Is the standard uptake value (SUV) appropriate for quantification in clinical PET imaging? - Variability induced by different SUV measurements and varying reconstruction methods

Comparison of 18F-FDG PET image quality and quantitative parameters between DPR and OSEM reconstruction algorithm in patients with lung cancer

OBJECTIVES

The present study aimed to investigate the influence of the deep progressive learning reconstruction (DPR) algorithm on the 18F-FDG PET image quality and quantitative parameters.

METHODS

In this retrospective study, data were collected from 55 healthy individuals and 184 patients with primary malignant pulmonary tumors who underwent 18F-FDG PET/CT examinations. PET data were reconstructed using the ordered subset expectation maximization (OSEM) and DPR algorithms. The influence of DPR algorithm on quantitative parameters was explored, including the SUVmax, SUVmean, standard deviation of SUV (SUVSD), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and tumor-to-background uptake ratio (TBR). Finally, the differences in image quality parameters, including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), between the two reconstruction algorithms were evaluated.

RESULTS

DPR algorithm significantly reduced the SUVmax and SUVSD of background tissues (all, P < 0.001) compared to OSEM algorithm, while no statistical difference was observed in SUVmean between the two algorithms (all, P > 0.05). DPR algorithm notably increased the SUVmax, SUVmean, and TBR of lesions (all, P < 0.001) and reduced MTV (P = 0.005), with minimal differences in TLG noted between the reconstruction algorithms (P < 0.001). The percentage differences in SUVmax (P = 0.001), SUVmean (P = 0.005), and TBR (P = 0.001) between the two algorithms were significantly higher in solid nodules than in pure ground glass nodules (pGGNs). The ΔCNR between solid nodules (P = 0.031) and mixed ground glass nodules (P = 0.020) was greater than that between pGGNs. SNR and CNR obtained using the DPR algorithm were markedly improved compared to those determined using the OSEM algorithm (all, P < 0.001).

CONCLUSION

Under identical acquisition conditions, the DPR algorithm enhanced the accuracy of quantitative parameters in pulmonary lesions and potentially improved lesion detectability. The DPR algorithm increased image SNR and CNR compared to those obtained using the OSEM algorithm, significantly optimizing overall image quality. This advancement facilitated precise clinical diagnosis, underpinning its potential to significantly contribute to the field of medical imaging.

Novel PET-CT-MR Imaging Based Quantitative Technique for Accurate Assessment of Radiation Induced Injuries

Radiation-induced injuries (RIIs) are significant complications of radiation therapy used in cancer treatments and affect organs in a systemic fashion such as the heart, lungs, liver, and bone marrow. Such ionizing radiation leads to inflammation, fibrosis, and/or irreparable DNA damage, each of which can significantly impact patient's quality of life, underscoring the need for advanced diagnostic and imaging techniques. A novel combination of PET/Computed Tomography (CT) with Quantitative MR Imaging has emerged as a crucial tool for early diagnosis and timely evaluation of RIIs. This review focuses on the important role of quantitative PET-CT-MR imaging in diagnosing and monitoring RIIs.

Investigating the Impact of Voxel Size and Postfiltering on Quantitative Analysis of Positron Emission Tomography/Computed Tomography: A Phantom Study

Aim

This study aims to investigate the influence of voxel size and postfiltering on the quantification of standardized uptake value (SUV) in positron emission tomography/computed tomography (PET/CT) images.

Materials and Methods

National Electrical Manufacturers Association phantom with the spheres of different sizes were utilized to simulate the lesions. The phantom was scanned using a PET/CT scanner, and the acquired images were reconstructed using two different matrix sizes, (192 × 192) and (256 × 256), and a wide range of postfiltering values.

Results

The findings demonstrated that postfiltering significantly affected SUV measurements. The changes in postfiltering values can result in overestimation or underestimation of SUV values, highlighting the importance of carefully selecting appropriate filters. Increasing the matrix size improved SUVmax and SUVmean values, particularly for small-sized spheres. Smaller voxel reconstructions slightly reduced partial volume effects and partially enhanced SUV quantification.

