Semiautomatic volume of interest drawing for 18F-FDG image analysis—method and preliminary results

An Adaptive Thresholding Method for BTV Estimation Incorporating PET Reconstruction Parameters: A Multicenter Study of the Robustness and the Reliability

OBJECTIVE

The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters.

METHOD

In a multicenter study, a phantom with spheres of different diameters (6.5-57.4 mm) was filled with (18)F-FDG at different target-to-background ratios (TBR: 2.5-70) and scanned for different acquisition periods (2-5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS.

RESULTS

The goodness of the model fit was high (R(2): 0.74-0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation.

CONCLUSIONS

Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration.

Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images

Tumor response to treatment varies among patients. Patient-specific prediction of tumor evolution based on medical images during the treatment can help to build and adapt patient's treatment planning in a non-invasive way. Personalized tumor growth modeling allows patient-specific prediction by estimating model parameters based on individual's images. The model parameters are often estimated by optimizing a cost function constructed based on the tumor delineations. In this paper, we propose a joint framework for tumor growth prediction and tumor segmentation in the context of patient's therapeutic follow ups. Throughout the treatment, a series of sequential positron emission tomography (PET) images are acquired for tumor response monitoring. We propose to take into account the predicted information, which is used in combination with the random walks (RW) algorithm, to develop an automatic tumor segmentation method on PET images. Moreover, we propose an iterative scheme of RW, making the segmentation more performant. Furthermore, the obtained segmentation is applied to the process of model parameter estimation so as to get the model based prediction of tumor evolution. We evaluate our methods on 7 lung tumor patients, totaling 29 PET exams, under radiotherapy by comparing the obtained tumor prediction and tumor segmentation with manual tumor delineation by expert. Our system produces promising results when compared to the state-of-the-art methods.

Heterogeneity of intratumoral (111)In-ibritumomab tiuxetan and (18)F-FDG distribution in association with therapeutic response in radioimmunotherapy for B-cell non-Hodgkin's lymphoma

BACKGROUND

The purpose of this study was to quantitatively evaluate the tumor accumulation and heterogeneity of (111)In-ibritumomab tiuxetan (Zevalin®) and tumor accumulation of (18)F-fluoro-deoxyglucose (FDG) and compare them to the tumor response in B-cell non-Hodgkin's lymphoma patients receiving (90)Y-ibritumomab tiuxetan (Zevalin®) therapy.

METHODS

Sixteen patients with histologically confirmed non-Hodgkin's B-cell lymphoma who underwent (90)Y-ibritumomab tiuxetan therapy along with (111)In-ibritumomab tiuxetan single-photon emission computerized tomography (SPECT)/CT and FDG positron emission tomography (PET)/CT were enrolled in this retrospective study. On pretherapeutic FDG PET/CT images, the maximum standardized uptake value (SUVmax) was measured. On SPECT/CT images, a percentage of the injected dose per gram (%ID/g) and SUVmax of (111)In-ibritumomab tiuxetan were measured at 48 h after its administration. The skewness and kurtosis of the voxel distribution were calculated to evaluate the intratumoral heterogeneity of tumor accumulation. As another intratumoral heterogeneity index, cumulative SUV-volume histograms describing the percentage of the total tumor volume above the percentage thresholds of pretherapeutic FDG and (111)In-ibritumomab tiuxetan SUVmax (area under the curve of the cumulative SUV histograms (AUC-CSH)) were calculated. All lesions (n = 42) were classified into responders and non-responders lesion-by-lesion on pre- and post-therapeutic CT images.

RESULTS

A positive correlation was observed between the FDG SUVmax and accumulation of (111)In-ibritumomab tiuxetan in lesions. A significant difference in pretherapeutic FDG SUVmax was observed between responders and non-responders, while no significant difference in (111)In-ibritumomab tiuxetan SUVmax was observed between the two groups. In contrast, voxel distribution of FDG demonstrated no significant differences in the three heterogeneity indices between responders and non-responders, while (111)In-ibritumomab tiuxetan demonstrated skewness of 0.58 ± 0.16 and 0.73 ± 0.24 (p < 0.05), kurtosis of 2.39 ± 0.32 and 2.78 ± 0.53 (p < 0.02), and AUC-CSH of 0.37 ± 0.04 and 0.34 ± 0.05 (p < 0.05) for responders and non-responders.

