Tumor Volume Segmentation Research Articles

Impact of PET and MRI threshold-based tumor volume segmentation on patient-specific targeted radionuclide therapy dosimetry using CLR1404

Variations in tumor volume segmentation methods in targeted radionuclide therapy (TRT) may lead to dosimetric uncertainties. This work investigates the impact of PET and MRI threshold-based tumor segmentation on TRT dosimetry in patients with primary and metastatic brain tumors. In this study, PET/CT images of five brain cancer patients were acquired at 6, 24, and 48 h post-injection of 124I-CLR1404. The tumor volume was segmented using two standardized uptake value (SUV) threshold levels, two tumor-to-background ratio (TBR) threshold levels, and a T1 Gadolinium-enhanced MRI threshold. The dice similarity coefficient (DSC), jaccard similarity coefficient (JSC), and overlap volume (OV) metrics were calculated to compare differences in the MRI and PET contours. The therapeutic 131I-CLR1404 voxel-level dose distribution was calculated from the 124I-CLR1404 activity distribution using RAPID, a Geant4 Monte Carlo internal dosimetry platform. The TBR, SUV, and MRI tumor volumes ranged from 2.3–63.9 cc, 0.1–34.7 cc, and 0.4–11.8 cc, respectively. The average ± standard deviation (range) was 0.19 ± 0.13 (0.01–0.51), 0.30 ± 0.17 (0.03–0.67), and 0.75 ± 0.29 (0.05–1.00) for the JSC, DSC, and OV, respectively. The DSC and JSC values were small and the OV values were large for both the MRI-SUV and MRI-TBR combinations because the regions of PET uptake were generally larger than the MRI enhancement. Notable differences in the tumor dose volume histograms were observed for each patient. The mean (standard deviation) 131I-CLR1404 tumor doses ranged from 0.28–1.75 Gy GBq−1 (0.07–0.37 Gy GBq−1). The ratio of maximum-to-minimum mean doses for each patient ranged from 1.4–2.0. The tumor volume and the interpretation of the tumor dose is highly sensitive to the imaging modality, PET enhancement metric, and threshold level used for tumor volume segmentation. The large variations in tumor doses clearly demonstrate the need for standard protocols for multimodality tumor segmentation in TRT dosimetry.

Volumetric analysis of MR images for glioma classification and their effect on brain tissues

This work aims to identify non-invasive quantitative parameters from three-dimensional brain magnetic resonance images in order: (1) to classify brain tumor (glioma) as low grade (LG) or high grade (HG) and (2) to analyze effect of tumor on brain gray matter (GM) and white matter (WM). In proposed model features were extracted from segmented tumor region based on its volume and shape for distinguishing the tumor grade. Statistical analysis revealed good correlation between segmented tumor volume and tumor grade. Various morphological parameters extracted from segmented tumor region were also significantly different for LG and HG cases ( $$p<0.05$$ ). Also, for brain tumor patients a considerable variation in normalized GM (%GM) volume was obtained compared to normalized WM (%WM) volume for LG and HG cases. We also found that, relative to controls, there was higher effect on %GM and %WM volumes for HG glioma patients as compared to LG glioma patients. The experimental results show that proposed feature set achieves LG/HG classification with high accuracy using support vector machines classifier.

11C-Methionine positron emission tomography delineates non-contrast enhancing tumor regions at high risk for recurrence in pediatric high-grade glioma.

We assessed the prognostic utility of 11C-Methionine positron emission tomography (MET-PET) in pediatric high-grade glioma (HGG). Thirty-one children had 62 MET-PET studies. Segmented tumor volumes from co-registered magnetic resonance studies were assessed for concordance with MET-PET uptake using Boolean operations. The tumor volume at diagnosis and treatment failure was assessed relative to MET-PET avid volume. The prognostic impact of MET-PET-delineated non-contrast enhancing tumor (NCET) was assessed. NCET was defined as the region of tumor defined by defined by FLAIR which did not enhance but showed MET-PET avidity. MET-PET concordance varied according to magnetic resonance sequence. MET-PET rarely added to the tumor volume in most cases. The volume of MET-PET with standardized uptake value >3.0 was differentially distributed at diagnosis, post treatment, and at recurrence. The initial MET-PET region overlapped with recurrent tumor in 90% of the cases. When the proportion of tumor which was NCET was >10%, an earlier time to progression (5.8months; 95% CI, 1-8.2 vs. 10.5months; 95% CI, 0.9-NR; p = 0.035) was noted. MET-PET delineates regions at increased risk for recurrence and may improve the definition of failure, prognostic assessment, and target definition for radiotherapy.

