Use of PET and PET/CT for Radiation Therapy Planning: IAEA expert report 2006–2007

Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving ¹⁸F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

Lung Cancer: Posttreatment Imaging: Radiation Therapy and Imaging Findings

In this review, we discuss the different radiation delivery techniques available to treat non-small cell lung cancer, typical radiologic manifestations of conventional radiotherapy, and different patterns of lung injury and temporal evolution of the newer radiotherapy techniques. More sophisticated techniques include intensity-modulated radiotherapy, stereotactic body radiotherapy, proton therapy, and respiration-correlated computed tomography or 4-dimensional computed tomography for radiotherapy planning. Knowledge of the radiation treatment plan and technique, the completion date of radiotherapy, and the temporal evolution of radiation-induced lung injury is important to identify expected manifestations of radiation-induced lung injury and differentiate them from tumor recurrence or infection.

Biological imaging for individualized therapy in radiation oncology: part II medical and clinical aspects

Positron emission tomography and multiparametric MRI provide crucial information concerning tumor extent and normal tissue anatomy. Moreover, they are able to visualize biological characteristics of the tumor, which can be considered in the radiation treatment planning and monitoring. In this review we discuss the impact of biological imaging positron emission tomography and multiparametric MRI for radiation oncology, based on the data of the literature and on the experience of our own institution in this field.

Influence of PET reconstruction para meters on the TrueX algorithm

With the increasing use of functional imaging in modern radiotherapy (RT) and the envisaged automated integration of PET into target definition, the need for reliable quantification of PET is growing. Reconstruction algorithms in new PET scanners employ pointspread-function (PSF) based resolution recovery, however, their impact on PET quantification still requires thorough investigation. Patients, material, methods: Measurements were performed on a Siemens PET/CT using an IEC phantom filled with varying activity. Data were reconstructed using the OSEM (Gauss filter) and the PSF TrueX (Gauss and Allpass filter) algorithm with all available products of iterations (i) and subsets (ss). The recovery coeffcient (RC) and threshold defining the real sphere volume were determined for all settings and compared to the clinical standard (4i21ss). PET acquisitions of eight lung patients were reconstructed using all algorithms with 4i21ss. Volume size and tracer uptake were determined with different segmentation methods. Results: The threshold for the TrueX was lower (up to 40%) than for the OSEM. The RC for the different algorithms and filters varied. TrueX was more sensitive to permutations of i and ss and only the RC of the OSEM stabilised with increasing number. For patient scans the difference of the volume and activity between TrueX and OSEM could be reduced by applying an adapted threshold and activity correction. Conclusion: The TrueX algorithm results in excellent diagnostic image quality, however, guidelines for native algorithms have to be extended for PSF based reconstruction methods. For appropriate tumour delineation, for the TrueX a lower threshold than the 42% recommended for the OSEM is necessary. These filter dependent thresholds have to be verified for different scanners prior to using them in multicenter trials.

Which is the optimal threshold for defining functional lung in single-photon emission computed tomography lung perfusion imaging of lung cancer patients?

PURPOSE

The aim of this study was to investigate the optimal threshold for the functional lung (FL) definition of single-photon emission computed tomography (SPECT) lung perfusion imaging.

PATIENTS AND METHODS

Forty consecutive stage III non-small-cell lung cancer patients underwent SPECT lung perfusion scans and PET/CT scans for treatment planning, and the images were coregistered. Total lung and perfusion lung volumes corresponding to 10, 20, …, 60% of the maximum SPECT count were segmented automatically. The SPECT-weighted mean lung dose (SWMDx%) and the percentage of FL volume receiving more than 20 Gy (Fx%V20) of different thresholds were investigated using SPECT-weighted dose-volume histograms. Receiver-operator characteristic curves were used to identify SWMD and FV20 of different thresholds in predicting the incidence of radiation pneumonitis (RP).

RESULTS

Eleven (27.5%) patients developed RP (grades 1, 2, 3, and 4 were 10.0, 7.5, 7.5, and 2.5%, respectively) after treatment. The largest area under the receiver-operator characteristic curve was 0.881 for the ability of SWMD to predict RP with 20% as the threshold and 0.928 for the ability of FV20 with 20% as the threshold.

CONCLUSION

The SWMD20% and FV20 of FL using 20% of the maximum SPECT count as the threshold may be better predictors for the risk of RP.

Anatomic, functional and molecular imaging in lung cancer precision radiation therapy: treatment response assessment and radiation therapy personalization

This article reviews key imaging modalities for lung cancer patients treated with radiation therapy (RT) and considers their actual or potential contributions to critical decision-making. An international group of researchers with expertise in imaging in lung cancer patients treated with RT considered the relevant literature on modalities, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). These perspectives were coordinated to summarize the current status of imaging in lung cancer and flag developments with future implications. Although there are no useful randomized trials of different imaging modalities in lung cancer, multiple prospective studies indicate that management decisions are frequently impacted by the use of complementary imaging modalities, leading both to more appropriate treatments and better outcomes. This is especially true of ¹⁸F-fluoro-deoxyglucose (FDG)-PET/CT which is widely accepted to be the standard imaging modality for staging of lung cancer patients, for selection for potentially curative RT and for treatment planning. PET is also more accurate than CT for predicting survival after RT. PET imaging during RT is also correlated with survival and makes response-adapted therapies possible. PET tracers other than FDG have potential for imaging important biological process in tumors, including hypoxia and proliferation. MRI has superior accuracy in soft tissue imaging and the MRI Linac is a rapidly developing technology with great potential for online monitoring and modification of treatment. The role of imaging in RT-treated lung cancer patients is evolving rapidly and will allow increasing personalization of therapy according to the biology of both the tumor and dose limiting normal tissues.