CONCLUSIONS

Pretherapeutic FDG accumulation was predictive of the tumor response in (90)Y-ibritumomab tiuxetan therapy. The heterogeneity of the intratumoral distribution rather than the absolute level of (111)In-ibritumomab tiuxetan was correlated with the tumor response.

Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm

A segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake.

Fusion of multi-tracer PET images for dose painting

PET imaging with FluoroDesoxyGlucose (FDG) tracer is clinically used for the definition of Biological Target Volumes (BTVs) for radiotherapy. Recently, new tracers, such as FLuoroThymidine (FLT) or FluoroMisonidazol (FMiso), have been proposed. They provide complementary information for the definition of BTVs. Our work is to fuse multi-tracer PET images to obtain a good BTV definition and to help the radiation oncologist in dose painting. Due to the noise and the partial volume effect leading, respectively, to the presence of uncertainty and imprecision in PET images, the segmentation and the fusion of PET images is difficult. In this paper, a framework based on Belief Function Theory (BFT) is proposed for the segmentation of BTV from multi-tracer PET images. The first step is based on an extension of the Evidential C-Means (ECM) algorithm, taking advantage of neighboring voxels for dealing with uncertainty and imprecision in each mono-tracer PET image. Then, imprecision and uncertainty are, respectively, reduced using prior knowledge related to defects in the acquisition system and neighborhood information. Finally, a multi-tracer PET image fusion is performed. The results are represented by a set of parametric maps that provide important information for dose painting. The performances are evaluated on PET phantoms and patient data with lung cancer. Quantitative results show good performance of our method compared with other methods.

Accuracy of perfusion MRI with high spatial but low temporal resolution to assess invasive breast cancer response to neoadjuvant chemotherapy: a retrospective study

Background

To illustrate that Breast-MRI performed in high spatial resolution and low temporal resolution (1 minute) allows the measurement of kinetic parameters that can assess the final pathologic response to neoadjuvant chemotherapy in breast cancer.

Methods

Breast-MRI was performed in 24 women before and after treatment. Eight series of 1.11 minute-duration were acquired with a sub-millimeter spatial resolution. Transfer constant (K^trans) and leakage space (V_e) were calculated using measured and theoretical Arterial Input Function (AIF). Changes in kinetic parameters after treatment obtained with both AIFs were compared with final pathologic response graded in non-responder (< 50% therapeutic effect), partial-responder (> 50% therapeutic effect) and complete responder. Accuracies to identify non-responders were compared with receiver operating characteristic curves.

Results

With measured-AIF, changes in kinetic parameters measured after treatment were in agreement with the final pathological response. Changes in V_e and K^trans were significantly different between non-(N = 11), partial-(N = 7), and complete (N = 6) responders, (P = 0.0092 and P = 0.0398 respectively). A decrease in V_e of more than -72% and more than -84% for K^trans resulted in 73% sensitivity for identifying non-responders (specificity 92% and 77% respectively). A decrease in V_e of more than -87% helped to identify complete responders (Sensitivity 89%, Specificity 83%). With theoretical-AIF, changes in kinetic parameters had lower accuracy.

Conclusion

There is a good agreement between pathological findings and changes in kinetic parameters obtained with breast-MRI in high spatial and low temporal resolution when measured-AIF is used. Further studies are necessary to confirm whether MRI contrast kinetic parameters can be used earlier as a response predictor to neoadjuvant chemotherapy.

Prediction of tumor response to neoadjuvant therapy in patients with esophageal cancer with use of 18F FDG PET: a systematic review

PURPOSE

To systematically review the accuracy of fluorine 18 ((18)F) fluorodeoxyglucose (FDG) positron emission tomography (PET) in the prediction of tumor response to neoadjuvant therapy in patients with esophageal cancer.