Dual Energy CT- Surrogate Quantitative Marker of Response to Targetted treatment in HCC

Background and Aim: Dual energy CT (DECT)– novel CT imaging technique at two different energies, can be used to discriminate between different materials (e.g., fat, calcium, iodine, and water), which are not allowed by conventional CT. The images reconstructed from these differential data, including iodine concentration maps, may provide diagnostic information beyond that obtainable with conventional CT Aim: To evaluate the supplementary value of dual energy CT to assess response in cirrhotic patients with HCC following targeted therapy. Methods: 20 patients with targeted therapies for dual energy CT (TACE/TARE/RFA) were evaluated. Iodine uptake(IU), derived from the iodine-based material-decomposition image and the iodine concentration ratio on the dual energy CT acquired during early portal venous phase was calculated by drawing regions of interest within the treated lesion, at the edge and within the surrounding liver parenchyma. Quantification of IU (absolute value for total IU (mg) and volume-normalized IU (mg/ml)) was based on semi-automatic tumor volume segmentation compared to the background liver. The decrease compared with baseline was calculated. Results: The mean normalized IU per patient within the treated lesion compared to the cirrhotic liver parenchyma -31%. Total IU per patient, combining both normalized IU and volume, showed the most pronounced decrease. Conclusion: DECT enables objective, easy and fast parameterization of tumor size and contrast medium uptake. DECT provides a quantitative method with which to analyze the treated lesions in the cirrhotic liver. The authors have none to declare.

Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma.

The purpose of this study was to determine the relevance of clinical data, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC) perfusion and the volume transfer constant (ktrans) from dynamic contrast-enhanced (DCE) perfusion for predicting overall survival (OS) and progression-free survival (PFS) in newly diagnosed treatment-naïve glioblastoma patients. Preoperative MR scans including standardized contrast-enhanced T1 (cT1), T2 - fluid-attenuated inversion recovery (FLAIR), ADC, DSC, and DCE of 125 patients with subsequent histopathologically confirmed glioblastoma were performed on a 3 Tesla MRI scanner. ADC, DSC, and DCE parameters were analyzed in semiautomatically segmented tumor volumes on contrast-enhanced (CE) cT1 and hyperintense signal changes on T2 FLAIR (ED). Univariate and multivariable Cox regression analyses including age, sex, extent of resection (EOR), and KPS were performed to assess the influence of each parameter on OS and PFS. Univariate Cox regression analysis demonstrated a significant association of age, KPS, and EOR with PFS and age, KPS, EOR, lower ADC, and higher rCBV with OS. Multivariable analysis showed independent significance of male sex, KPS, EOR, and increased rCBVCE for PFS, and age, sex, KPS, and EOR for OS. MRI parameters help to predict OS in a univariate Cox regression analysis, and increased rCBVCE is associated with shorter PFS in the multivariable model. In summary, however, our findings suggest that the relevance of MRI parameters is outperformed by clinical parameters in a multivariable analysis, which limits their prognostic value for survival prediction at the time of initial diagnosis.

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.

Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell carcinoma). Furthermore, in order to predict histologic subtypes, we employed machine-learning methods and independently evaluated their prediction performance. Two independent radiomic cohorts with a combined size of 350 patients were included in our analysis. A total of 440 radiomic features were extracted from the segmented tumor volumes of pretreatment CT images. These radiomic features quantify tumor phenotypic characteristics on medical images using tumor shape and size, intensity statistics, and texture. Univariate analysis was performed to assess each feature's association with the histological subtypes. In our multivariate analysis, we investigated 24 feature selection methods and 3 classification methods for histology prediction. Multivariate models were trained on the training cohort and their performance was evaluated on the independent validation cohort using the area under ROC curve (AUC). Histology was determined from surgical specimen. In our univariate analysis, we observed that fifty-three radiomic features were significantly associated with tumor histology. In multivariate analysis, feature selection methods ReliefF and its variants showed higher prediction accuracy as compared to other methods. We found that Naive Baye's classifier outperforms other classifiers and achieved the highest AUC (0.72; p-value = 2.3 × 10(-7)) with five features: Stats_min, Wavelet_HLL_rlgl_lowGrayLevelRunEmphasis, Wavelet_HHL_stats_median, Wavelet_HLL_stats_skewness, and Wavelet_HLH_glcm_clusShade. Histological subtypes can influence the choice of a treatment/therapy for lung cancer patients. We observed that radiomic features show significant association with the lung tumor histology. Moreover, radiomics-based multivariate classifiers were independently validated for the prediction of histological subtypes. Despite achieving lower than optimal prediction accuracy (AUC 0.72), our analysis highlights the impressive potential of non-invasive and cost-effective radiomics for precision medicine. Further research in this direction could lead us to optimal performance and therefore to clinical applicability, which could enhance the efficiency and efficacy of cancer care.