Integration of PET/MR Hybrid Imaging into Radiation Therapy Treatment

Hybrid PET/MR imaging is in early development for treatment planning. This article briefly reviews research and clinical applications of PET/MR imaging in radiation oncology. With improvements in workflow, more specific tracers, and fast and robust acquisition protocols, PET/MR imaging will play an increasingly important role in better target delineation for treatment planning and have clear advantages in the evaluation of tumor response and in a better understanding of tumor heterogeneity. With advances in treatment delivery and the potential of integrating PET/MR imaging with research on radiomics for radiation oncology, quantitative and physiologic information could lead to more precise and personalized RT.

Visualization of 4D multimodal imaging data and its applications in radiotherapy planning

PURPOSE

To explore the benefit of using 4D multimodal visualization and interaction techniques for defined radiotherapy planning tasks over a treatment planning system used in clinical routine (C-TPS) without dedicated 4D visualization.

METHODS

We developed a 4D visualization system (4D-VS) with dedicated rendering and fusion of 4D multimodal imaging data based on a list of requirements developed in collaboration with radiation oncologists. We conducted a user evaluation in which the benefits of our approach were evaluated in comparison to C-TPS for three specific tasks: assessment of internal target volume (ITV) delineation, classification of tumor location in peripheral or central, and assessment of dose distribution. For all three tasks, we presented test cases for which we measured correctness, certainty, consistency followed by an additional survey regarding specific visualization features.

RESULTS

Lower quality of the test ITVs (ground truth quality was available) was more likely to be detected using 4D-VS. ITV ratings were more consistent in 4D-VS and the classification of tumor location had a higher accuracy. Overall evaluation of the survey indicates 4D-VS provides better spatial comprehensibility and simplifies the tasks which were performed during testing.

CONCLUSIONS

The use of 4D-VS has improved the assessment of ITV delineations and classification of tumor location. The visualization features of 4D-VS have been identified as helpful for the assessment of dose distribution during user testing.

Prognostic value of metabolic variables of [18F]FDG PET/CT in surgically resected stage I lung adenocarcinoma

The objective of this study was to assess the prognostic value of metabolic tumor burden measured by positron emission tomography/computed tomography (PET/CT) in patients with stage I lung adenocarcinoma.We reviewed 127 consecutive patients with pathologically proven stage I lung adenocarcinoma who underwent pretreatment [18F]FDG PET/CT scans in our hospital from 2005 June to 2012 June. The maximum, mean, and peak standardized uptake value (SUVmax, SUVmean, and SUVpeak), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and computed tomography volume (CTV) were measured. The Kaplan-Meier and Cox proportional hazards model were used with age, gender, TNM stage, clinical stage, histological grade, nodule type, tumor size, and metabolic parameters to predict progression-free survival (PFS). The cut-off point was determined through receiver-operating characteristic curve.In univariate analysis, the histological grade, nodule type, diameter (cut-off value of 2.0 cm), CTV (6.56 cm), SUVmax (3.25 g/mL), SUVmean (1.58 g/mL), SUVpeak (1.84 g/mL), MTV (4.80 cm), and TLG (10.40) were significantly associated with PFS (all P value < .05). Patients with poorly differentiated adenocarcinoma, solid nodule type, large size, and high metabolic tumor burden were associated with poor prognosis. In multivariate analysis, only histological grade was independent prognostic factors for progression with a P value of .005 (RR, 0.355; 95% CI, 0.173-0.728). Among 5 PET/CT metabolic parameters, only MTV was independent prognostic factors for progression with a P value of .031 (RR, 1.118; 95% CI, 1.010-1.237).Histological grade was an independent predictor for progression in patients with stage I lung adenocarcinoma. Among 5 PET/CT metabolic parameters, only MTV was an independent predictor for progression.

Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

In this study, we investigate the use of imaging feature-based outcomes research ('radiomics') combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC)  =  0.65 (p  =  0.004), 0.73 (p  =  0.026), and 0.66 (p  =  0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC  =  0.68 (p  =  0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC  =  0.60 (p  =  0.092) and 0.65 (p  =  0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

PURPOSE

The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application.

APPROACH

A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed.

FINDINGS

A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol.

CONCLUSIONS

Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.

Quantitative imaging: Correlating image features with the segmentation accuracy of PET based tumor contours in the lung

The purpose of this study was to investigate the correlation between image features extracted from PET images and the accuracy of manually drawn lesion contours in the lung. Such correlations are interesting in that they could potentially be used in predictive models to help guide physician contouring. In this work, 26 synthetic PET datasets were created using an anthropomorphic phantom and Monte Carlo simulation. Manual contours of simulated lesions were provided by 10 physicians. Contour accuracy was quantified using five commonly used similarity metrics which were then correlated with several features extracted from the images. Features were sub-divided into three groups using intensity, geometry, and texture as categorical descriptors. When averaged among the participants, the results showed relatively strong correlations with complexity and contrast_I (r≥0.65, p<0.001), and moderate correlations with several other image features (r≥0.5, p<0.01). The predictive nature of these correlations was improved through stepwise regression and the creation of multi-feature models. Imaging features were also correlated with the standard deviation of contouring error in order to investigate inter-observer variability. Several features were consistently identified as influential including integral of mean curvature and complexity. These relationships further the understanding as to what causes variation in the contouring of PET positive lesions.

18 F-FDG PET/CT for planning external beam radiotherapy alters therapy in 11% of 581 patients

BACKGROUND

¹⁸ F-FDG PET/CT (FDG PET/CT) used in radiotherapy planning for extra-cerebral malignancy may reveal metastases to distant sites that may affect the choice of therapy.

AIM

To investigate the role of FDG PET/CT on treatment strategy changes induced by the use of PET/CT as part of the radiotherapy planning. 'A major change of treatment strategy' was defined as either including more lesions in the gross tumour volume (GTV) distant from the primary tumour or a change in treatment modalities.

