The age of reason for FDG PET image-derived indices

Abstract

The clinical use of PET imaging using 18 F-FDG is currently predominantly focused on diagnosis in the field of oncology. In this context, image analysis is largely based on visual interpretation and the use of simple image-derived indices, such as the maximum standardized uptake value (SUV max ), which corresponds to the voxel with the maximum activity concentration in the tumour scaled by the administered activity, patient weight and blood glucose concentration. On the other hand, during the last few years there has been increasing interest in the use of 18 F-FDG PET imaging for the prediction and monitoring of therapy response. In this context the SUV max has been also predominantly used, where differences between a pretreatment and posttreatment scan have been shown to closely correlate with clinical response to treatment in a number of different cancer models [1,2].The clear advantage of such a simplistic image-derived index is the ease of use, which has significantly contributed to its widespread application in clinical practice. SUV max is also in principle less dependent on partial volume effects (PVE) resulting from the limited spatial resolution of PET imaging [3]. On the other hand, there are a number of issues associated with its use, such as lack of robustness in terms of image noise, as well as in terms of the reconstruction algorithm and the associated corrections used during the reconstruction process. Clearly the impact of these issues can be minimized by standardization of the injection, acquisition, and image reconstruction and analysis protocols. In addition, the use of peak SUV (SUV peak ), calculated by averaging the voxel values inside a small region of interest centred on the tumour maximum activity concentration voxel, can reduce the sensitivity of SUV max to noise, although results may be sensitive to the actual definition of the ROI used to compute SUV peak [4]. However and most importantly, SUV max represents only very limited information in considering radiotracer accumulation and not considering information on the associated tumour uptake distribution or on the overall tumour functional volume. This is true even in the context of static acquisitions associated with routine whole-body 18 F-FDG PET imaging, where dynamic acquisitions, which offer the possibility, through kinetic modelling, of deriving quantitative glucose metabolic rates on a voxel-by-voxel basis, are not considered.Remaining with the context of static PET imaging, overall tumour activity accumulation can be alternatively characterized using the mean SUV (SUV mean ). SUV mean corresponds to the mean activity concentration in a delineated three-dimensional (3D) functional tumour volume. The most significant issues associated with the accuracy of SUV mean determination include the precision and robustness in the delineation of the tumour functional volume used [5,6], and the important influence of PVE which is closely related to the overall tumour size. The impact of PVE becomes more significant in studies of response to...

Hybrid Imaging: Instrumentation and Data Processing

“…Hybrid imaging has been key to cancer patient management [267], thanks also to its ability to depict tumor heterogeneity in a morphological and functional context [260]. Given that conventional indices, such as standard uptake value (SUV), maximum or metabolic tumor volume (MTV) are insufficient to characterize malignancies [268], several groups have started to investigate the feasibility of in vivo tumor characterization in the context of textural evaluation [269]. As a result of this initiative, first reports have appeared that point at promising results of a quantitative assessment of tumor heterogeneity in light of therapy response prediction [270,271], disease-specific survival [272] as well as prognostic stratification [273].…”

Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma

“…More recently, intra-tumour uptake heterogeneity has been identified as a potential source of treatment failure [11] and its characterization through 18 F-FDG PET imaging is currently generating a substantial amount of interest [12][13][14][15]. Such characterization provides additional and complementary PET image derived quantitative indices with potential value as already demonstrated in predicting therapy response or as prognostic factors in several cancers including lung [16], sarcoma [17], oesophageal [18,19] and rectal cancer [20].…”

FDG PET/CT methodology for evaluation of treatment response in lymphoma: from “graded visual analysis” and “semiquantitative SUVmax” to global disease burden assessment

“…We agree with these investigators that molecular imaging using FDG PET plays a pivotal role in risk stratification in treatment individualization. While these endeavours are important steps in PET/CT imaging for disease assessment and prognostication, we would like to share our views on the relevance of emerging PET metrics that have the potential to replace existing ones in the near future for optimal patient management [5].…”