Conclusions

Careful consideration of postfiltering values and matrix size selection can lead to better SUV quantification. These findings emphasize the need to optimize the reconstruction parameters to enhance the clinical utility of PET/CT in detecting and evaluating malignant lesions.

Predictive value of 2-deoxy-2-fluorine-18-fluoro-D-glucose positron emission tomography/computed tomography parameters for MYCN amplification in high-risk neuroblastoma

OBJECTIVES

To investigate the predictive value of 2-deoxy-2-fluorine-18-fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) parameters for MYCN amplification in high-risk neuroblastoma (HR-NB).

MATERIALS AND METHODS

A retrospective analysis was performed by reviewing 68 HR-NB patients who underwent MYCN testing and 18F-FDG PET/CT imaging at our hospital between January 2018 and December 2019. Based on the results of MYCN testing, patients were categorized into either the MYCN-amplified (MNA) or MYCN non-amplified (MYCN-NA) group. The 18F-FDG PET/CT parameters, including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), peak standardized uptake value (SUVpeak), tumor metabolic volume (MTV), total lesion glycolysis (TLG), coefficient of variation (COV), and areas under the curve of cumulative SUV-volume histogram index (AUC-CSH index) were evaluated. Independent predictors were identified through univariate and multivariate logistic regression analyses, and their diagnostic performance was evaluated using the receiver-operating characteristic (ROC) curve.

RESULTS

Univariate logistic regression analysis revealed that SUVpeak was significantly associated with MYCN amplification. Multivariate logistic regression analysis showed that SUVpeak was an independent predictor of MYCN amplification in HR-NB [Odds ratio (OR) = 0.673, 95 % confidence interval (95 % CI): 0.494-0.917, P = 0.012]. ROC curve analysis demonstrated that the predictive model including SUVpeak had higher diagnostic performance [area under the curve (AUC): 0.790, 95 % CI: 0.677-0.881, sensitivity: 0.861, specificity: 0.591, positive predictive value (PPV): 0.820, negative predictive value (NPV): 0.722] compared to using SUVpeak alone (AUC: 0.640, 95 % CI: 0.514-0.752, sensitivity: 0.630, specificity: 0.682, PPV: 0.806, NPV: 0.469).

CONCLUSION

SUVpeak can predict the MYCN amplification in HR-NB patients. The predictive model constructed by combining SUVpeak and age can distinguish MYCN status in HR-NB non-invasively with superior efficacy compared to using SUVpeak alone.

The variation of quantitative parameters and Deauville scores with different reconstruction algorithms in FDG PET/CT imaging of lymphoma patients

INTRODUCTION AND OBJECTIVES

18F-FDG PET with the Deauville score (DS) is a unique semiquantitative method for lymphoma. However, type of standard uptake values (max, mean, and peak) reconstruction algorithms could affect DS. We compared the Bayesian Penalized Likelihood reconstruction algorithm (BPL) with Ordered Subsets Expectation Maximization (OSEM) on quantitative parameters and DS in lymphoma. We investigated the effect of the size of the lymph node on quantitative variation.

PATIENTS AND METHODS

Raw PET data of 255 lymphoma patients were reconstructed separately with Q.Clear (GE Healthcare), a BPL, and SharpIR (GE Healthcare), an OSEM algorithm. In both images, each patient's liver, mediastinal blood pool, and SUVs (SUVmax, SUVmean, and SUVpeak) of a total of 487 lesions selected from the patients were performed. DSmax, DSmean, and DSpeak were compared.

RESULTS

In our study, DS increased significantly with BPL (p < 0.001), and the DS increased to 4-5 in thirty patients evaluated as 1-2-3 with OSEM. It was found that the quantitative values of the lymph nodes increased statistically with BPL (p < 0.001), and the liver from the reference regions were significantly decreased (p < 0.001). In addition, difference in lymph node was independently associated with size of lesion and was significantly more pronounced in small lesions (p < 0.001). The effects of BPL algorithm were more pronounced in SUVmax than in SUVmean and SUVpeak. DS-mean and DS-peak scores were less changed by BPL than DS-max.