METHODS

The study includes 581 consecutive patients who underwent an FDG PET/CT scan for radiotherapy planning in our institution in the year 2008. All PET/CT scans were performed with the patient in treatment position with the use of immobilization devices according to the intended radiotherapy treatment. All scans were evaluated by a nuclear medicine physician together with a radiologist to delineate PET-positive GTV (GTV-PET).

RESULTS

For 63 of the patients (11%), the PET/CT simulation scans resulted in a major change in treatment strategy because of the additional diagnostic information. Changes were most frequently observed in patients with lung cancer (20%) or upper gastrointestinal cancer (12%). In 65% of the patients for whom the PET/CT simulation scan revealed unexpected dissemination, radiotherapy was given - changed (n = 38) or unchanged (n = 13) according to the findings on the FDG PET/CT.

CONCLUSION

Unexpected dissemination on the FDG PET/CT scanning performed for radiotherapy planning caused a change in treatment strategy in 11% of 581 patients.

A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions

PURPOSE

The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness.

METHODS

The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10-37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available.

RESULTS

Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a calibration curve was determined to automatically generate the optimal parameter from the variance of the ROI. This "calibration curve" was then applied to contour the whole data set. The accuracy (mean discrepancy between DPM model-based contours and reference contours) of volume estimation was below (1 ± 7)% on the whole data set (1 SD). The overlap between true and automatically segmented contours, measured by the Dice similarity coefficient, was 0.93 with a SD of 0.03.

CONCLUSIONS

The proposed DPM model was able to accurately reproduce known volumes of FDG concentration, with high overlap between segmented and true volumes. For all the analyzed inserts of the IEC phantom, the algorithm proved to be robust to variations in radius and in TBR. The main advantage of this algorithm was that no setting of DPM parameters was required in advance, since the proper setting of the only parameter that could significantly influence the segmentation results was automatically related to the uptake variance of the chosen ROI. Furthermore, the algorithm did not need any preliminary choice of the optimum number of classes to describe the ROIs within PET images and no assumption about the shape of the lesion and the uptake heterogeneity of the tracer was required.

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 (GTVp_CT/MRI), together with ATLAAS-generated contours (GTVp_ATLAAS) were used to derive the RT planning GTV (GTVp_final). 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 GTVp_ATLAAS was smaller than GTVp_CT/MRI in 70% of the cases, with an average conformity index of 0.70. The information provided by ATLAAS was used to grow the GTVp_CT/MRI in 10 cases (up to 10.6mL) and to shrink the GTVp_CT/MRI in 7 cases (up to 12.3mL). ATLAAS provided complementary information to CT/MRI and GTVp_ATLAAS 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.

A comparative study of target volumes based on 18F-FDG PET-CT and ten phases of 4DCT for primary thoracic squamous esophageal cancer

Purpose

To investigate the correlations in target volumes based on ¹⁸F-FDG PET/CT and four-dimensional CT (4DCT) to detect the feasibility of implementing PET in determining gross target volumes (GTV) for tumor motion for primary thoracic esophageal cancer (EC).

Methods

Thirty-three patients with EC sequentially underwent contrast-enhanced 3DCT, 4DCT, and ¹⁸F-FDG PET-CT thoracic simulation. The internal gross target volume (IGTV)₁₀ was obtained by combining the GTV from ten phases of 4DCT. The GTVs based on PET/CT images were defined by setting of different standardized uptake value thresholds and visual contouring. The difference in volume ratio, conformity index (CI), and degree of inclusion (DI) between IGTV₁₀ and GTV_PET was compared.

Results

The images from 20 patients were suitable for further analysis. The optimal volume ratio of 0.95±0.32, 1.06±0.50, 1.07±0.49 was at standardized uptake value (SUV)_2.5, SUV_20%, or manual contouring. The mean CIs were from 0.33 to 0.54. The best CIs were at SUV_2.0 (0.51±0.11), SUV_2.5 (0.53±0.13), SUV_20% (0.53±0.12), and manual contouring (0.54±0.14). The mean DIs of GTV_PET in IGTV₁₀ were from 0.60 to 0.90, and the mean DIs of IGTV₁₀ in GTV_PET ranged from 0.35 to 0.78. A negative correlation was found between the mean CI and different SUV (P=0.000).

Conclusion

None of the PET-based contours had both close spatial and volumetric approximation to the 4DCT IGTV₁₀. Further evaluation and optimization of PET as a tool for target identification are required.

Clinical respiratory motion correction software (reconstruct, register and averaged-RRA), for 18F-FDG-PET-CT: phantom validation, practical implications and patient evaluation

OBJECTIVE

On fluorine-18 fludeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) CT of pulmonary or hepatic lesions, standard uptake value (SUV) is often underestimated due to patient breathing. The aim of this study is to validate, on phantom and patient data, a motion correction algorithm [reconstruct, register and averaged (RRA)] implemented on a PET-CT system.

METHODS

Three phantoms containing five spheres filled with ¹⁸F-FDG and suspended in a water or Styrofoam^®18F-FDG-filled tank to create different contrasts and attenuation environment were acquired on a Discovery GE710. The spheres were animated with a 2-cm longitudinal respiratory-based movement. Respiratory-gated (RRA) and ungated PET images were compared with static reference images (without movement). The optimal acquisition time, number of phases and the best phase within the respiratory cycle were investigated. The impact of irregular motion was also investigated. Quantification impact was computed on each sphere. Quantification improvement on 28 lung lesions was also investigated.

RESULTS

Phantoms: 4 min was required to obtain a stable quantification with the RRA method. The reference phase and the number of phases used for RRA did not affect the quantification which was similar on static acquisitions but different on ungated images. The results showed that the maximum standard uptake value (SUV_max) restoration is majored for the smallest spheres (≤2.1 ml).