MATERIALS AND METHODS

The MEDLINE and EMBASE databases were systematically searched for relevant studies. Methodologic quality of the included studies was assessed. Sensitivities and specificities of (18)F FDG PET in individual studies were calculated and underwent meta-analysis with a random effects model. A summary receiver operating characteristic curve (sROC) was constructed with the Moses-Shapiro-Littenberg method. A chi(2) test was performed to test for heterogeneity (defined as P < .10). Potential sources for heterogeneity were explored by assessing whether certain covariates significantly (P < .05) influenced the relative diagnostic odds ratio.

RESULTS

Twenty reports, comprising a total of 849 patients with esophageal cancer, were included. Overall, the studies were of moderate methodologic quality. Sensitivity and specificity of (18)F FDG PET ranged from 33% to 100% and from 30% to 100%, respectively, with pooled estimates of 67% (95% confidence interval: 62%, 72%) and 68% (95% confidence interval: 64%, 73%), respectively. The area under the sROC curve was 0.7815. There was significant heterogeneity in both the sensitivity and specificity of the included studies (P < .0001). Spearman rho between the logit of sensitivity and the logit of 1-specificity was 0.086 (P = .719), which suggested that there was no threshold effect. Studies performed outside of the United States and studies of higher methodologic quality yielded significantly higher overall accuracy.

CONCLUSION

On the basis of current evidence, (18)F FDG PET should not yet be used in routine clinical practice to guide neoadjuvant therapy decisions in patients with esophageal cancer.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.09091324/-/DC1.

Comparative assessment of methods for estimating tumor volume and standardized uptake value in (18)F-FDG PET

In (18)F-FDG PET, tumors are often characterized by their metabolically active volume and standardized uptake value (SUV). However, many approaches have been proposed to estimate tumor volume and SUV from (18)F-FDG PET images, none of them being widely agreed upon. We assessed the accuracy and robustness of 5 methods for tumor volume estimates and of 10 methods for SUV estimates in a large variety of configurations.

METHODS

PET acquisitions of an anthropomorphic phantom containing 17 spheres (volumes between 0.43 and 97 mL, sphere-to-surrounding-activity concentration ratios between 2 and 68) were used. Forty-one nonspheric tumors (volumes between 0.6 and 92 mL, SUV of 2, 4, and 8) were also simulated and inserted in a real patient (18)F-FDG PET scan. Four threshold-based methods (including one, T(bgd), accounting for background activity) and a model-based method (Fit) described in the literature were used for tumor volume measurements. The mean SUV in the resulting volumes were calculated, without and with partial-volume effect (PVE) correction, as well as the maximum SUV (SUV(max)). The parameters involved in the tumor segmentation and SUV estimation methods were optimized using 3 approaches, corresponding to getting the best of each method or testing each method in more realistic situations in which the parameters cannot be perfectly optimized.

RESULTS

In the phantom and simulated data, the T(bgd) and Fit methods yielded the most accurate volume estimates, with mean errors of 2% +/- 11% and -8% +/- 21% in the most realistic situations. Considering the simulated data, all SUV not corrected for PVE had a mean bias between -31% and -46%, much larger than the bias observed with SUV(max) (-11% +/- 23%) or with the PVE-corrected SUV based on T(bgd) and Fit (-2% +/- 10% and 3% +/- 24%).

CONCLUSION

The method used to estimate tumor volume and SUV greatly affects the reliability of the estimates. The T(bgd) and Fit methods yielded low errors in volume estimates in a broad range of situations. The PVE-corrected SUV based on T(bgd) and Fit were more accurate and reproducible than SUV(max).

