Optimal definition of biological tumor volume using positron emission tomography in an animal model

BACKGROUND: The goal of the study is to investigate (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG-PET)’s ability to delineate the viable portion of a tumor in an animal model using cross-sectional histology as the validation standard. METHODS: Syngeneic mammary tumors were grown in female Lewis rats. Macroscopic histological images of the transverse tumor sections were paired with their corresponding FDG micro-PET slices of the same cranial-caudal location to form 51 pairs of co-registered images. A binary classification system based on four FDG-PET tumor contouring methods was applied to each pair of images: threshold based on (1) percentage of maximum tumor voxel counts (C(max)), (2) percentage of tumor peak voxel counts (C(peak)), (3) multiples of liver mean voxel counts (C(liver)) derived from PERCIST, and (4) an edge-detection-based automated contouring system. The sensitivity, which represented the percentage of viable tumor areas correctly delineated by the gross tumor area (GTA) generated from a particular tumor contouring method, and the ratio (expressed in percentage) of the overestimated areas of a gross tumor area (GTA(OE))/whole tumor areas on the macroscopic histology (WTA(H)), which represented how much a particular GTA extended into the normal structures surrounding the primary tumor target, were calculated. RESULTS: The receiver operating characteristic curves of all pairs of FDG-PET images have a mean area under the curve value of 0.934 (CI of 0.911–0.954), for representing how well each contouring method accurately delineated the viable tumor area. FDG-PET single value threshold tumor contouring based on 30 and 35 % of tumor C(max) or C(peak) and 6 × C(liver) + 2 × SD achieved a sensitivity greater than 90 % with a GTA(OE)/WTA(H) ratio less than 10 %. Contouring based on 50 % of C(max) or C(peak) had a much lower sensitivity of 67.2–75.6 % with a GTA(OE)/WTA(H) ratio of 1.1–1.7 %. Automated edge detection was not reliable in this system. CONCLUSIONS: Single-value-threshold tumor contouring using (18)F-FDG-PET is able to accurately delineate the viable portion of a tumor. 30 and 35 % of C(max), 30 and 35 % of C(peak), and 6 × C(liver) + 2 × SD are three appropriate threshold values to delineate viable tumor volume in our animal model. The commonly used threshold value of 50 % of C(max) or C(peak) failed to detect one third of the viable tumor volume in our model

Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation.: 18F-FDG PET and CT tumor delineation in NSCLC

International audienceUNLABELLED: 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. 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 (+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. 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

Metabolically active volumes automatic delineation methodologies in PET imaging: review and perspectives

International audiencePET imaging is now considered a gold standard tool in clinical oncology, especially for diagnosis purposes. More recent applications such as therapy follow up or tumor targeting in radiotherapy require a fast, accurate and robust metabolically active tumor volumes on emission images, which cannot be obtained through manual contouring. This clinical need has sprung a large number of methodological developments regarding automatic methods to defined tumor volumes on PET images. This paper reviews most of the methodologies that have been recently proposed and discusses their framework and methodological and/or clinical validation. Perspectives regarding the future work to be done are also suggested

Head and neck target delineation using a novel PET automatic segmentation algorithm

Purpose To evaluate the feasibility and impact of using a novel advanced PET auto-segmentation method in Head and Neck (H&N) radiotherapy treatment (RT) planning. Methods ATLAAS, Automatic decision Tree-based Learning Algorithm for Advanced Segmentation, previously developed and validated on pre-clinical data, was applied to 18F-FDG-PET/CT scans of 20 H&N patients undergoing Intensity Modulated Radiation Therapy. Primary Gross Tumour Volumes (GTVs) manually delineated on CT/MRI scans (GTVpCT/MRI), together with ATLAAS-generated contours (GTVpATLAAS) were used to derive the RT planning GTV (GTVpfinal). ATLAAS outlines were compared to CT/MRI and final GTVs qualitatively and quantitatively using a conformity metric. Results The ATLAAS contours were found to be reliable and useful. The volume of GTVpATLAAS was smaller than GTVpCT/MRI in 70% of the cases, with an average conformity index of 0.70. The information provided by ATLAAS was used to grow the GTVpCT/MRI in 10 cases (up to 10.6 mL) and to shrink the GTVpCT/MRI in 7 cases (up to 12.3 mL). ATLAAS provided complementary information to CT/MRI and GTVpATLAAS contributed to up to 33% of the final GTV volume across the patient cohort. Conclusions ATLAAS can deliver operator independent PET segmentation to augment clinical outlining using CT and MRI and could have utility in future clinical studies

Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation.: 18F-FDG PET and CT tumor delineation in NSCLC

International audienceUNLABELLED: 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. 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 (+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. 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

Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma

Purpose: Several methods have been proposed for the segmentation of 18F-FDG uptake in PET. In this study, we assessed the performance of four categories of 18F-FDG PET image segmentation techniques in pharyngolaryngeal squamous cell carcinoma using clinical studies where the surgical specimen served as the benchmark. Methods: Nine PET image segmentation techniques were compared including: five thresholding methods; the level set technique (active contour); the stochastic expectation-maximization approach; fuzzy clustering-based segmentation (FCM); and a variant of FCM, the spatial wavelet-based algorithm (FCM-SW) which incorporates spatial information during the segmentation process, thus allowing the handling of uptake in heterogeneous lesions. These algorithms were evaluated using clinical studies in which the segmentation results were compared to the 3-D biological tumour volume (BTV) defined by histology in PET images of seven patients with T3-T4 laryngeal squamous cell carcinoma who underwent a total laryngectomy. The macroscopic tumour specimens were collected "en bloc”, frozen and cut into 1.7- to 2-mm thick slices, then digitized for use as reference. Results: The clinical results suggested that four of the thresholding methods and expectation-maximization overestimated the average tumour volume, while a contrast-oriented thresholding method, the level set technique and the FCM-SW algorithm underestimated it, with the FCM-SW algorithm providing relatively the highest accuracy in terms of volume determination (−5.9 ± 11.9%) and overlap index. The mean overlap index varied between 0.27 and 0.54 for the different image segmentation techniques. The FCM-SW segmentation technique showed the best compromise in terms of 3-D overlap index and statistical analysis results with values of 0.54 (0.26-0.72) for the overlap index. Conclusion: The BTVs delineated using the FCM-SW segmentation technique were seemingly the most accurate and approximated closely the 3-D BTVs defined using the surgical specimens. Adaptive thresholding techniques need to be calibrated for each PET scanner and acquisition/processing protocol, and should not be used without optimizatio

Optimization of image-guided radiotherapy (IGRT) for lung cancer

Senan, S. [Promotor]Slotman, B.J. [Promotor]Sörnsen De Koste, J.R. van [Copromotor

A Tri-Modality Image Fusion Method for Target Delineation of Brain Tumors in Radiotherapy

Purpose To develop a tri-modality image fusion method for better target delineation in image-guided radiotherapy for patients with brain tumors. Methods A new method of tri-modality image fusion was developed, which can fuse and display all image sets in one panel and one operation. And a feasibility study in gross tumor volume (GTV) delineation using data from three patients with brain tumors was conducted, which included images of simulation CT, MRI, and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) examinations before radiotherapy. Tri-modality image fusion was implemented after image registrations of CT+PET and CT+MRI, and the transparency weight of each modality could be adjusted and set by users. Three radiation oncologists delineated GTVs for all patients using dual-modality (MRI/CT) and tri-modality (MRI/CT/PET) image fusion respectively. Inter-observer variation was assessed by the coefficient of variation (COV), the average distance between surface and centroid (ADSC), and the local standard deviation (SDlocal). Analysis of COV was also performed to evaluate intra-observer volume variation. Results The inter-observer variation analysis showed that, the mean COV was 0.14(±0.09) and 0.07(±0.01) for dual-modality and tri-modality respectively; the standard deviation of ADSC was significantly reduced (p<0.05) with tri-modality; SDlocal averaged over median GTV surface was reduced in patient 2 (from 0.57 cm to 0.39 cm) and patient 3 (from 0.42 cm to 0.36 cm) with the new method. The intra-observer volume variation was also significantly reduced (p = 0.00) with the tri-modality method as compared with using the dual-modality method. Conclusion With the new tri-modality image fusion method smaller inter- and intra-observer variation in GTV definition for the brain tumors can be achieved, which improves the consistency and accuracy for target delineation in individualized radiotherapy