PATIENTS

SUV_max on RRA and ungated acquisitions were statistically different to the SUV_max on whole-body images (p = 0.05) but not different from each other (mean SUV_max: 7.0 ± 7.8 vs 6.9 ± 7.8, p = 0.23 on RRA and ungated images, respectively). We observed a statistically significant correlation between SUV restoration and lesion displacement, with a real SUV quantitation improvement for lesion with movement >1.2 mm.

CONCLUSION

According to the results obtained using phantoms, RRA method is promising, showing a real impact on the lesion quantification on phantom data. With regard to the patient study, our results showed a trend towards an increase in the SUVs and a decrease in the volume between the ungated and RRA data. We also noticed a statistically significant correlation between the quantitative restoration obtained with RRA compared with ungated data and lesion displacement, indicating that the RRA approach should be reserved to patients with small lesions or nodes moving with a displacement larger than 1.2 cm. Advances in knowledge: This article investigates the performances of motion correction software recently introduced in PET. The conclusion revealed that such respiratory motion correction approach shows a real impact on the lesion quantification but must be reserved to the patient for whom lesion displacement was confirmed and high enough to clearly impact lesion evaluation.

Optimization of oncological ¹⁸F-FDG PET/CT imaging based on a multiparameter analysis

PURPOSE

This paper describes a method to achieve consistent clinical image quality in (18)F-FDG scans accounting for patient habitus, dose regimen, image acquisition, and processing techniques.

METHODS

Oncological PET/CT scan data for 58 subjects were evaluated retrospectively to derive analytical curves that predict image quality. Patient noise equivalent count rate and coefficient of variation (CV) were used as metrics in their analysis. Optimized acquisition protocols were identified and prospectively applied to 179 subjects.

RESULTS

The adoption of different schemes for three body mass ranges (<60 kg, 60-90 kg, >90 kg) allows improved image quality with both point spread function and ordered-subsets expectation maximization-3D reconstruction methods. The application of this methodology showed that CV improved significantly (p < 0.0001) in clinical practice.

CONCLUSIONS

Consistent oncological PET/CT image quality on a high-performance scanner was achieved from an analysis of the relations existing between dose regimen, patient habitus, acquisition, and processing techniques. The proposed methodology may be used by PET/CT centers to develop protocols to standardize PET/CT imaging procedures and achieve better patient management and cost-effective operations.

[Role of functional imaging in the definition of target volumes for lung cancer radiotherapy]

Functional imaging with positron emission tomography (PET) is interesting to optimize lung radiotherapy planning, and probably to deliver a heterogeneous dose or adapt the radiation dose during treatment. Only fluorodeoxyglucose (FDG) PET-computed tomography (CT) is validated for staging lung cancer and planning radiotherapy. The optimal segmentation methods remain to be defined as well as the interest of "dose painting" from pre-treatment PET (metabolism: FDG) or hypoxia (fluoromisonidazole: FMISO) and the interest of replanning based on pertherapeutic PET.

Image based modeling of tumor growth

Tumors are a main cause of morbidity and mortality worldwide. Despite the efforts of the clinical and research communities, little has been achieved in the past decades in terms of improving the treatment of aggressive tumors. Understanding the underlying mechanism of tumor growth and evaluating the effects of different therapies are valuable steps in predicting the survival time and improving the patients' quality of life. Several studies have been devoted to tumor growth modeling at different levels to improve the clinical outcome by predicting the results of specific treatments. Recent studies have proposed patient-specific models using clinical data usually obtained from clinical images and evaluating the effects of various therapies. The aim of this review is to highlight the imaging role in tumor growth modeling and provide a worthwhile reference for biomedical and mathematical researchers with respect to tumor modeling using the clinical data to develop personalized models of tumor growth and evaluating the effect of different therapies.

External beam radiotherapy in thyroid carcinoma: clinical review and recommendations of the AIRO "Radioterapia Metabolica" Group

The therapeutic approach to thyroid carcinoma usually involves surgery as initial treatment. The use of external beam radiotherapy (EBRT) is limited to high-risk patients and depends on clinical stage and histologic type. Different behavior patterns and degrees of aggressiveness of thyroid carcinomas require different management for differentiated, medullary, and anaplastic carcinoma. However, the role of EBRT is an issue of debate. Most clinical studies are retrospective and based on single-institution experiences. In this article, we review the main literature and give recommendations for the use of EBRT in thyroid carcinoma on behalf of the "Radioterapia Metabolica" Group of the Italian Radiation Oncology Association.

Functional magnetic resonance imaging techniques and their development for radiation therapy planning and monitoring in the head and neck cancers

Radiation therapy (RT), in particular intensity-modulated radiation therapy (IMRT), is becoming a more important nonsurgical treatment strategy in head and neck cancer (HNC). The further development of IMRT imposes more critical requirements on clinical imaging, and these requirements cannot be fully fulfilled by the existing radiotherapeutic imaging workhorse of X-ray based imaging methods. Magnetic resonance imaging (MRI) has increasingly gained more interests from radiation oncology community and holds great potential for RT applications, mainly due to its non-ionizing radiation nature and superior soft tissue image contrast. Beyond anatomical imaging, MRI provides a variety of functional imaging techniques to investigate the functionality and metabolism of living tissue. The major purpose of this paper is to give a concise and timely review of some advanced functional MRI techniques that may potentially benefit conformal, tailored and adaptive RT in the HNC. The basic principle of each functional MRI technique is briefly introduced and their use in RT of HNC is described. Limitation and future development of these functional MRI techniques for HNC radiotherapeutic applications are discussed. More rigorous studies are warranted to translate the hypotheses into credible evidences in order to establish the role of functional MRI in the clinical practice of head and neck radiation oncology.

