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ABSTRACT: The interest of PET complementary information for the delineation of the target volume in radiotherapy of lung cancer is increasing. However, respiratory motion requires the determination of a functional internal target volume (ITV) on PET images for which several strategies have been proposed. The purpose of this study was the comparison of these strategies for taking into account respiratory motion and deriving the ITV: (1) adding fixed margins to the volume defined on a single binned image, (2) segmenting a motion averaged image and (3) considering the union of volumes delineated on binned frames. For this third strategy, binned frames were either non-corrected for motion, or corrected using two different methods: elastic registration or super resolution. The strategies' performances were assessed on realistic simulated datasets combining the NCAT phantom with a PET Philips GEMINI scanner model in GATE, and containing various configurations of tumor to background contrast, with both regular and irregular respiratory motion (with a range of motion amplitudes). The obtained ITVs' sensitivity (SE) and positive predictive value (PVE) with respect to the known true ITV were significantly higher (from 0.8 to 0.95) than all other techniques when using binned frames corrected for motion, independently of motion regularity, amplitude, or tumor to background contrast. Although the absolute difference was small and not always significant, images corrected using super resolution led to systematically better results than using elastic registration. The worst results were obtained when using the motion averaged image for SE (around 0.5-0.6) and using the margins added to a single frame for PPV (0.6-0.7), respectively. The best strategy to account for breathing motion for tumor ITV delineation in radiotherapy planning is to rely on the use of the union of volumes delineated on super resolution-corrected binned images.
Physics in Medicine and Biology 10/2012; 57(22):7409-7430. · 2.83 Impact Factor
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Journal of Nuclear Medicine 10/2012; · 6.38 Impact Factor
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European Journal of Nuclear Medicine 09/2012; 39(11):1670-2. · 4.53 Impact Factor
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ABSTRACT: Background. The objectives of this study were to investigate the predictive value of sequential (18)F-FDG PET scans for pathological tumor response grade (TRG) after preoperative chemoradiotherapy (PCRT) in locally advanced rectal cancer (LARC) and the impact of partial volume effects correction (PVC). Methods. Twenty-eight LARC patients were included. Responders and non-responders status were determined in histopathology. PET indices [SUV max and mean, volume and total lesion glycolysis (TLG)] at baseline and their evolution after one and two weeks of PCRT were extracted by delineation of the PET images, with or without PVC. Their predictive value was investigated using Mann-Whitney-U tests and ROC analysis. Results. Within baseline parameters, only SUV(mean) was correlated with response. No evolution after one week was predictive of the response, whereas after two weeks all the parameters except volume were, the best prediction being obtained with TLG (AUC 0.79, sensitivity 63%, specificity 92%). PVC had no significant impact on these results. Conclusion. Several PET indices at baseline and their evolution after two weeks of PCRT are good predictors of response in LARC, with or without PVC, whereas results after one week are suboptimal. Best predictor was TLG reduction after two weeks, although baseline SUV(mean) had smaller but similar predictive power.
Acta oncologica (Stockholm, Sweden) 08/2012; · 2.27 Impact Factor
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ABSTRACT: Over the past few years several automatic and semi-automatic PET segmentation methods for target volume definition in radiotherapy have been proposed. The objective of this study is to compare different methods in terms of dosimetry. For such a comparison, a gold standard is needed. For this purpose, realistic GATE-simulated PET images were used. Three lung cases and three H&N cases were designed with various shapes, contrasts and heterogeneities. Four different segmentation approaches were compared: fixed and adaptive thresholds, a fuzzy C-mean and the fuzzy locally adaptive Bayesian method. For each of these target volumes, an IMRT treatment plan was defined. The different algorithms and resulting plans were compared in terms of segmentation errors and ground-truth volume coverage using different metrics (V(95), D(95), homogeneity index and conformity index). The major differences between the threshold-based methods and automatic methods occurred in the most heterogeneous cases. Within the two groups, the major differences occurred for low contrast cases. For homogeneous cases, equivalent ground-truth volume coverage was observed for all methods but for more heterogeneous cases, significantly lower coverage was observed for threshold-based methods. Our study demonstrates that significant dosimetry errors can be avoided by using more advanced image-segmentation methods.
Physics in Medicine and Biology 08/2012; 57(17):5381-97. · 2.83 Impact Factor
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ABSTRACT: Respiratory motion modeling of both tumor and surrounding tissues is a key element in minimizing errors and uncertainties in radiation therapy. Different continuous motion models have been previously developed. However, most of these models are based on the use of parameters such as amplitude and phase extracted from 1D external respiratory signal. A potentially reduced correlation between the internal structures (tumor and healthy organs) and the corresponding external surrogates obtained from such 1D respiratory signal is a limitation of these models. The objective of this work is to describe a continuous patient specific respiratory motion model, accounting for the irregular nature of respiratory signals, using patient external surface information as surrogate measures rather than a 1D respiratory signal.
