Whole-Body [18F]-Fluoride PET SUV Imaging to Monitor Response to Dasatinib Therapy in Castration-Resistant Prostate Cancer Bone Metastases: Secondary Results from ACRIN 6687.

ACRIN 6687, a multi-center clinical trial evaluating differential response of bone metastases to dasatinib in men with metastatic castration-resistant prostate cancer (mCRPC), used [18F]-fluoride (NaF) PET imaging. We extend previous ACRIN 6687 dynamic imaging results by examining NaF whole-body (WB) static SUV PET scans acquired after dynamic scanning. Eighteen patients underwent WB NaF imaging prior to and 12 weeks into dasatinib treatment. Regional VOI analysis of the most NaF avid bone metastases and an automated whole-body method using Quantitative Total Bone Imaging software (QTBI; AIQ Solutions, Inc., Madison, WI, USA) were used. We assessed differences in tumor and normal bone, between pre- and on-treatment dasatinib, and evaluated parameters in association with PFS and OS. Significant decrease in average SUVmax and average SUVpeak occurred in response to dasatinib. Univariate and multivariate analysis showed NaF uptake had significant association with PFS. Pharmacodynamic changes with dasatinib in tumor bone can be identified by WB NaF PET in men with mCRPC. WB PET has the benefit of examining the entire body and is less complicated than single FOV dynamic imaging.

Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images.

Quantitative imaging biomarkers (QIBs) provide medical image-derived intensity, texture, shape, and size features that may help characterize cancerous tumors and predict clinical outcomes. Successful clinical translation of QIBs depends on the robustness of their measurements. Biomarkers derived from positron emission tomography images are prone to measurement errors owing to differences in image processing factors such as the tumor segmentation method used to define volumes of interest over which to calculate QIBs. We illustrate a new Bayesian statistical approach to characterize the robustness of QIBs to different processing factors. Study data consist of 22 QIBs measured on 47 head and neck tumors in 10 positron emission tomography/computed tomography scans segmented manually and with semiautomated methods used by 7 institutional members of the NCI Quantitative Imaging Network. QIB performance is estimated and compared across institutions with respect to measurement errors and power to recover statistical associations with clinical outcomes. Analysis findings summarize the performance impact of different segmentation methods used by Quantitative Imaging Network members. Robustness of some advanced biomarkers was found to be similar to conventional markers, such as maximum standardized uptake value. Such similarities support current pursuits to better characterize disease and predict outcomes by developing QIBs that use more imaging information and are robust to different processing factors. Nevertheless, to ensure reproducibility of QIB measurements and measures of association with clinical outcomes, errors owing to segmentation methods need to be reduced.

Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data.

PURPOSE: Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making. METHODS: To assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis. RESULTS: On the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between -36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between -50.5 to 701.5%, and the repeat difference for each institution ranged between -37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively. CONCLUSION: Analysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi-site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.

A Phase II Study of 3'-Deoxy-3'-18F-Fluorothymidine PET in the Assessment of Early Response of Breast Cancer to Neoadjuvant Chemotherapy: Results from ACRIN 6688.

UNLABELLED: Our objective was to determine whether early change in standardized uptake values (SUVs) of 3'deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) using PET with CT could predict pathologic complete response (pCR) of primary breast cancer to neoadjuvant chemotherapy (NAC). The key secondary objective was to correlate SUV with the proliferation marker Ki-67 at baseline and after NAC. METHODS: This prospective, multicenter phase II study did not specify the therapeutic regimen, thus, NAC varied among centers. All evaluable patients underwent (18)F-FLT PET/CT at baseline (FLT1) and after 1 cycle of NAC (FLT2); 43 patients were imaged at FLT1, FLT2, and after NAC completion (FLT3). The percentage change in maximum SUV (%ΔSUVmax) between FLT1 and FLT2 and FLT3 was calculated for the primary tumors. The predictive value of ΔSUVmax for pCR was determined using receiver-operating-characteristic curve analysis. The correlation between SUVmax and Ki-67 was also assessed. RESULTS: Fifty-one of 90 recruited patients (median age, 54 y; stage IIA-IIIC) met the eligibility criteria for the primary objective analysis, with an additional 22 patients totaling 73 patients for secondary analyses. A pCR in the primary breast cancer was achieved in 9 of 51 patients. NAC resulted in a significant reduction in %SUVmax (mean Δ, 39%; 95% confidence interval, 31-46). There was a marginal difference in %ΔSUVmax_FLT1-FLT2 between pCR and no-pCR patient groups (Wilcoxon 1-sided P = 0.050). The area under the curve for ΔSUVmax in the prediction of pCR was 0.68 (90% confidence interval, 0.50-0.83; Delong 1-sided P = 0.05), with slightly better predictive value for percentage mean SUV (P = 0.02) and similar prediction for peak SUV (P = 0.04). There was a weak correlation with pretherapy SUVmax and Ki-67 (r = 0.29, P = 0.04), but the correlation between SUVmax and Ki-67 after completion of NAC was stronger (r = 0.68, P < 0.0001). CONCLUSION: (18)F-FLT PET imaging of breast cancer after 1 cycle of NAC weakly predicted pCR in the setting of variable NAC regimens. Posttherapy (18)F-FLT uptake correlated with Ki-67 on surgical specimens. These results suggest some efficacy of (18)F-FLT as an indicator of early therapeutic response of breast cancer to NAC and support future multicenter studies to test (18)F-FLT PET in a more uniformly treated patient population.

18F-Fluoromisonidazole Quantification of Hypoxia in Human Cancer Patients Using Image-Derived Blood Surrogate Tissue Reference Regions.

UNLABELLED: (18)F-fluoromisonidazole ((18)F-FMISO) is the most widely used PET agent for imaging hypoxia, a condition associated with resistance to tumor therapy. (18)F-FMISO equilibrates in normoxic tissues but is retained under hypoxic conditions because of reduction and binding to macromolecules. A simple tissue-to-blood (TB) ratio is suitable for quantifying hypoxia. A TB ratio threshold of 1.2 or greater is useful in discriminating the hypoxic volume (HV) of tissue; TBmax is the maximum intensity of the hypoxic region and does not invoke a threshold. Because elimination of blood sampling would simplify clinical use, we tested the validity of using imaging regions as a surrogate for blood sampling. METHODS: Patients underwent 20-min (18)F-FMISO scanning during the 90- to 140-min interval after injection with venous blood sampling. Two hundred twenty-three (18)F-FMISO patient studies had detectable surrogate blood regions in the field of view. Quantitative parameters of hypoxia (TBmax, HV) derived from blood samples were compared with values using surrogate blood regions derived from the heart, aorta, or cerebellum. In a subset of brain cancer patients, parameters from blood samples and from the cerebellum were compared for their ability to independently predict outcome. RESULTS: Vascular regions of heart showed the highest correlation to measured blood activity (R(2) = 0.84). For brain studies, cerebellar activity was similarly correlated to blood samples. In brain cancer patients, Kaplan-Meier analysis showed that image-derived reference regions had predictive power nearly identical to parameters derived from blood, thus obviating the need for venous sampling in these patients. CONCLUSION: Simple static analysis of (18)F-FMISO PET captures both the intensity (TBmax) and the spatial extent (HV) of tumor hypoxia. An image-derived region to assess blood activity can be used as a surrogate for blood sampling in quantification of hypoxia.