Development of a generic thresholding algorithm for the delineation of 18FDG-PET-positive tissue: application to the comparison of three thresholding models

An iterative generic algorithm has been developed to compare three thresholding models used to delineate gross tumour volume on (18)F-FDG PET images. 3D volume was extracted and characteristic parameters were measured. Three fitting models using different parameters were studied: model 1 (volume, contrast), model 2 (contrast) and model 3 (SUV). The calibration was performed using a cylindrical phantom filled with hot spheres. To validate the models, two other phantoms were used. The calibration procedure showed a better fitting model for model 1 (R(2) from 0.94 to 1.00) than for model 3 (0.95) and model 2 (0.69). The validation study shows that model 3 yielded large volume measurement errors. Models 1 and 2 gave close results with no significant differences. Model 2 was preferred because it presents less error dispersion and needs fewer characteristic parameters, making it easier to implement. Our results show the importance of developing a generic algorithm to compare the performances of fitting models objectively and to validate results on other phantoms than the ones used during the calibration process to avoid methodological biases.

Imaging in targeted delivery of therapy to cancer

We review the current status of imaging as applied to targeted therapy with particular focus on antibody-based therapeutics. Antibodies have high tumor specificity and can be engineered to optimize delivery to, and retention within, the tumor. Whole antibodies can activate natural immune effector mechanisms and can be conjugated to beta- and alpha-emitting radionuclides, toxins, enzymes, and nanoparticles for enhanced therapeutic effect. Imaging is central to the development of these agents and is used for patient selection, performing dosimetry and assessment of response. gamma- and positron-emitting radionuclides may be used to image the distribution of antibody-targeted therapeutics While some radionuclides such as iodine-131 emit both beta and gamma radiation and are therefore suitable for both imaging and therapy, others are more suited to imaging or therapy alone. Hence for radionuclide therapy of neuroendocrine tumors, patients can be selected for therapy on the basis of gamma-emitting indium-111-octreotide imaging and treated with beta-emitting yttrium-90-octreotate. Positron-emitting radionuclides can give greater sensitivity that gamma-emitters but only a single radionuclide can be imaged at one time and the range of radionuclides is more limited. The multiple options for antibody-based therapeutic molecules, imaging technologies and therapeutic scenarios mean that very large amounts of diverse data are being acquired. This can be most effectively shared and progress accelerated by use of common data standards for imaging, biological, and clinical data.

A phase I trial of radioimmunotherapy with 131I-A5B7 anti-CEA antibody in combination with combretastatin-A4-phosphate in advanced gastrointestinal carcinomas

PURPOSE

In preclinical models, radioimmunotherapy with (131)I-A5B7 anti-carcinoembryonic antigen (CEA) antibody ((131)I-A5B7) combined with the vascular disruptive agent combretastatin-A4-phosphate (CA4P) produced cures unlike either agent alone. We conducted a phase I trial determining the dose-limiting toxicity (DLT), maximum tolerated dose, efficacy, and mechanism of this combination in patients with gastrointestinal adenocarcinomas.

EXPERIMENTAL DESIGN

Patients had CEA of 10 to 1,000 microg/L, QTc < or =450 ms, no cardiac arrhythmia/ischaemia, and adequate hematology/biochemistry. Tumor was suitable for blood flow analysis by dynamic contrast enhanced-magnetic resonance imaging (MRI). The starting dose was 1,800 MBq/m(2) of (131)I-A5B7 on day 1 and 45 mg/m(2) CA4P given 48 and 72 hours post-(131)I-A5B7, then weekly for up to seven weeks.

RESULTS

Twelve patients were treated, with mean age of 63 years (range, 32-77). Two of six patients at the first dose level had DLTs (grade 4 neutropenia). The dose was reduced to 1,600 MBq/m(2), and CA4P escalated to 54 mg/m(2). Again, two of six patients had DLTs (neutropenia). Of ten assessable patients, three had stable disease and seven had progressive disease. Single-photon emission computed tomography confirmed tumor antibody uptake in all 10 patients. DCE-MRI confirmed falls in kinetic parameters (K(trans)/IAUGC(60)) in 9 of 12 patients. The change of both pharmacokinetic parameters reached a level expected to produce efficacy in one patient who had a minor response on computed tomography and a reduced serum tumor marker level.

CONCLUSIONS

This is believed to be the first trial reporting the combination of radioimmunotherapy and vascular disruptive agent; each component was shown to function, and myelosuppression was dose-limiting. Optimal dose and timing of CA4P, and moderate improvements in the performance of radioimmunotherapy seem necessary for efficacy.