Ten patients were used in this study having each one 4D CT series, a synchronized RPM signal and patient surfaces extracted from the 4D CT volumes using a threshold based segmentation algorithm. A patient specific model based on the use of principal component analysis was subsequently constructed. This model relates the internal motion described by deformation matrices and the external motion characterized by the amplitude and the phase of the respiratory signal in the case of the RPM or using specific regions of interest (ROI) in the case of the patients' external surface utilization. The capability of the different models considered to handle the irregular nature of respiration was assessed using two repeated 4D CT acquisitions (in two patients) and static CT images acquired at extreme respiration conditions (end of inspiration and expiration) for one patient.
Both quantitative and qualitative parameters covering local and global measures, including an expert observer study, were used to assess and compare the performance of the different motion estimation models considered. Results indicate that using surface information [correlation coefficient (CC): 0.998 ± 0.0006 and model error (ME): 1.35 ± 0.21 mm] is superior to the use of both motion phase and amplitude extracted from a 1D respiratory signal (CC and ME of 0.971 ± 0.02 and 1.64 ± 0.28 mm). The difference in performance was more substantial compared to the use of only one parameter (phase or amplitude) used in the motion model construction. Similarly, the patient surface based model was better in estimating the motion in the repeated 4D CT acquisitions and those CT images acquired at the full inspiration (FI) and the full expiration (FE). Once more, within this context the use of both amplitude and phase in the model building was substantially more robust than the use of phase or amplitude only.
The present study demonstrates the potential of using external patient surfaces for the construction of patient specific respiratory motion models. Such information can be obtained using different devices currently available. The use of external surface information led to the best performance in estimating internal structure motion. On the other hand, the use of both amplitude and phase parameters derived from an 1D respiration signal led to largely superior model performance relative to the use of only one of these two parameters in the model building process.
Medical Physics 06/2012; 39(6):3386-95. · 2.83 Impact Factor
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ABSTRACT: (18)F-FDG PET measurement of standardized uptake value (SUV) is increasingly used for monitoring therapy response and predicting outcome. Alternative parameters computed through textural analysis were recently proposed to quantify the heterogeneity of tracer uptake by tumors as a significant predictor of response. The primary objective of this study was to evaluate the reproducibility of these heterogeneity measurements.
Double baseline (18)F-FDG PET scans were acquired within 4 d of each other for 16 patients before any treatment was considered. A Bland-Altman analysis was performed on 8 parameters based on histogram measurements and 17 parameters based on textural heterogeneity features after discretization with values between 8 and 128.
The reproducibility of maximum and mean SUV was similar to that in previously reported studies, with a mean percentage difference of 4.7% ± 19.5% and 5.5% ± 21.2%, respectively. By comparison, better reproducibility was measured for some textural features describing local heterogeneity of tracer uptake, such as entropy and homogeneity, with a mean percentage difference of -2% ± 5.4% and 1.8% ± 11.5%, respectively. Several regional heterogeneity parameters such as variability in the intensity and size of regions of homogeneous activity distribution had reproducibility similar to that of SUV measurements, with 95% confidence intervals of -22.5% to 3.1% and -1.1% to 23.5%, respectively. These parameters were largely insensitive to the discretization range.
Several parameters derived from textural analysis describing heterogeneity of tracer uptake by tumors on local and regional scales had reproducibility similar to or better than that of simple SUV measurements. These reproducibility results suggest that these (18)F-FDG PET-derived parameters, which have already been shown to have predictive and prognostic value in certain cancer models, may be used to monitor therapy response and predict patient outcome.
Journal of Nuclear Medicine 03/2012; 53(5):693-700. · 6.38 Impact Factor
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ABSTRACT: The objective of this study was to investigate the clinical impact of partial-volume effect (PVE) correction on the predictive and prognostic value of metabolically active tumor volume (MATV) measurements on (18)F-FDG PET baseline scans for therapy response and overall survival in esophageal cancer patients.
Fifty patients with esophageal cancer treated with concomitant radiochemotherapy between 2004 and 2008 were retrospectively considered. PET baseline scans were corrected for PVE with iterative deconvolution incorporating wavelet denoising. MATV delineation on both original and corrected images was performed using the automatic fuzzy locally adaptive Bayesian methodology. Several parameters were extracted considering the original and corrected images: maximum and peak standardized uptake value (SUV), mean SUV, MATV, and total lesion glycolysis (TLG) (TLG = MATV × mean SUV). The predictive value of each parameter with or without correction was investigated using Kruskal-Wallis tests, and the prognostic value was determined with Kaplan-Meier curves.