CONCLUSION

Different reconstruction algorithms in FDG PET/CT affect the quantitative evaluation. That variation may affect the change in DS in lymphoma patients, thus affecting patient management.

Post-acquisition standardization of positron emission tomography images

Purpose

Tissue radiotracer activity measured from positron emission tomography (PET) images is an important biomarker that is clinically utilized for diagnosis, staging, prognostication, and treatment response assessment in patients with cancer and other clinical disorders. Using PET image values to define a normal range of metabolic activity for quantification purposes is challenging due to variations in patient-related factors and technical factors. Although the formulation of standardized uptake value (SUV) has compensated for some of these variabilities, significant non-standardness still persists. We propose an image processing method to substantially mitigate these variabilities.

Methods

The standardization method is similar for activity concentration (AC) PET and SUV PET images with some differences and consists of two steps. The calibration step is performed only once for each of AC PET or SUV PET, employs a set of images of normal subjects, and requires a reference object, while the transformation step is executed for each patient image to be standardized. In the calibration step, a standardized scale is determined along with 3 key image intensity landmarks defined on it including the minimum percentile intensitys min , median intensitys m , and high percentile intensitys max . s min ands m are estimated based on image intensities within the body region in the normal calibration image set. The optimal value of the maximum percentile β corresponding to the intensitys max is estimated via an optimization process by using the reference object to optimally separate the highly variable high uptake values from the normal uptake intensities. In the transformation step, the first two landmarks-the minimum percentile intensityp α ( I ) , and the median intensityp m ( I ) -are found for the given image I for the body region, and the high percentile intensityp β ( I ) is determined corresponding to the optimally estimated high percentile value β . Subsequently, intensities of I are mapped to the standard scale piecewise linearly for different segments. We employ three strategies for evaluation and comparison with other standardization methods: (i) comparing coefficient of variationC V O of mean intensity within test objects O across different normal test subjects before and after standardization; (ii) comparing mean absolute difference (MD O ) of mean intensity within test objects O across different subjects in repeat scans before and after standardization; (iii) comparingC V O of mean intensity across different normal subjects before and after standardization where the scans came from different brands of scanners.

Results

Our data set consisted of 84 FDG-PET/CT scans of the body torso including 38 normal subjects and two repeat-scans of 23 patients. We utilized one of two objects-liver and spleen-as a reference object and the other for testing. The proposed standardization method reducedC V O andMD O by a factor of 3-8 in comparison to other standardization methods and no standardization. Upon standardization by our method, the image intensities (both for AC and SUV) from two different brands of scanners become statistically indistinguishable, while without standardization, they differ significantly and by a factor of 3-9.

Conclusions

The proposed method is automatic, outperforms current standardization methods, and effectively overcomes the residual variation left over in SUV and inter-scanner variations.

A proper protocol for routine 18F-FDG uEXPLORER total-body PET/CT scans

BACKGROUND

Conventional clinical PET scanners typically have an axial field of view (AFOV) of 15-30 cm, resulting in limited coverage and relatively low photon detection efficiency. Taking advantage of the development of long-axial PET/CT, the uEXPLORER PET/CT scanner with an axial coverage of 194 cm increases the effective count rate by approximately 40 times compared to that of conventional PET scanners. Ordered subset expectation maximization (OSEM) is the most widely used iterative algorithm in PET. The major drawback of OSEM is that the iteration process must be stopped before convergence to avoid image degradation due to excessive noise. A new Bayesian penalized-likelihood iterative PET reconstruction, named HYPER iterative, was developed and is now available on the uEXPLORER total-body PET/CT, which incorporates a noise control component by using a penalty function in each iteration and finds the maximum likelihood solution through repeated iterations. To date, its impact on lesion visibility in patients with a full injected dose or half injected dose is unclear. The goal of this study was to determine a proper protocol for routine 18F-FDG uEXPLORER total-body PET/CT scans.

