Overview of the clinical effectiveness of positron emission tomography imaging in selected cancers
ABSTRACT To assess the clinical effectiveness of positron emission tomography (PET) using 2-[18F]-fluoro-2-deoxy-D-glucose (FDG) in breast, colorectal, head and neck, lung, lymphoma, melanoma, oesophageal and thyroid cancers. Management decisions relating to diagnosis, staging/restaging, recurrence, treatment response and radiotherapy (RT) planning were evaluated separately.
Major electronic databases were searched up to August 2005 and a survey of UK PET facilities was performed in February 2006.
This assessment augments the systematic search undertaken in a previous review. Studies were limited to those using commercial dedicated PET or PET/computed tomography (CT) devices with FDG, in one of the eight cancers.
The new search identified six systematic reviews and 158 primary studies. An economic model for England showed that in non-small cell lung cancer (NSCLC) FDG-PET was cost-effective in CT node-negative patients, but not in CT node-positive patients. A less robust model also showed that FDG-PET was cost-effective in RT planning for NSCLC. A model for Scotland showed that in late-stage Hodgkin's lymphoma (HL), FDG-PET was cost-effective for restaging after induction therapy. For staging/restaging colorectal cancer, FDG-PET changed patient management in a way that can impact on curative therapy. For detection of solitary pulmonary nodule (SPN) there was also impact on patient management, but the resulting effect on patient outcomes was unclear. FDG-PET had an impact on patient management across paediatric lymphoma decisions, but further study of individual management decisions is required. For other cancer management decisions, the evidence on patient management is weak. FDG-PET was accurate in detecting distant metastases across several sites, but sensitivity was variable for detection of lymph-node metastases and poor in early stage disease where sentinel lymph-node biopsy would be used and for small lesions. There were 61 studies of treatment response. These were generally small and covered all cancers except melanoma. They showed that FDG-PET imaging could be correlated with response, in some cases, but the impact on patient management was not documented. There were 17 small studies of RT planning in four cancers; here, FDG-PET led to alteration of RT volumes and doses, but the impact on patient outcomes was not studied. FDG-PET improved diagnostic accuracy compared with alternatives in a number of other cancer management decisions, but more comparative evidence is needed. There were 23 studies of PET/CT in six cancers (excluding breast and melanoma). In these FDG-PET/CT generally improved accuracy by 10-15% over PET, resolving some equivocal images. The survey of PET facilities in the UK showed that PET and PET/CT are being used for a variety of cancer indications. PET facilities are not distributed evenly across the UK and use is inconsistent. Various research studies are underway in most centres, but only a few of these are collaborative studies. There is major variation in throughput and cost per scan (635-1300 pounds).
The strongest evidence for the clinical effectiveness of FDG-PET is in staging NSCLC, restaging HL, staging/restaging colorectal cancer and detection of SPN. Some of these may still require clinical audit to augment the evidence base. Other management decisions require further research to show the impact of FDG-PET on patient management or added value in the diagnostic pathway. It is likely that capital investment will be in the newer PET/CT technology, for which there is less evidence. However, as this technology appears to be slightly more accurate than PET/CT, the PET clinical effectiveness results presented here can be extrapolated to cover PET/CT. PET research could be undertaken on FDG-PET or FDG-PET/CT, using a standard cancer work-up process on typical patients who are seen within the NHS in England. For treatment response and RT planning, the need for larger studies using consistent methods across the UK is highlighted as a priority for all cancers. For all studies, consideration should be given to collaboration across sites nationally and internationally, taking cognisance of the work of the National Cancer Research Institute.
Full-textDOI: · Available from: Karen Facey, Jul 07, 2015
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ABSTRACT: Static whole-body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single-bed-coverage limiting the axial field-of-view to ∼15-20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole-body PET acquisition protocol of ∼45 min total length is presented, composed of (i) an initial 6 min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (six passes × seven bed positions, each scanned for 45 s). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of ten different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole body. In addition, the total acquisition length can be reduced from 45 to ∼35 min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error and the CNR metrics, resulting in enhanced task-based imaging.Physics in Medicine and Biology 09/2013; 58(20):7391-7418. DOI:10.1088/0031-9155/58/20/7391 · 2.92 Impact Factor
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ABSTRACT: In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (∼15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical (18)F-deoxyglucose patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ∼30 min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole-body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.Physics in Medicine and Biology 09/2013; 58(20):7419-7445. DOI:10.1088/0031-9155/58/20/7419 · 2.92 Impact Factor
Dataset: OH 2013 154(37) 1447-53