The objectives of this study were to investigate the relationship between CT-and 18 F-FDG PET-based tumor volumes in nonsmall cell lung cancer (NSCLC) and the impact of tumor size and uptake heterogeneity on various approaches to delineating uptake on PET images. Methods: Twenty-five NSCLC cancer patients with 18 F-FDG PET/CT were considered. Seventeen underwent surgical resection of their tumor, and the maximum diameter was measured. Two observers manually delineated the tumors on the CT images and the tumor uptake on the corresponding PET images, using a fixed threshold at 50% of the maximum (T 50 ), an adaptive threshold methodology, and the fuzzy locally adaptive Bayesian (FLAB) algorithm. Maximum diameters of the delineated volumes were compared with the histopathology reference when available. The volumes of the tumors were compared, and correlations between the anatomic volume and PET uptake heterogeneity and the differences between delineations were investigated. Results: All maximum diameters measured on PET and CT images significantly correlated with the histopathology reference (r . 0.89, P , 0.0001). Significant differences were observed among the approaches: CT delineation resulted in large overestimation (132% 6 37%), whereas all delineations on PET images resulted in underestimation (from 215% 6 17% for T 50 to 24% 6 8% for FLAB) except manual delineation (18% 6 17%). Overall, CT volumes were significantly larger than PET volumes (55 6 74 cm 3 for CT vs. from 18 6 25 to 47 6 76 cm 3 for PET). A significant correlation was found between anatomic tumor size and heterogeneity (larger lesions were more heterogeneous). Finally, the more heterogeneous the tumor uptake, the larger was the underestimation of PET volumes by threshold-based techniques. Conclusion: Volumes based on CT images were larger than those based on PET images. Tumor size and tracer uptake heterogeneity have an impact on threshold-based methods, which should not be used for the delineation of cases of large heterogeneous NSCLC, as these methods tend to largely underestimate the spatial extent of the functional tumor in such cases. For an accurate delineation of PET volumes in NSCLC, advanced image segmentation algorithms able to deal with tracer uptake heterogeneity should be preferred. Theuseof 18 F-FDG PET, with the addition of CT since the development of PET/CT devices, has been increasing for staging non-small cell lung cancer (NSCLC) (1). In addition, the use of 18 F-FDG PET/CT in radiotherapy treatment planning for the definition of gross tumor volume has been similarly growing (2). Manual contouring of the tumor boundaries on the CT images is still the conventional methodology for target volume definition. On the other hand, and despite a high spatial resolution, the delineation on CT alone may be biased by insufficient contrast between tumor and healthy tissues (e.g., in cases of atelectasis, pleural effusion, and fibrosis or for tumors attached to the chest wall or mediastinum). Several studies have investigated the impact of...

The objective of this study was to establish the repeatability and reproducibility limits of several volume-related PET imagederived indices-namely tumor volume (TV), mean standardized uptake value, total glycolytic volume (TGV), and total proliferative volume (TPV)-relative to those of maximum standardized uptake value (SUV max ), commonly used in clinical practice. Methods: Fixed and adaptive thresholding, fuzzy C-means, and fuzzy locally adaptive Bayesian methodology were considered for TV delineation. Double-baseline 18 F-FDG (17 lesions, 14 esophageal cancer patients) and 39-deoxy-39-18 F-fluorothymidine ( 18 F-FLT) (12 lesions, 9 breast cancer patients) PET scans, acquired at a mean interval of 4 d and before any treatment, were used for reproducibility evaluation. The repeatability of each method was evaluated for the same datasets and compared with manual delineation. Results: A negligible variability of less than 5% was measured for all segmentation approaches in comparison to manual delineation (5%-35%). SUV max reproducibility levels were similar to others previously reported, with a mean percentage difference of 1.8% 6 16.7% and 20.9% 6 14.9% for the 18 F-FDG and 18 F-FLT lesions, respectively. The best TV, TGV, and TPV reproducibility limits ranged from 221% to 31% and 230% to 37% for 18 F-FDG and 18 F-FLT images, respectively, whereas the worst reproducibility limits ranged from 290% to 73% and 268% to 52%, respectively. Conclusion: The reproducibility of estimating TV, mean standardized uptake value, and derived TGV and TPV was found to vary among segmentation algorithms. Some differences between 18 F-FDG and 18 F-FLT scans were observed, mainly because of differences in overall image quality. The smaller reproducibility limits for volumederived image indices were similar to those for SUV max , suggesting that the use of appropriate delineation tools should allow the determination of tumor functional volumes in PET images in a repeatable and reproducible fashion.

Background:18 F-FDG PET image-derived parameters, such as standardized uptake value (SUV), functional tumor length (TL) and volume (TV) or total lesion glycolysis (TLG) may be useful for determining prognosis in patients with esophageal carcinoma. The objectives of this work were to investigate the prognostic value of these indices in esophageal cancer patients undergoing combined chemoradiotherapy treatment and the impact of TV delineation strategies. Methods: 45 patients were retrospectively analysed. Tumors were delineated on pretreatment 18 F-FDG scans using adaptive threshold and automatic (FLAB) methodologies. SUV max, SUV peak , SUV mean , TL, TV, and TLG were computed. The prognostic value of each parameter for overall survival was investigated using Kaplan-Meier and Cox regression models for univariate and multivariate analyses respectively. Results:Large differences were observed between methodologies (from -140% to +50% for TV). SUV measurements were not significant prognostic factors of overall survival, whereas TV, TL and TLG were, irrespective of the segmentation strategy. After multivariate analysis including standard tumor staging, only TV (p<0.002) and TL (p=0.042) determined using FLAB were independent prognostic factors.Conclusions: Whereas no SUV measurement was a significant prognostic factor, TV, TL and TLG were significant prognostic factors of overall survival, irrespective of the delineation methodology. Only functional tumor volume and length derived using FLAB were independent prognostic factors, highlighting the need for accurate and robust PET tumor delineation tools for oncology applications.3

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography. They lead to a loss of signal in tissues of size similar to the point spread function and induce activity spillover between regions. Although PVE can be corrected for by using algorithms that provide the correct radioactivity concentration in a series of regions of interest (ROIs), so far little attention has been given to the possibility of creating improved images as a result of PVE correction. Potential advantages of PVE-corrected images include the ability to accurately delineate functional volumes as well as improving tumour-to-background ratio, resulting in an associated improvement in the analysis of response to therapy studies and diagnostic examinations, respectively. The objective of our study was therefore to develop a methodology for PVE correction not only to enable the accurate recuperation of activity concentrations, but also to generate PVE-corrected images. In the multiresolution analysis that we define here, details of a high-resolution image H (MRI or CT) are extracted, transformed and integrated in a low-resolution image L (PET or SPECT). A discrete wavelet transform of both H and L images is performed by using the "à trous" algorithm, which allows the spatial frequencies (details, edges, textures) to be obtained easily at a level of resolution common to H and L. A model is then inferred to build the lacking details of L from the high-frequency details in H. The process was successfully tested on synthetic and simulated data, proving the ability to obtain accurately corrected images. Quantitative PVE correction was found to be comparable with a method considered as a reference but limited to ROI analyses. Visual improvement and quantitative correction were also obtained in two examples of clinical images, the first using a combined PET/CT scanner with a lymphoma patient and the second using a FDG brain PET and corresponding T1-weighted MRI in an epileptic patient.