RESULTS

The uEXPLORER total-body PET/CT images reconstructed using both OSEM and HYPER iterative algorithms of 20 tumour patients were retrospectively reviewed. The quality of the 5 min PET image was excellent (score 5) for all of the dose and reconstruction methods. Using the HYPER iterative method, the PET images reached excellent quality at 1 min with full-dose PET and at 2 min with half-dose PET. The PET image reached a similar excellent quality at 2 min with a full dose and at 3 min with a half dose using OSEM. The noise in the OSEM reconstruction was higher than that in the HYPER iterative. Compared to OSEM, the HYPER iterative had a slightly higher SUVmax and TBR of the lesions for large positive lesions (≥ 2 cm) (SUVmax: up to 9.03% higher in full dose and up to 12.52% higher in half dose; TBR: up to 8.69% higher in full dose and up to 23.39% higher in half dose). For small positive lesions (≤ 10 mm), the HYPER iterative had an obviously higher SUVmax and TBR of the lesions (SUVmax: up to 45.21% higher in full dose and up to 74.96% higher in half dose; TBR: up to 44.91% higher in full dose and up to 93.73% higher in half dose).

CONCLUSIONS

A 1 min scan with a full dose and a 2 min scan with a half dose are optimal for clinical diagnosis using the HYPER iterative and 2 min and 3 min for OSEM. For quantification of the small lesions, HYPER iterative reconstruction is preferred.

Pelvic PET/MR attenuation correction in the image space using deep learning

Introduction

The five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction.

Methods

A convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction. Dixon MR images and the four-class attenuation correction µ-map were used as input to the models. CT and PET/MR examinations for 22 patients ([18F]FDG) were used for training and validation, and 17 patients were used for testing (6 [18F]PSMA-1007 and 11 [68Ga]Ga-PSMA-11). A quantitative analysis of PSMA uptake using voxel- and lesion-based error metrics was used to assess performance.

Results

In the voxel-based analysis, the proposed model reduced the median root mean squared percentage error from 12.1% and 8.6% for the four- and five-class Dixon-based AC methods, respectively, to 6.2%. The median absolute percentage error in the maximum standardized uptake value (SUVmax) in bone lesions improved from 20.0% and 7.0% for four- and five-class Dixon-based AC methods to 3.8%.

Conclusion

The proposed method reduces the voxel-based error and SUVmax errors in bone lesions when compared to the four- and five-class Dixon-based AC models.

PET-CTBased Quantitative Parameters for Assessment of Treatment Response and Disease Activity in Cancer and Noncancerous Disorders

The various semiquantitative and quantitative PET-CT parameters provide measurement of disease activity and assessment of treatment response in the PET-CT studies. These include standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and total metabolic tumor volume (TMTV). Thresholding and adaptive thresholding methods are commonly used algorithms for the evaluation of global disease activity. Readily available commercial software frequently in-built with the current generation PET-CT scanners for providing easy, less time consuming, highly reproducible, and more accurate measurement of global disease activity on PET-CT imaging in evaluation of malignant as well as benign disorders.

PET metabolic tumor volume as a new prognostic factor in childhood rhabdomyosarcoma

PURPOSE

Childhood RMS is a rare malignant disease in which evaluation of tumour spread at diagnosis is essential for therapeutic management. F-18 FDG-PET imaging is currently used for initial RMS disease staging.

MATERIALS AND METHODS

This multicentre retrospective study in six French university hospitals was designed to analyse the prognostic accuracy of MTV at diagnosis for patients with RMS between 1 January 2007 and 31 October 2017, for overall (OS) and progression-free survival (PFS). MTV was defined as the sum of the primitive tumour and the largest metastasis, where relevant, with a 40% threshold of the primary tumour SUVmax. Additional aims were to define the prognostic value of SUVmax, SUVpeak, and bone lysis at diagnosis.