Abstract B02: Ultrasound evaluation of anti-angiogenic therapy on patient-derived renal cell carcinoma xenograft tumors in the chicken embryo model

Abstract Introduction: The avian embryo patient-derived xenograft (PDX) model's use as a platform for pre-clinical drug testing is a promising application. However, conventional methods of monitoring, such as tumor-take rates, light microscopy, and histology, either do not provide sufficient detail for in-depth therapy evaluation or are reserved for end-point analysis. Ultrasound monitoring permits non-destructive longitudinal evaluation of tumor growth, progression, and angiogenesis in the chorioallantoic membrane (CAM) xenograft ex ovo model. High-frequency ultrasound was selected for its low cost, lack of ionizing radiation, and high spatial and temporal resolutions. Methods: A subset of tumor initiating cells (RCC243) was isolated from a patient-derived parental renal carcinoma cell line (RCC22). Cells were grown to confluence, pelleted, and combined with an equal volume of Matrigel. On the ninth day of embryonic development (EDD-9), the CAM surface of 8 animals was pierced, and 10µL of the cell-Matrigel mix was deposited into the opening. Half of the embryos were treated every two days with 10µL of TAK-441, a Hedgehog inhibitor with hypothesized anti-angiogenic effects. Three-dimensional anatomical (B-mode) and contrast-enhanced images were acquired using a Vevo 2100 ultrasound system (VisualSonics Inc.) equipped with a 20 MHz linear array transducer. On EDD-18, tumor volumes were assessed using the B-mode images. The CAM vasculature was then cannulated with a glass capillary needle, and a 50µL solution of Vevo MicroMarker (VisualSonics Inc.) microbubble contrast agent (2 x 109 microbubbles/mL) was injected in a pulsatile manner. Tumor blood perfusion (blood volume, velocity, and flow) was then assessed using destruction-reperfusion contrast ultrasound. Image analysis was performed within manually segmented tumor volumes. Results: Hedgehog inhibition of RCC243 tumors via TAK-441 therapy produced a significant decrease in mean tumor volume (vehicle: 187.68 ± 69.55 mm3 vs. treatment: 78.94 ± 52.35 mm3; p &amp;lt; 0.05) and blood flow (vehicle: 645.7 ± 261.5 mm3/min vs. treatment: 190.8 ± 133.4 mm3/min; p &amp;lt; 0.05). There was a non-significant trend of reduced blood volume (vehicle: 97.0 ± 64.7 mm3 vs. treatment: 33.7 ± 23.8 mm3; p = 0.12), and flow velocity (vehicle: 6.34 ± 1.07 mm/s vs. treatment: 5.52 ± 1.18 mm/s; p = 0.34) in the treated tumors. Conclusions: This proof of principal study shows that tumors implanted in an ex ovo chick CAM model can successfully be imaged using high-frequency ultrasound and quantitative measures of tumor volume, blood volume, velocity and flow can be obtained. Responses to anti-angiogenic therapy can be successfully quantified. This approach to therapy evaluation is inexpensive and permits observation of tumor-induced angiogenesis over a 9 day timeframe. Citation Format: Matthew R. Lowerison, Chantalle J. Willie, Siddika Pardhan, Nicholas E. Power, Ann F. Chambers, Hon S. Leong, James C. Lacefield. Ultrasound evaluation of anti-angiogenic therapy on patient-derived renal cell carcinoma xenograft tumors in the chicken embryo model. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Angiogenesis and Vascular Normalization: Bench to Bedside to Biomarkers; Mar 5-8, 2015; Orlando, FL. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl):Abstract nr B02.

Impact of CT attenuation correction method on quantitative respiratory-correlated (4D) PET/CT imaging.