Whereas PVE correction had a significant quantitative impact on the absolute values of the investigated parameters, their clinical value within the clinical context of interest was not significantly modified-a result that was observed for both overall survival and response to therapy. The hierarchy between parameters was the same before and after correction. SUV measurements (maximum, peak, and mean) had nonsignificant (P > 0.05) predictive or prognostic value, whereas functional tumor-related measurements (MATV and TLG) were significant (P < 0.002) predictors of response and independent prognostic factors.
PVE correction does not improve the predictive and prognostic value of baseline PET image-derived parameters in esophageal cancer patients.
Journal of Nuclear Medicine 01/2012; 53(1):12-20. · 6.38 Impact Factor
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ABSTRACT: The objectives of this study were to investigate the relationship between CT- and (18)F-FDG PET-based tumor volumes in non-small cell lung cancer (NSCLC) and the impact of tumor size and uptake heterogeneity on various approaches to delineating uptake on PET images.
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.
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 (+32% ± 37%), whereas all delineations on PET images resulted in underestimation (from -15% ± 17% for T(50) to -4% ± 8% for FLAB) except manual delineation (+8% ± 17%). Overall, CT volumes were significantly larger than PET volumes (55 ± 74 cm(3) for CT vs. from 18 ± 25 to 47 ± 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.
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.
Journal of Nuclear Medicine 11/2011; 52(11):1690-7. · 6.38 Impact Factor
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ABSTRACT: Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography leading to underestimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multiresolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low-resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model, which may introduce artifacts in regions where no significant correlation exists between anatomical and functional details.
A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method.
Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present, the new model outperformed the 2D global approach, avoiding artifacts and significantly improving quality of the corrected images and their quantitative accuracy.
A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multiresolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information.
Medical Physics 09/2011; 38(9):4920-3. · 2.83 Impact Factor
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ABSTRACT: Current respiratory motion monitoring devices used for motion synchronization in medical imaging and radiotherapy provide either 1D respiratory signal over a specific region or 3D information based on few external or internal markers. On the other hand, newer technology may offer the potential to monitor the entire patient external surface in real time. The main objective of this study was to assess the motion correlation between such an external patient surface and internal anatomical landmarks motion.
Four dimensional computed tomography (4D CT) volumes for ten patients were used in this study. Anatomical landmarks were manually selected in the thoracic region across the 4D CT datasets by two experts. The landmarks included normal structures as well as the tumor location. In addition, a distance map representing the entire external patient surface, which corresponds to surfaces acquired by a time of flight (ToF) camera or similar devices, was created by segmenting the skin of all 4D CT volumes using a thresholding algorithm. Finally, the correlation between the internal landmarks and external surface motion was evaluated for different regions (placement and size) throughout a patient's surface.
Significant variability was observed in the motion of the different parts of the external patient surface. The larger motion magnitude was consistently measured in the central regions of the abdominal and the thoracic areas for the different patient datasets considered. The highest correlation coefficients were observed between the motion of these external surface areas and internal landmarks such as the diaphragm and mediastinum structures as well as the tumor location landmarks (0.8 +/- 0.18 and 0.72 +/- 0.12 for the abdominal and the thoracic regions, respectively). Worse correlation was observed when one considered landmarks not significantly influenced by respiratory motion such as the apex and the sternum.
There were large differences in the motion correlation observed considering different regions of interest placed over a patients' external surface and internal anatomical landmarks. The positioning of current devices used for respiratory motion synchronization may reduce such correlation by averaging the motion over correlated and poorly correlated external regions. The potential of capturing in real-time the motion of the complete external patient surface as well as choosing the area of the surface that correlates best with the internal motion should allow reducing such variability and associated errors in both respiratory motion synchronization and subsequent motion modeling processes.
Medical Physics 06/2011; 38(6):3157-64. · 2.83 Impact Factor
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ABSTRACT: The objectives of this study were to investigate the predictive value of tumour measurements on 2-deoxy-2-[(18)F]fluoro-D-glucose ((18)F-FDG) positron emission tomography (PET) pretreatment scan regarding therapy response in oesophageal cancer and to evaluate the impact of tumour delineation strategies.