Risk factors and management of oligometastatic non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is an aggressive malignancy with close to half of all patients presenting with metastatic disease. A proportion of these patients with limited metastatic disease, termed oligometastatic disease, have been shown to benefit from a definitive treatment approach. Synchronous and metachronous presentation of oligometastatic disease have prognostic significance, with current belief that metachronous disease is more favorable. Surgical excision of intracranial and extracranial oligometastatic disease has been shown to improve survival, especially in patients with lymph node-negative disease, adenocarcinoma histology and smaller thoracic tumors. Definitive radiation to sites of oligometastatic disease and initial thoracic disease has also been shown to have a similar impact on survival for both intracranial and extracranial disease. Recent studies have reported on the use of targeted agents combined with ablative doses of radiation in the oligometastatic setting with promising outcomes. In this review, we present the historical and current literature describing surgical and radiation treatment options for patients with oligometastatic NSCLC.

PET/MR in Breast Cancer

Breast cancer is an international public health concern in which an optimal treatment plan requires a precise staging. Both MRI and PET imaging techniques have made significant progress in the last decades with constant improvements that made both modalities clinically relevant in several stages of breast cancer management and follow-up. On one hand, specific breast MRI permits high diagnostic accuracy for local tumor staging, and whole-body MRI can also be of great use in distant staging, eventually accompanied by organ-specific MRI sequences. Moreover, many different MRI sequences can be performed, including functional MRI, letting us foresee important improvements in breast cancer characterization in the future. On the contrary, (18)F-FDG-PET has a high diagnostic performance for the detection of distant metastases, and several other tracers currently under development may profoundly affect breast cancer management in the future with better determination of different types of breast cancers allowing personalized treatments. As a consequence PET/MR is a promising emerging technology, and it is foreseeable that in cases where both PET and MRI data are needed, a hybrid acquisition is justified when available. However, at this stage of deployment of such hybrid scanners in a clinical setting, more data are needed to demonstrate their added value beyond just patient comfort of having to undergo a single examination instead of two, and the higher confidence of diagnostic interpretation of these co-registered images. Optimized imaging protocols are still being developed and are prone to provide more efficient hybrid protocols with a potential improvement in diagnostic accuracy. More convincing studies with larger number of patients as well as cost-effectiveness studies are needed. This article provides insights into the current state-of-the-art of PET/MR in patients with breast cancer and gives an outlook on future developments of both imaging techniques and potential applications in the future.

Detection of bladder metabolic artifacts in (18)F-FDG PET imaging

Positron emission tomography using (18)F-fluorodeoxyglucose ((18)F-FDG-PET) is a widely used imaging modality in oncology. It enables significant functional information to be included in analyses of anatomical data provided by other image modalities. Although PET offers high sensitivity in detecting suspected malignant metabolism, (18)F-FDG uptake is not tumor-specific and can also be fixed in surrounding healthy tissue, which may consequently be mistaken as cancerous. PET analyses may be particularly hampered in pelvic-located cancers by the bladder׳s physiological uptake potentially obliterating the tumor uptake. In this paper, we propose a novel method for detecting (18)F-FDG bladder artifacts based on a multi-feature double-step classification approach. Using two manually defined seeds (tumor and bladder), the method consists of a semi-automated double-step clustering strategy that simultaneously takes into consideration standard uptake values (SUV) on PET, Hounsfield values on computed tomography (CT), and the distance to the seeds. This method was performed on 52 PET/CT images from patients treated for locally advanced cervical cancer. Manual delineations of the bladder on CT images were used in order to evaluate bladder uptake detection capability. Tumor preservation was evaluated using a manual segmentation of the tumor, with a threshold of 42% of the maximal uptake within the tumor. Robustness was assessed by randomly selecting different initial seeds. The classification averages were 0.94±0.09 for sensitivity, 0.98±0.01 specificity, and 0.98±0.01 accuracy. These results suggest that this method is able to detect most (18)F-FDG bladder metabolism artifacts while preserving tumor uptake, and could thus be used as a pre-processing step for further non-parasitized PET analyses.

Magnetic resonance imaging in lung: a review of its potential for radiotherapy

MRI has superior soft-tissue definition compared with existing imaging modalities in radiation oncology; this has the added benefit of functional as well as anatomical imaging. This review aimed to evaluate the current use of MRI for lung cancer and identify the potential of a MRI protocol for lung radiotherapy (RT). 30 relevant studies were identified. Improvements in MRI technology have overcome some of the initial limitations of utilizing MRI for lung imaging. A number of commercially available and novel sequences have shown image quality to be adequate for the detection of pulmonary nodules with the potential for tumour delineation. Quantifying tumour motion is also feasible and may be more representative than that seen on four-dimensional CT. Functional MRI sequences have shown correlation with flu-deoxy-glucose positron emission tomography (FDG-PET) in identifying malignant involvement and treatment response. MRI can also be used as a measure of pulmonary function. While there are some limitations for the adoption of MRI in RT-planning process for lung cancer, MRI has shown the potential to compete with both CT and PET for tumour delineation and motion definition, with the added benefit of functional information. MRI is well placed to become a significant imaging modality in RT for lung cancer.

FDG and Beyond

Although many PET tracers are in use, FDG still is the most widely used in clinical oncology practice. FDG therefore deserves an in-depth discussion, which is even more interesting because of the huge increase in the molecular biology of glucose metabolism. Obviously, other tracers are of increasing importance as well, and these will be discussed in short.

[Positron emission tomography and stereotactic body radiation therapy for lung cancer: From treatment planning to response evaluation]

Stereotactic body radiation therapy is the standard treatment for inoperable patients with early-stage lung cancer. Local control rates range from 80 to 90 % 2 years after treatment. The role of positron emission tomography in patient selection is well known, but its use for target definition or therapeutic response evaluation is less clear. We reviewed the literature in order to assess the current state of knowledge in this area.