Respiratory-correlated positron emission tomography (PET/CT) 4D PET/CT is used to mitigate errors from respiratory motion; however, the optimal CT attenuation correction (CTAC) method for 4D PET/CT is unknown. The authors performed a phantom study to evaluate the quantitative performance of CTAC methods for 4D PET/CT in the ground truth setting. A programmable respiratory motion phantom with a custom movable insert designed to emulate a lung lesion and lung tissue was used for this study. The insert was driven by one of five waveforms: two sinusoidal waveforms or three patient-specific respiratory waveforms. 3DPET and 4DPET images of the phantom under motion were acquired and reconstructed with six CTAC methods: helical breath-hold (3DHEL), helical free-breathing (3DMOT), 4D phase-averaged (4DAVG), 4D maximum intensity projection (4DMIP), 4D phase-matched (4DMATCH), and 4D end-exhale (4DEXH) CTAC. Recovery of SUV(max), SUV(mean), SUV(peak), and segmented tumor volume was evaluated as RC(max), RC(mean), RC(peak), and RC(vol), representing percent difference relative to the static ground truth case. Paired Wilcoxon tests and Kruskal-Wallis ANOVA were used to test for significant differences. For 4DPET imaging, the maximum intensity projection CTAC produced significantly more accurate recovery coefficients than all other CTAC methods (p < 0.0001 over all metrics). Over all motion waveforms, ratios of 4DMIP CTAC recovery were 0.2 ± 5.4, -1.8 ± 6.5, -3.2 ± 5.0, and 3.0 ± 5.9 for RC(max), RC(peak), RC(mean), and RC(vol). In comparison, recovery coefficients for phase-matched CTAC were -8.4 ± 5.3, -10.5 ± 6.2, -7.6 ± 5.0, and -13.0 ± 7.7 for RC(max), RC(peak), RC(mean), and RC(vol). When testing differences between phases over all CTAC methods and waveforms, end-exhale phases were significantly more accurate (p = 0.005). However, these differences were driven by the patient-specific respiratory waveforms; when testing patient and sinusoidal waveforms separately, patient waveforms were significantly different between phases (p < 0.0001) while the sinusoidal waveforms were not significantly different (p = 0.98). When considering only the subset of 4DMATCH images that corresponded to the end-exhale image phase, 4DEXH, mean and interquartile range were similar to 4DMATCH but variability was considerably reduced. Comparative advantages in accuracy and precision of SUV metrics and segmented volumes were demonstrated with the use of the maximum intensity projection and end-exhale CT attenuation correction. While respiratory phase-matched CTAC should in theory provide optimal corrections, image artifacts and differences in implementation of 4DCT and 4DPET sorting can degrade the benefit of this approach. These results may be useful to guide the implementation, analysis, and development of respiratory-correlated thoracic PET/CT in the radiation oncology and diagnostic settings.

A new method for segmentation of FDG PET metabolic tumour volume using the peritumoural halo layer and a 10-step colour scale. A study in patients with papillary thyroid carcinoma.

We observed a layer between tumour activity and background on FDG PET/CT with the 10-step colour scale and the window level set properly. We named the layer peritumoral halo layer (PHL). We performed this study to establish the reliability of metabolic tumor volume (MTV) segmentation using PHL (MTV(PHL)) in patients with papillary thyroid carcinoma. Of a total of 140 papillary thyroid carcinoma (PTC) patients, 70 (50.0%) had FDG-avid PTC. In these patients, MTV(PHL), MTV segmented according to fixed 50% SUVmax (MTV(50%)), and fixed SUV with 2.5 to 4.0 (MTV(2.5) to MTV(4.0)) were compared with pathologic tumour volume (PTV). The absolute percentage difference between MTV(PHL) and PTV was compared in micropapillary carcinoma (MPTC) and non-micropapillary carcinoma (non-MPTC) subgroups. The % SUVmax and SUV thresholds of MTV(PHL) were compared with tumour SUVmax. Among the MTVs, MTV(50%) was not correlated with PTV (r = -0.16, p = 0.182) and was not reliable according to the Bland-Altman plot. Although MTV(2.5), MTV(3.0), MTV(3.5), and MTV(4.0) correlated with PTV (r = 0.85, 0.86, 0.87, and 0.87, respectively; p < 0.001), these MTVs were not reliable on Bland-Altman analyses. MTV(PHL) was significantly correlated with PTV (r = 0.80, p < 0.001), and the Bland-Altman plot did not show systemic error. The MTV(PHL) was more accurate in non-MPTC than in MPTC (p < 0.001), and the absolute % difference was smaller as PTV became larger (σ = -0.65, p < 0.001). The MTV(PHL) thresholds had correlations with SUVmax (% SUVmax threshold: σ = -0.87, p < 0.001; SUV threshold: r = 0.88, p < 0.001). MTV(PHL) was more reliable than MTV(%SUVmax) or MTV(SUV). The reliability of MTV(PHL) improved with larger PTVs. The threshold of the MTV(PHL) was naturally altered by PHL according to SUVmax.

Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast.

Contrast-enhanced T1-weighted imaging is usually included in MRI procedures for automatic tumor segmentation. Use of an MR contrast agent may not be appropriate for some applications, however. We assessed the feasability of automatic tumor segmentation by multiparametric cluster analysis that uses intrinsic MRI contrast only. Multiparametric MRI consisting of quantitative T1, T2, and apparent diffusion coefficient (ADC) mapping was performed in mice bearing subcutaneous tumors (n = 21). k-means and fuzzy c-means clustering with all possible combinations of MRI parameters, i.e. feature vectors, and 2-7 clusters were performed on the multiparametric data. Clusters associated with tumor tissue were selected on the basis of the relative signal intensity of tumor tissue in T2-weighted images. The optimum segmentation method was determined by quantitative comparison of automatic segmentation with manual segmentation performed by three observers. In addition, the automatically segmented tumor volumes from seven separate tumor data sets were quantitatively compared with histology-derived tumor volumes. The highest similarity index between manual and automatic segmentation (SI manual,automatic = 0.82 ± 0.06) was observed for k-means clustering with feature vector {T2, ADC} and four clusters. A strong linear correlation between automatically and manually segmented tumor volumes (R (2) = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R (2) = 0.96). Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.

Adaptive threshold segmentation of pituitary adenomas from FDG PET images for radiosurgery.

In this study we have attempted to optimize a PET based adaptive threshold segmentation method for delineating small tumors, particularly in a background of high tracer activity. The metabolic nature of pituitary adenomas and the constraints of MRI imaging in the postoperative setting to delineate these tumors during radiosurgical procedures motivated us to develop this method. Phantom experiments were done to establish a relationship between the threshold required for segmenting the PET images and the target size and the activity concentration within the target in relation to its background. The threshold was developed from multiple linear regression of the experimental data optimized for tumor sizes less than 4 cm3. We validated our method against the phantom target volumes with measured target to background ratios ranging from 1.6 to 14.58. The method was tested on ten retrospective patients with residual growth hormone‐secreting pituitary adenomas that underwent radiosurgery and compared against the volumes delineated by manual method. The predicted volumes against the true volume of the phantom inserts gave a correlation coefficient of 99% (p<0.01). In the ten retrospective patients, the automatically segmented tumor volumes against volumes manually delineated by the clinicians had a correlation of 94% (p<0.01). This adaptive threshold segmentation showed promising results in delineating tumor volumes in pituitary adenomas planned for stereotactic radiosurgery, particularly in the postoperative setting where MR and CT images may be associated with artifacts, provided optimization experiment is carried out.PACS number: 87.57.nm, 87.57.uk

Robust Radiomics feature quantification using semiautomatic volumetric segmentation.

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.

SU‐E‐J‐182: A Feasibility Study Evaluating Automatic Identification of Gross Tumor Volume for Breast Cancer Radiotherapy Using Dynamic Contrast‐Enhanced MR Imaging

Purpose:To develop a computerized pharmacokinetic model‐free Gross Tumor Volume (GTV) segmentation method based on dynamic contrastenhanced MRI (DCE‐MRI) data that can improve physician GTV contouring efficiency.Methods:12 patients with biopsy‐proven early stage breast cancer with post‐contrast enhanced DCE‐MRI images were analyzed in this study. A fuzzy c‐means (FCM) clustering‐based method was applied to segment 3D GTV from pre‐operative DCE‐MRI data. A region of interest (ROI) is selected by a clinician/physicist, and the normalized signal evolution curves were calculated by dividing the signal intensity enhancement value at each voxel by the pre‐contrast signal intensity value at the corresponding voxel. Three semi‐quantitative metrics were analyzed based on normalized signal evolution curves: initial Area Under signal evolution Curve (iAUC), Immediate Enhancement Ratio (IER), and Variance of Enhancement Slope (VES). The FCM algorithm wass applied to partition ROI voxels into GTV voxels and non‐GTV voxels by using three analyzed metrics. The partition map for the smaller cluster is then generated and binarized with an automatically calculated threshold. To reduce spurious structures resulting from background, a labeling operation was performed to keep the largest three‐dimensional connected component as the identified target. Basic morphological operations including hole‐filling and spur removal were useutilized to improve the target smoothness. Each segmented GTV was compared to that drawn by experienced radiation oncologists. An agreement index was proposed to quantify the overlap between the GTVs identified using two approaches and a thershold value of 0.4 is regarded as acceptable.Results:The GTVs identified by the proposed method were overlapped with the ones drawn by radiation oncologists in all cases, and in 10 out of 12 cases, the agreement indices were above the threshold of 0.4.Conclusion:The proposed automatic segmentation method was shown to be promising and might be used to improve physician contouring efficiency.J Horton receives grant from NIH and Varian Medical Systems; F‐F Yin receives grant from Varian Medical Systems

