precisely positioned, and resulting images were rigidly registered. Within each tumor volume, voxel SUV distributions from different tracers were compared and inter‐tracer correlations were evaluated using voxel‐based Spearman correlation coefficients. Correlation coefficients were then Fisher‐transformed, and a two‐sided t‐test was applied to determine if sarcoma and carcinoma populations differed significantly in inter‐tracer correlations. SUV measures such as SUV_max, SUV_peak, and SUV_mean were also compared between sarcomas and carcinomas using Mann‐Whitney U‐tests. Results: Significant differences in inter‐tracer correlations were observed between sarcoma and carcinoma tumors. Population‐averaged Spearman correlation coefficients were significantly higher for carcinoma tumors than sarcoma tumors in comparisons of FLT:Cu‐ATSM (0.83 vs. 0.38; p<0.0001), FDG:FLT (0.80 vs. 0.61; p=0.02), and FDG:Cu‐ATSM (0.82 vs. 0.69; p=0.04). Tracer distributions generally overlapped in carcinomas; in sarcomas, however, different tracers clustered in different tumor regions. Carcinomas also had significantly higher average FDG SUV_max (11.1 vs. 5.0; p=0.01) and higher Cu‐ATSM SUV_mean (2.6 vs. 1.2; p=0.02) than sarcoma tumors. Conclusion: Carcinoma tumors, with high spatial correlations between tumor metabolism, proliferation, and hypoxia, are robust targets for therapies that target a single biological property. Sarcomas may not be well‐suited for such therapies. Histology‐specific robustness in biological target definition has large implications for dose painting strategies, as well as for other biologically targeted therapies.

Purpose: High precision within quantitative imaging studies is required to define biological targets for dose painting, and to evaluate responses to therapy. However, within PET imaging systems there are position‐dependent systematic fluctuations in contrast‐recovery. The objective of this study was to determine the impact of patient set‐up on PET‐based quantification of heterogeneous tumors. Methods: Ten dogs with sinonasal tumors received six repeated [18F]FDG‐PET scans. Between each PET acquisition, known set‐up errors were produced by varying patient position in precisely controlled 1.0±0.5 mm steps. Image noise was investigated by retrospectively varying acquisition time of a stationary scan. Resulting changes in SUVmax and PET‐based target volumes were evaluated. Correlation between corresponding voxels of co‐registered images determined repeatability of spatial distributions. A theoretical upper limit of repeatability was estimated from simulated PET images using empirical models of position‐dependent contrast‐recovery. Results: From the simulated PET images, a theoretical upper limit of 0.90 was estimated for the repeatability of spatial distributions when set‐up errors are present. When only image noise was varied between [18F]FDG‐PET scans, repeatability of spatial distributions was measured at 0.95. However, set‐up errors lead to significant decreases (p < 0.01) in voxel correlations. Introducing set‐up errors between [18F]FDG‐PET acquisitions reduced the repeatability of spatial distributions to 0.80, and caused changes of 5% in SUVmax and 10% in volumes of PET‐based targets. Conclusions: Set‐up errors as small as a few millimeters (< 3mm) reduced the reproducibility of quantitative PET imaging of heterogeneous tumors. PET‐based quantitative values generally varied within 10%, but target volumes within some tumors changed by 30%. Errors during PET acquisition will lead to uncertainty in quantifying changes in tumor function, and to limited accuracy and precision of PET‐based biological target volumes. Therefore, tumor delineation must be independent of SUV thresholds and uncertainty margins are required for PET‐based response quantification.

conducted. The overall effects of year and procedure groups were significant (P ¼ .014; Fig 1). The hazard ratio of OSR pre-2007 was 1.54 (95% confidence interval, 0.75-3.16) indicating higher risk, and the hazard ratio of EVAR was 0.58 (95% confidence interval, 0.23-1.42) indicating a lower risk.Conclusions: Survival after RAAA has improved since 2007, primarily driven by an "EVAR-first" approach despite increased overall comorbidities. Survival in patients after open aneurysm repair has also improved; however, the difference did not reach statistical significance. EVAR is the best first-line therapy for rAAA at experienced centers.