Tumor necrosis targeted radiotherapy of non-small cell lung cancer using radioiodinated protohypericin in a mouse model

Lung cancer is the leading cause of cancer-related death. About 80% of lung cancers are non-small cell lung cancers (NSCLC). Radiotherapy is widely used in treatment of NSCLC. However, the outcome of NSCLC remains unsatisfactory. In this study, a vascular disrupting agent (VDA) combretastatin-A4-phosphate (CA4P) was used to provide massive necrosis targets. (131)I labeled necrosis-avid agent protohypericin ((131)I-prohy) was explored for therapy of NSCLC using tumor necrosis targeted radiotherapy (TNTR). Gamma counting, autoradiography, fluorescence microscopy and histopathology were used for biodistribution analysis. Magnetic resonance imaging (MRI) was used to monitor tumor volume, ratios of necrosis and tumor doubling time (DT). The biodistribution data revealed 131I-prohy was delivered efficiently to tumors. Tracer uptake peaked at 24 h in necrotic tumor of (131)I-prohy with and without combined CA4P (3.87 ± 0.38 and 2.96 ± 0.34%ID/g). (131)I-prohy + CA4P enhanced the uptake of (131)I-prohy in necrotic tumor compared to (131)I-prohy alone. The TNTR combined with CA4P prolonged survival of tumor bearing mice relative to vehicle control group, CA4P control group and (131)I-prohy control group with median survival of 35, 20, 22 and 27 days respectively. In conclusion, TNTR appeared to be effective for the treatment of NSCLC.

Noninvasive Evaluation of Metabolic Tumor Volume in Lewis Lung Carcinoma Tumor-Bearing C57BL/6 Mice with Micro-PET and the Radiotracers 18F-Alfatide and 18F-FDG: A Comparative Analysis

Purpose

To explore the value of a new simple lyophilized kit for labeling PRGD₂ peptide (¹⁸F-ALF-NOTA-PRGD₂, denoted as ¹⁸F-alfatide) in the determination of metabolic tumor volume (MTV) with micro-PET in lewis lung carcinoma (LLC) tumor-bearing C57BL/6 mice verified by pathologic examination and compared with those using ¹⁸F-fluorodeoxyglucose (FDG) PET.

Methods

All LLC tumor-bearing C57BL/6 mice underwent two attenuation-corrected whole-body micro-PET scans with the radiotracers ¹⁸F-alfatide and ¹⁸F-FDG within two days. ¹⁸F-alfatide metabolic tumor volume (V_RGD) and ¹⁸F-FDG metabolic tumor volume (V_FDG) were manually delineated slice by slice on PET images. Pathologic tumor volume (V_Path) was measured in vitro after the xenografts were removed.

Results

A total of 37 mice with NSCLC xenografts were enrolled and 33 of them underwent ¹⁸F-alfatide PET, and 35 of them underwent ¹⁸F-FDG PET and all underwent pathological examination. The mean ± standard deviation of V_Path, V_RGD, and V_FDG were 0.59±0.32 cm³ (range,0.13~1.64 cm³), 0.61±0.37 cm³ (range,0.15~1.86 cm³), and 1.24±0.53 cm³ (range,0.17~2.20 cm³), respectively. V_Path vs. V_RGD, V_Path vs. V_FDG, and V_RGD vs. V_FDG comparisons were t = -0.145, P = 0.885, t = -6.239, P<0.001, and t = -5.661, P<0.001, respectively. No significant difference was found between V_Path and V_RGD. V_FDG was much larger than V_RGD and V_Path. V_RGD seemed more approximate to the pathologic gross tumor volume. Furthermore, V_Path was more strongly correlated with V_RGD (R = 0.964,P<0.001) than with V_FDG (R = 0.584,P<0.001).

Conclusions

¹⁸F-alfatide PET provided a better estimation of gross tumor volume than ¹⁸F-FDG PET in LLC tumor-bearing C57BL/6 mice.

PETSTEP: Generation of synthetic PET lesions for fast evaluation of segmentation methods

Purpose

This work describes PETSTEP (PET Simulator of Tracers via Emission Projection): a faster and more accessible alternative to Monte Carlo (MC) simulation generating realistic PET images, for studies assessing image features and segmentation techniques.

Methods

PETSTEP was implemented within Matlab as open source software. It allows generating three-dimensional PET images from PET/CT data or synthetic CT and PET maps, with user-drawn lesions and user-set acquisition and reconstruction parameters. PETSTEP was used to reproduce images of the NEMA body phantom acquired on a GE Discovery 690 PET/CT scanner, and simulated with MC for the GE Discovery LS scanner, and to generate realistic Head and Neck scans. Finally the sensitivity (S) and Positive Predictive Value (PPV) of three automatic segmentation methods were compared when applied to the scanner-acquired and PETSTEP-simulated NEMA images.

Results

PETSTEP produced 3D phantom and clinical images within 4 and 6 min respectively on a single core 2.7 GHz computer. PETSTEP images of the NEMA phantom had mean intensities within 2% of the scanner-acquired image for both background and largest insert, and 16% larger background Full Width at Half Maximum. Similar results were obtained when comparing PETSTEP images to MC simulated data. The S and PPV obtained with simulated phantom images were statistically significantly lower than for the original images, but led to the same conclusions with respect to the evaluated segmentation methods.

Conclusions

PETSTEP allows fast simulation of synthetic images reproducing scanner-acquired PET data and shows great promise for the evaluation of PET segmentation methods.

Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors

PURPOSE

To determine whether quantitative imaging features from pretreatment positron emission tomography (PET) can enhance patient overall survival risk stratification beyond what can be achieved with conventional prognostic factors in patients with stage III non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

The institutional review board approved this retrospective chart review study and waived the requirement to obtain informed consent. The authors retrospectively identified 195 patients with stage III NSCLC treated definitively with radiation therapy between January 2008 and January 2013. All patients underwent pretreatment PET/computed tomography before treatment. Conventional PET metrics, along with histogram, shape and volume, and co-occurrence matrix features, were extracted. Linear predictors of overall survival were developed from leave-one-out cross-validation. Predictive Kaplan-Meier curves were used to compare the linear predictors with both quantitative imaging features and conventional prognostic factors to those generated with conventional prognostic factors alone. The Harrell concordance index was used to quantify the discriminatory power of the linear predictors for survival differences of at least 0, 6, 12, 18, and 24 months. Models were generated with features present in more than 50% of the cross-validation folds.

RESULTS

Linear predictors of overall survival generated with both quantitative imaging features and conventional prognostic factors demonstrated improved risk stratification compared with those generated with conventional prognostic factors alone in terms of log-rank statistic (P = .18 vs P = .0001, respectively) and concordance index (0.62 vs 0.58, respectively). The use of quantitative imaging features selected during cross-validation improved the model using conventional prognostic factors alone (P = .007). Disease solidity and primary tumor energy from the co-occurrence matrix were found to be selected in all folds of cross-validation.

CONCLUSION

Pretreatment PET features were associated with overall survival when adjusting for conventional prognostic factors in patients with stage III NSCLC.

Validation of functional imaging as a biomarker for radiation treatment response

Major advances in radiotherapy techniques, increasing knowledge of tumour biology and the ability to translate these advances into new therapeutic approaches are important goals towards more individualized cancer treatment. With the development of non-invasive functional and molecular imaging techniques such as positron emission tomography (PET)-CT scanning and MRI, there is now a need to evaluate potential new biomarkers for tumour response prediction, for treatment individualization is not only based on morphological criteria but also on biological tumour characteristics. The goal of individualization of radiotherapy is to improve treatment outcome and potentially reduce chronic treatment toxicity. This review gives an overview of the molecular and functional imaging modalities of tumour hypoxia and tumour cell metabolism, proliferation and perfusion as predictive biomarkers for radiation treatment response in head and neck tumours and in lung tumours. The current status of knowledge on integration of PET/CT/MRI into treatment management and bioimage-guided adaptive radiotherapy are discussed.

A Segmentation Algorithm for Quantitative Analysis of Heterogeneous Tumors of the Cervix With ¹⁸F-FDG PET/CT

As positron-emission tomography (PET) images have low spatial resolution and much noise, accurate image segmentation is one of the most challenging issues in tumor quantification. Tumors of the uterine cervix present a particular challenge because of urine activity in the adjacent bladder. Here, we propose and validate an automatic segmentation method adapted to cervical tumors. Our proposed methodology combined the gradient field information of both the filtered PET image and the level set function into a level set framework by constructing a new evolution equation. Furthermore, we also constructed a new hyperimage to recognize a rough tumor region using the fuzzy c-means algorithm according to the tissue specificity as defined by both PET (uptake) and computed tomography (attenuation) to provide the initial zero level set, which could make the segmentation process fully automatic. The proposed method was verified based on simulation and clinical studies. For simulation studies, seven different phantoms, representing tumors with homogenous/heterogeneous-low/high uptake patterns and different volumes, were simulated with five different noise levels. Twenty-seven cervical cancer patients at different stages were enrolled for clinical evaluation of the method. Dice similarity coefficients (DSC) and Hausdorff distance (HD) were used to evaluate the accuracy of the segmentation method, while a Bland-Altman analysis of the mean standardized uptake value (SUVmean) and metabolic tumor volume (MTV) was used to evaluate the accuracy of the quantification. Using this method, the DSCs and HDs of the homogenous and heterogeneous phantoms under clinical noise level were 93.39 ±1.09% and 6.02 ±1.09 mm, 93.59 ±1.63% and 8.92 ±2.57 mm, respectively. The DSCs and HDs in patients measured 91.80 ±2.46% and 7.79 ±2.18 mm. Through Bland-Altman analysis, the SUVmean and the MTV using our method showed high correlation with the clinical gold standard. The results of both simulation and clinical studies demonstrated the accuracy, effectiveness, and robustness of the proposed method. Further assessment of the quantitative indices indicates the feasibility of this algorithm in accurate quantitative analysis of cervical tumors in clinical practice.

PET-CT guided curative conformal radiation therapy in limited stage small cell lung cancer

BACKGROUND

The prognosis of small cell lung cancer (SCLC) has been improving with the advances in diagnostic and therapeutic modalities. Positron emission tomography/computed tomography (FDG-PET/CT) which has been studied in non-small cell lung cancer (NSCLC) for a long time, and it has only recently been applied to SCLC. Therefore we sought to observe firstly the prognostic importance of the FDG uptake in limited disease small cell lung cancer (LD-SCLC) patients and secondly the clinical outcomes and toxicity profiles of LD-SCLC patients treated with conformal radiation therapy (RT) using FDG-PET/CT simulation.

METHODS

Between 2009 and 2011, 33 LD-SCLC patients with LD-SCLC underwent disease staging using FDG-PET/CT conformal RT. Thoracic radiation was administered at a daily fraction of 2 Gy. Total dose was prescribed according to the treatment protocol such as, concurrent or sequential chemotherapy and in some patients according to the response of CT. All patients underwent chemotherapy. Survival was estimated using the Kaplan-Meier method.

RESULTS

The median age of the patients was 58 years (range, 38-77 years). The median follow-up time was 20 months (range, 6.6-47.6 months). The 3-year overall survival (OS) and locoregional control rates were 23% and 48%, respectively.