FDG PET metabolic tumor volume segmentation and pathologic volume of primary human solid tumors.

The purpose of this study was to establish the correlation and reliability among the pathologic tumor volume and gradient and fixed threshold segmentations of (18)F-FDG PET metabolic tumor volume of human solid tumors. There were 52 patients included in the study who had undergone baseline PET/CT with subsequent resection of head and neck, lung, and colorectal tumors. The pathologic volume was calculated from three dimensions of the gross tumor specimen as a reference standard. The primary tumor metabolic tumor volume was segmented using gradient and 30%, 40%, and 50% maximum standardized uptake value (SUVmax) threshold methods. Pearson correlation coefficient, intraclass correlation coefficient, and Bland-Altman analyses were performed to establish the correlation and reliability among the pathologic volume and segmented metabolic tumor volume. The mean pathologic volume; gradient-based metabolic tumor volume; and 30%, 40%, and 50% SUVmax threshold metabolic tumor volumes were 13.46, 13.75, 15.47, 10.63, and 7.57 mL, respectively. The intraclass correlation coefficients among the pathologic volume and the gradient-based and 30%, 40%, and 50% SUVmax threshold metabolic tumor volumes were 0.95, 0.85, 0.80, and 0.76, respectively. The Bland-Altman biases were -0.3, -2.0, 2.82, and 5.9 mL, respectively. Of the small tumors (< 10 mL), 23 of the 35 patients had PET segmented volume outside 50% of the pathologic volume, and among the large tumors (≥ 10 mL) three of the 17 patients had PET segmented volumes that were outside 50% of pathologic volume. FDG PET metabolic tumor volume estimated using gradient segmentation had superior correlation and reliability with the estimated ellipsoid pathologic volume of the tumors compared with threshold method segmentation.

Interreader Agreement and Variability of FDG PET Volumetric Parameters in Human Solid Tumors

The purpose of this article is to evaluate the interreader agreement and variability of two (18)F-FDG PET parameters, metabolic tumor volume and total lesion glycolysis, in human solid tumors. One hundred eleven patients (mean [± SD] age, 61.9 ± 12.5 years) with baseline staging FDG PET/CT scans were included. Two readers independently read the scans and segmented metabolic tumor volume and total lesion glycolysis using two fixed thresholds, 40% and 50% of the lesion's maximum standardized uptake value (SUVmax). The impact of the lesion's FDG avidity and location on reader agreement and variability was established. Intraclass correlation coefficient (ICC), precision, and Bland-Altman analysis were used to evaluate agreement and variability. The ICCs for 40% and 50% SUVmax segmentations of metabolic tumor volume between the readers were 0.987 and 0.995, and the corresponding values for 40% and 50% SUVmax segmentations of total lesion glycolysis were 0.987 and 0.986, respectively (p = 0.0001). The corresponding precisions were 0.5%, 0.2%, 0.5%, and 0.5%, respectively. The mean biases between the readers for 40% and 50% SUVmax segmentations of metabolic tumor volume were -1.78 ± 8.42 mL and -0.46 ± 2.1 mL and for 40% and 50% SUVmax segmentations of total lesion glycolysis were -7.3 ± 31.6 g and -2.97 ± 12.86 g, respectively. Subgroup analysis showed better precision and lesser variability for 50% SUVmax segmentations of metabolic tumor volume and total lesion glycolysis in patients with the highest and lowest FDG-avid primary tumors. The precision was highest and variability was lowest for lung tumors. There is excellent interreader agreement for measurement of metabolic tumor volume and total lesion glycolysis with 40% and 50% SUVmax threshold segmentations in human solid tumors.

Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome

Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer (18)F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV) before and during (chemo)radiotherapy were compared and related to clinical outcome. The study group comprised 46 patients with 48 squamous cell carcinomas of the head and neck, treated with accelerated (chemo)radiotherapy, who underwent FLT PET/CT prior to treatment and in the 2nd and 4th week of therapy. Primary gross tumour volumes were visually delineated on CT images (GTV CT). PVs were visually determined on all PET scans (PV VIS). The following semiautomatic segmentation methods were applied to sequential PET scans: background-subtracted relative-threshold level (PV RTL), a gradient-based method using the watershed transform algorithm and hierarchical clustering analysis (PV W&C), and a fuzzy locally adaptive Bayesian algorithm (PV FLAB). Pretreatment PV VIS correlated best with PV FLAB and GTV CT. Correlations with PV RTL and PV W&C were weaker although statistically significant. During treatment, the PV VIS, PV W&C and PV FLAB significant decreased over time with the steepest decline over time for PV FLAB. Among these advanced segmentation methods, PV FLAB was the most robust in segmenting volumes in the third scan (67 % of tumours as compared to 40 % for PV W&C and 27 % for PV RTL). A decrease in PV FLAB above the median between the pretreatment scan and the scan obtained in the 4th week was associated with better disease-free survival (4 years 90 % versus 53 %). In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may potentially facilitate radiation dose adaptation to changing PV.

A Comparison of Amplitude-Based and Phase-Based Positron Emission Tomography Gating Algorithms for Segmentation of Internal Target Volumes of Tumors Subject to Respiratory Motion

To quantitatively compare the accuracy of tumor volume segmentation in amplitude-based and phase-based respiratory gating algorithms in respiratory-correlated positron emission tomography (PET). List-mode fluorodeoxyglucose-PET data was acquired for 10 patients with a total of 12 fluorodeoxyglucose-avid tumors and 9 lymph nodes. Additionally, a phantom experiment was performed in which 4 plastic butyrate spheres with inner diameters ranging from 1 to 4 cm were imaged as they underwent 1-dimensional motion based on 2 measured patient breathing trajectories. PET list-mode data were gated into 8 bins using 2 amplitude-based (equal amplitude bins [A1] and equal counts per bin [A2]) and 2 temporal phase-based gating algorithms. Gated images were segmented using a commercially available gradient-based technique and a fixed 40% threshold of maximum uptake. Internal target volumes (ITVs) were generated by taking the union of all 8 contours per gated image. Segmented phantom ITVs were compared with their respective ground-truth ITVs, defined as the volume subtended by the tumor model positions covering 99% of breathing amplitude. Superior-inferior distances between sphere centroids in the end-inhale and end-exhale phases were also calculated. Tumor ITVs from amplitude-based methods were significantly larger than those from temporal-based techniques (P=.002). For lymph nodes, A2 resulted in ITVs that were significantly larger than either of the temporal-based techniques (P<.0323). A1 produced the largest and most accurate ITVs for spheres with diameters of ≥2 cm (P=.002). No significant difference was shown between algorithms in the 1-cm sphere data set. For phantom spheres, amplitude-based methods recovered an average of 9.5% more motion displacement than temporal-based methods under regular breathing conditions and an average of 45.7% more in the presence of baseline drift (P<.001). Target volumes in images generated from amplitude-based gating are larger and more accurate, at levels that are potentially clinically significant, compared with those from temporal phase-based gating.

TU‐G‐141‐08: Impact of Audiovisual Biofeedback Respiratory Training On 4D‐PET Image Quality