Fifty patients with oesophageal cancer treated with concomitant radiochemotherapy between 2004 and 2008 were retrospectively considered and classified as complete, partial or non-responders (including stable and progressive disease) according to Response Evaluation Criteria in Solid Tumors (RECIST). The classification of partial and complete responders was confirmed by biopsy. Tumours were delineated on the (18)F-FDG pretreatment scan using an adaptive threshold and the automatic fuzzy locally adaptive Bayesian (FLAB) methodologies. Several parameters were then extracted: maximum and peak standardized uptake value (SUV), tumour longitudinal length (TL) and volume (TV), SUV(mean), and total lesion glycolysis (TLG = TV × SUV(mean)). The correlation between each parameter and response was investigated using Kruskal-Wallis tests, and receiver-operating characteristic methodology was used to assess performance of the parameters to differentiate patients.
Whereas commonly used parameters such as SUV measurements were not significant predictive factors of the response, parameters related to tumour functional spatial extent (TL, TV, TLG) allowed significant differentiation of all three groups of patients, independently of the delineation strategy, and could identify complete and non-responders with sensitivity above 75% and specificity above 85%. A systematic although not statistically significant trend was observed regarding the hierarchy of the delineation methodologies and the parameters considered, with slightly higher predictive value obtained with FLAB over adaptive thresholding, and TLG over TV and TL.
TLG is a promising predictive factor of concomitant radiochemotherapy response with statistically higher predictive value than SUV measurements in advanced oesophageal cancer.
European Journal of Nuclear Medicine 05/2011; 38(9):1595-606. · 4.53 Impact Factor
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Journal of Nuclear Medicine 03/2011; 52(4):658; author reply 658-9. · 6.38 Impact Factor
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ABSTRACT: (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) image-derived parameters, such as standardized uptake value (SUV), functional tumour length (TL) and tumour volume (TV) or total lesion glycolysis (TLG), may be useful for determining prognosis in patients with oesophageal carcinoma. The objectives of this work were to investigate the prognostic value of these indices in oesophageal cancer patients undergoing combined chemoradiotherapy treatment and the impact of TV delineation strategies.
A total of 45 patients were retrospectively analysed. Tumours were delineated on pretreatment (18)F-FDG scans using adaptive threshold and automatic (fuzzy locally adaptive Bayesian, FLAB) methodologies. The maximum standardized uptake value (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.
Large differences were observed between methodologies (from -140 to +50% for TV). SUV measurements were not significant prognostic factors for overall survival, whereas TV, TL and TLG were, irrespective of the segmentation strategy. After multivariate analysis including standard tumour staging, only TV (p < 0.002) and TL (p = 0.042) determined using FLAB were independent prognostic factors.
Whereas no SUV measurement was a significant prognostic factor, TV, TL and TLG were significant prognostic factors for overall survival, irrespective of the delineation methodology. Only functional TV and TL derived using FLAB were independent prognostic factors, highlighting the need for accurate and robust PET tumour delineation tools for oncology applications.
European Journal of Nuclear Medicine 03/2011; 38(7):1191-202. · 4.53 Impact Factor
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ABSTRACT: (18)F-FDG PET is often used in clinical routine for diagnosis, staging, and response to therapy assessment or prediction. The standardized uptake value (SUV) in the primary or regional area is the most common quantitative measurement derived from PET images used for those purposes. The aim of this study was to propose and evaluate new parameters obtained by textural analysis of baseline PET scans for the prediction of therapy response in esophageal cancer.
Forty-one patients with newly diagnosed esophageal cancer treated with combined radiochemotherapy were included in this study. All patients underwent pretreatment whole-body (18)F-FDG PET. Patients were treated with radiotherapy and alkylatinlike agents (5-fluorouracil-cisplatin or 5-fluorouracil-carboplatin). Patients were classified as nonresponders (progressive or stable disease), partial responders, or complete responders according to the Response Evaluation Criteria in Solid Tumors. Different image-derived indices obtained from the pretreatment PET tumor images were considered. These included usual indices such as maximum SUV, peak SUV, and mean SUV and a total of 38 features (such as entropy, size, and magnitude of local and global heterogeneous and homogeneous tumor regions) extracted from the 5 different textures considered. The capacity of each parameter to classify patients with respect to response to therapy was assessed using the Kruskal-Wallis test (P < 0.05). Specificity and sensitivity (including 95% confidence intervals) for each of the studied parameters were derived using receiver-operating-characteristic curves.
Relationships between pairs of voxels, characterizing local tumor metabolic nonuniformities, were able to significantly differentiate all 3 patient groups (P < 0.0006). Regional measures of tumor characteristics, such as size of nonuniform metabolic regions and corresponding intensity nonuniformities within these regions, were also significant factors for prediction of response to therapy (P = 0.0002). Receiver-operating-characteristic curve analysis showed that tumor textural analysis can provide nonresponder, partial-responder, and complete-responder patient identification with higher sensitivity (76%-92%) than any SUV measurement.