CONCLUSIONS

There are few studies examining the impact of PET-CT and the prognostic significance of FDG-uptake on outcomes in patients with LD-SCLC. Higher RT doses in response to higher FDG uptake may be safely applied for the purpose of locoregional control.

PET/CT imaging for target volume delineation in curative intent radiotherapy of non-small cell lung cancer: IAEA consensus report 2014

This document describes best practice and evidence based recommendations for the use of FDG-PET/CT for the purposes of radiotherapy target volume delineation (TVD) for curative intent treatment of non-small cell lung cancer (NSCLC). These recommendations have been written by an expert advisory group, convened by the International Atomic Energy Agency (IAEA) to facilitate a Coordinated Research Project (CRP) aiming to improve the applications of PET based radiation treatment planning (RTP) in low and middle income countries. These guidelines can be applied in routine clinical practice of radiotherapy TVD, for NSCLC patients treated with concurrent chemoradiation or radiotherapy alone, where FDG is used, and where a calibrated PET camera system equipped for RTP patient positioning is available. Recommendations are provided for PET and CT image visualization and interpretation, and for tumor delineation using planning CT with and without breathing motion compensation.

PET image segmentation using a Gaussian mixture model and Markov random fields

Background

Classification algorithms for positron emission tomography (PET) images support computational treatment planning in radiotherapy. Common clinical practice is based on manual delineation and fixed or iterative threshold methods, the latter of which requires regression curves dependent on many parameters.

Methods

An improved statistical approach using a Gaussian mixture model (GMM) is proposed to obtain initial estimates of a target volume, followed by a correction step based on a Markov random field (MRF) and a Gibbs distribution to account for dependencies among neighboring voxels. In order to evaluate the proposed algorithm, phantom measurements of spherical and non-spherical objects with the smallest diameter being 8 mm were performed at signal-to-background ratios (SBRs) between 2.06 and 9.39. Additionally ⁶⁸Ga-PET data from patients with lesions in the liver and lymph nodes were evaluated.

Results

The proposed algorithm produces stable results for different reconstruction algorithms and different lesion shapes. Furthermore, it outperforms all threshold methods regarding detection rate, determines the spheres’ volumes more accurately than fixed threshold methods, and produces similar values as iterative thresholding. In a comparison with other statistical approaches, the algorithm performs equally well for larger volumes and even shows improvements for small volumes and SBRs. The comparison with experts’ manual delineations on the clinical data shows the same qualitative behavior as for the phantom measurements.

Conclusions

In conclusion, a generic probabilistic approach that does not require data measured beforehand is presented whose performance, robustness, and swiftness make it a feasible choice for PET segmentation.

The role of PET-CT in radiotherapy planning of solid tumours

Background

PET-CT is becoming more and more important in various aspects of oncology. Until recently it was used mainly as part of diagnostic procedures and for evaluation of treatment results. With development of personalized radiotherapy, volumetric and radiobiological characteristics of individual tumour have become integrated in the multistep radiotherapy (RT) planning process. Standard anatomical imaging used to select and delineate RT target volumes can be enriched by the information on tumour biology gained by PET-CT. In this review we explore the current and possible future role of PET-CT in radiotherapy treatment planning. After general explanation, we assess its role in radiotherapy of those solid tumours for which PET-CT is being used most.

Conclusions

In the nearby future PET-CT will be an integral part of the most radiotherapy treatment planning procedures in an every-day clinical practice. Apart from a clear role in radiation planning of lung cancer, with forthcoming clinical trials, we will get more evidence of the optimal use of PET-CT in radiotherapy planning of other solid tumours.

Feasibility of in situ, high-resolution correlation of tracer uptake with histopathology by quantitative autoradiography of biopsy specimens obtained under 18F-FDG PET/CT guidance

Core biopsies obtained using PET/CT guidance contain bound radiotracer and therefore provide information about tracer uptake in situ. Our goal was to develop a method for quantitative autoradiography of biopsy specimens (QABS), to use this method to correlate (18)F-FDG tracer uptake in situ with histopathology findings, and to briefly discuss its potential application.

METHODS

Twenty-seven patients referred for a PET/CT-guided biopsy of (18)F-FDG-avid primary or metastatic lesions in different locations consented to participate in this institutional review board-approved study, which complied with the Health Insurance Portability and Accountability Act. Autoradiography of biopsy specimens obtained using 5 types of needles was performed immediately after extraction. The response of autoradiography imaging plates was calibrated using dummy specimens with known activity obtained using 2 core-biopsy needle sizes. The calibration curves were used to quantify the activity along biopsy specimens obtained with these 2 needles and to calculate the standardized uptake value, SUVARG. Autoradiography images were correlated with histopathologic findings and fused with PET/CT images demonstrating the position of the biopsy needle within the lesion. Logistic regression analysis was performed to search for an SUVARG threshold distinguishing benign from malignant tissue in liver biopsy specimens. Pearson correlation between SUVARG of the whole biopsy specimen and average SUVPET over the voxels intersected by the needle in the fused PET/CT image was calculated.

RESULTS

Activity concentrations were obtained using autoradiography for 20 specimens extracted with 18- and 20-gauge needles. The probability of finding malignancy in a specimen is greater than 50% (95% confidence) if SUVARG is greater than 7.3. For core specimens with preserved shape and orientation and in the absence of motion, one can achieve autoradiography, CT, and PET image registration with spatial accuracy better than 2 mm. The correlation coefficient between the mean specimen SUVARG and SUVPET was 0.66.

CONCLUSION

Performing QABS on core-biopsy specimens obtained using PET/CT guidance enables in situ correlation of (18)F-FDG tracer uptake and histopathology on a millimeter scale. QABS promises to provide useful information for guiding interventional radiology procedures and localized therapies and for in situ high-spatial-resolution validation of radiopharmaceutical uptake.