Purpose: To test the hypothesis that audiovisual biofeedback (AV) respiratory training improves 4D‐PET image quality. Methods: In an IRB‐approved study, two 4D‐PET scans, with AV and free breathing, were acquired sequentially for 10 lung cancer patients. AV was performed by guiding a patient to follow a waveform with period and displacement determined by averaging the respiratory cycles of the patient. The acquisition time for the second scan was increased to compensate for activity decaying. 4D‐PET images were created by phase‐based gating with six bins. The 4D‐PET images with AV were compared to those with free breathing by quantifying (1) SUVmax and (2) tumor motion range. The motion range was quantified through the center of mass of a segmented tumor volume at each phase. To investigate the correlation of respiratory regularity and SUVmax, we separated tumors into two groups: (1) tumors with large motion (&gt;5 mm, spatial resolution) and (2) the others. We tested the hypothesis using the two‐tailed paired t‐test. Results: For tumors with large motion, the average SUVmax with AV and free breathing was 11.5 and 10.8, respectively (p = 0.03); for the other tumors, the average SUVmax with AV and free breathing was 14.4 and 12.4, respectively (p &lt; 0.001). For tumors with large motion, the average motion range with AV and free breathing was 9.1 and 7.4 mm (p = 0.36) in the superior‐inferior direction, respectively. However, the results were patient‐specific. In the 7 tumors with large motion, SUVmax with AV was improved for 3 tumors, worsen for 2 tumors, not correlated with respiratory regularity for 2 tumors. Conclusion: A 10‐patients study demonstrated statistically significant improvement of SUVmax with AV for tumors with large motion. Futures studies will focus on optimizing the interface for improved compliance and recruiting patients who have tumors with large motion.Research supported by the Kwanjeong Education Foundation in Korea, NIH/NCI 2 R01 CA 093626, and Stanford BioX Interdisciplinary Initiatives

SU-D-500-05: Comparison of Gating Algorithms in 4D-PET for Mobile Tumor Segmentation

Purpose: To quantitatively compare the accuracy of tumor volume segmentation in four different gating algorithms in gated 4D‐PET. Methods: Four acrylic spheres with inner diameters ranging from 1cm to 4cm were filled with a 11‐C solution and affixed inside a cylindrical bath of 18‐FDG. The system was attached to a robotic arm that underwent 1D motion according to large‐amplitude trajectories based on measured patient breathing trajectories. Two trajectories were used: one with and one without baseline drift. List‐mode data was split into two‐minute images at different source‐to‐background ratios (SBRs), which were gated into eight bins using two amplitude‐based (equal amplitude bins (A1) and equal counts per bin (A2)) and two temporal phased‐based gating algorithms. All gated images were segmented using a commercially available gradient‐based technique. Internal target volumes (ITVs), generated by taking the union of all eight contours per gated image, were compared to their respective ground truths. The ground‐truth ITV was defined as the volume subtended by the tumor model positions covering 99% of breathing amplitude. Superior‐inferior distances between sphere centroids in the end‐inhale and end‐exhale phases were also calculated. Results: Averaged over all sphere sizes, both trajectories, and high and low SBRs, A2 was the closest in accuracy of ITV segmentation, with a measured‐to‐expected ratio of 1.002 vs. 0.920 and 0.964 for temporal phase‐based methods (p<0.05). A1 consistently recovered the greatest volume and had the highest accuracy in the presence of irregular breathing, while A2 performed the best in accurately representing the smallest (1cm) sphere. Amplitude‐based methods consistently produced more distance between end‐inhale and end‐exhale phases than phase‐based methods (27.5% more on average), with A1 recovering up to 94% of peak‐to‐peak separation (p<0.01). Conclusion: Target volumes in images generated from amplitude‐based gating are more accurate, potentially at clinically significant levels, than those from temporal phase‐based gating. NIH R01 CA096679

MR Imaging Assessment of Tumor Perfusion and 3D Segmented Volume at Baseline, during Treatment, and at Tumor Progression in Children with Newly Diagnosed Diffuse Intrinsic Pontine Glioma

DIPG is among the most devastating brain tumors in children, necessitating the development of novel treatment strategies and advanced imaging markers such as perfusion to adequately monitor clinical trials. This study investigated tumor perfusion and 3D segmented tumor volume as predictive markers for outcome in children with newly diagnosed DIPG. Imaging data were assessed at baseline, during, and after RT, and every other month thereafter until tumor progression for 35 patients (ages 2-16 years) with newly diagnosed DIPG enrolled in the phase I clinical study, NCT00472017. Patients were treated with conformal RT and vandetanib, a vascular endothelial growth factor receptor 2 inhibitor. Tumor perfusion increased and tumor volume decreased during combined RT and vandetanib therapy. These changes slowly diminished in follow-up scans until tumor progression. However, increased tumor perfusion and decreased tumor volume during combined therapy were associated with longer PFS. Apart from a longer OS for patients who showed elevated tumor perfusion after RT, there was no association for tumor volume and other perfusion variables with OS. Our results suggest that tumor perfusion may be a useful predictive marker for the assessment of treatment response and tumor progression in children with DIPG treated with both RT and vandetanib. The assessment of tumor perfusion yields valuable information about tumor microvascular status and its response to therapy, which may help better understand the biology of DIPGs and monitor novel treatment strategies in future clinical trials.