Textural features of tumor metabolic distribution extracted from baseline (18)F-FDG PET images allow for the best stratification of esophageal carcinoma patients in the context of therapy-response prediction.
Journal of Nuclear Medicine 02/2011; 52(3):369-78. · 6.38 Impact Factor
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ABSTRACT: Current state-of-the-art algorithms for functional uptake volume segmentation in PET imaging consist of threshold-based approaches, whose parameters often require specific optimization for a given scanner and associated reconstruction algorithms. Different advanced image segmentation approaches previously proposed and extensively validated, such as among others fuzzy C-means (FCM) clustering, or fuzzy locally adaptive bayesian (FLAB) algorithm have the potential to improve the robustness of functional uptake volume measurements. The objective of this study was to investigate robustness and repeatability with respect to various scanner models, reconstruction algorithms and acquisition conditions.
Robustness was evaluated using a series of IEC phantom acquisitions carried out on different PET/CT scanners (Philips Gemini and Gemini Time-of-Flight, Siemens Biograph and GE Discovery LS) with their associated reconstruction algorithms (RAMLA, TF MLEM, OSEM). A range of acquisition parameters (contrast, duration) and reconstruction parameters (voxel size) were considered for each scanner model, and the repeatability of each method was evaluated on simulated and clinical tumours and compared to manual delineation.
For all the scanner models, acquisition parameters and reconstruction algorithms considered, the FLAB algorithm demonstrated higher robustness in delineation of the spheres with low mean errors (10%) and variability (5%), with respect to threshold-based methodologies and FCM. The repeatability provided by all segmentation algorithms considered was very high with a negligible variability of <5% in comparison to that associated with manual delineation (5-35%).
The use of advanced image segmentation algorithms may not only allow high accuracy as previously demonstrated, but also provide a robust and repeatable tool to aid physicians as an initial guess in determining functional volumes in PET.
European Journal of Nuclear Medicine 01/2011; 38(4):663-72. · 4.53 Impact Factor
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International journal of radiation oncology, biology, physics 12/2010; 78(5):1605. · 4.59 Impact Factor
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ABSTRACT: The objective of this study was to establish the repeatability and reproducibility limits of several volume-related PET image-derived 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.
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 3'-deoxy-3'-(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.
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% +/- 16.7% and -0.9% +/- 14.9% for the (18)F-FDG and (18)F-FLT lesions, respectively. The best TV, TGV, and TPV reproducibility limits ranged from -21% to 31% and -30% to 37% for (18)F-FDG and (18)F-FLT images, respectively, whereas the worst reproducibility limits ranged from -90% to 73% and -68% to 52%, respectively.
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 volume-derived 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.
Journal of Nuclear Medicine 09/2010; 51(9):1368-76. · 6.38 Impact Factor
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ABSTRACT: Accurate contouring of positron emission tomography (PET) functional volumes is now considered crucial in image-guided radiotherapy and other oncology applications because the use of functional imaging allows for biological target definition. In addition, the definition of variable uptake regions within the tumor itself may facilitate dose painting for dosimetry optimization.
Current state-of-the-art algorithms for functional volume segmentation use adaptive thresholding. We developed an approach called fuzzy locally adaptive Bayesian (FLAB), validated on homogeneous objects, and then improved it by allowing the use of up to three tumor classes for the delineation of inhomogeneous tumors (3-FLAB). Simulated and real tumors with histology data containing homogeneous and heterogeneous activity distributions were used to assess the algorithm's accuracy.
The new 3-FLAB algorithm is able to extract the overall tumor from the background tissues and delineate variable uptake regions within the tumors, with higher accuracy and robustness compared with adaptive threshold (T(bckg)) and fuzzy C-means (FCM). 3-FLAB performed with a mean classification error of less than 9% +/- 8% on the simulated tumors, whereas binary-only implementation led to errors of 15% +/- 11%. T(bckg) and FCM led to mean errors of 20% +/- 12% and 17% +/- 14%, respectively. 3-FLAB also led to more robust estimation of the maximum diameters of tumors with histology measurements, with <6% standard deviation, whereas binary FLAB, T(bckg) and FCM lead to 10%, 12%, and 13%, respectively.
These encouraging results warrant further investigation in future studies that will investigate the impact of 3-FLAB in radiotherapy treatment planning, diagnosis, and therapy response evaluation.
International journal of radiation oncology, biology, physics 05/2010; 77(1):301-8. · 4.59 Impact Factor
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Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009; 01/2009