RESULTS

Participants were 101 patients with a median age of 7.4 years (IQR [4.0-12.5], 62 boys), with localized disease (35 cases), regional nodal spread (43 cases), or distant metastases (23). 44 patients had alveolar subtypes. In a univariate analysis, a MTV greater than 200 cm3 was associated with OS (HR = 3.47 [1.79;6.74], p<0.001) and PFS (HR = 3.03 [1.51;6.07], p = 0.002). SUVmax, SUVpeak, and bone lysis also influenced OS (respectively p = 0.005, p = 0.004 and p = 0.007) and PFS (p = 0.029, p = 0.019 and p = 0.015). In a multivariate analysis, a MTV greater than 200 cm3 was associated with OS (HR = 2.642 [1.272;5.486], p = 0.009) and PFS (HR = 2.707 [1.322;5.547], p = 0.006) after adjustment for confounding factors, including SUVmax, SUVpeak, and bone lysis.

CONCLUSION

A metabolic tumor volume greater than 200 cm3, SUVmax, SUVpeak, and bone lysis in the pre-treatment assessment were unfavourable for outcome.

Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper

Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.

Impact of time of flight and point spread function on quantitative parameters of lung lesions in 18F-FDG PET/CT

Background

Image reconstruction algorithm is one of the important factors affecting the quantitative parameters of PET/CT. The purpose of this study was to investigate the effects of time of flight (TOF) and point spread function (PSF) on quantitative parameters of lung lesions in ¹⁸F-FDG PET/CT.

Methods

This retrospective study evaluated 60 lung lesions in 39 patients who had undergone ¹⁸F-fluoro-deoxy-glucose (FDG) PET/CT. All lesions larger than 10 mm in diameter were included in the study. The PET data were reconstructed with a baseline ordered-subsets expectation–maximization (OSEM) algorithm, OSEM + PSF, OSEM + TOF and OSEM + TOF + PSF respectively. The differences of maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG)and signal to noise ratio (SNR)were compared among different reconstruction algorithms.

Results

Compared with OSEM reconstruction, using OSEM + TOF + PSF increased SUVmean and SUVmax by 23.73% and 22.71% respectively, and SNR increased by 70.18%, MTV decreased by 23.84% (p < 0.01). The percentage difference was significantly higher in smaller lesions (diameter 10–22 mm) than in larger lesions (diameter 23–44 mm), and significantly higher in low contrast lesions (SNR ≤ 15.31) than in high contrast lesions (SNR > 15.31). The difference of TLG among various reconstruction algorithms is relatively small, the highest value is − 6.48% of OSEM + TOF + PSF, and the lowest value is 0.81% of OSEM + TOF.

Conclusion

TOF and PSF significantly affected the quantitative parameters of lung lesions in ¹⁸F-FDG PET/CT. OSEM + TOF + PSF can significantly increased SUVmax, SUVmean and SNR, and significantly reduce MTV, especially in small lesions and low contrast lesions. TLG can be relatively stable in different reconstruction algorithms.

Differences among [18F]FDG PET-derived parameters in lung cancer produced by three software packages

Investigation of differences in derived [18F]FDG PET metabolic and volumetric parameters among three different software programs in lung cancer. A retrospective analysis was performed on a group of 98 lung cancer patients who underwent a baseline [18F]FDG PET/CT study. To assess appropriate delineation methods, the NEMA phantom study was first performed using the following software: Philips EBW (Extended Brilliance Workstation), MIM Software and Rover. Based on this study, the best cut-off methods (dependent on tumour size) were selected, extracted and applied for lung cancer delineation. Several semiquantitative [18F]FDG parameters (SUVmax, SUVmean, TLG and MTV) were assessed and compared among the three software programs. The parameters were assessed based on body weight (BW), lean body mass (LBM) and Bq/mL. Statistically significant differences were found in SUVmean (LBM) between MIM Software and Rover (4.62 ± 2.15 vs 4.84 ± 1.20; p < 0.005), in SUVmean (Bq/mL) between Rover and Philips EBW (21,852.30 ± 21,821.23 vs 19,274.81 ± 13,340.28; p < 0.005) and Rover and MIM Software (21,852.30 ± 21,821.23 vs 19,399.40 ± 10,051.30; p < 0.005), and in MTV between MIM Software and Philips EBW (19.87 ± 25.83 vs 78.82 ± 228.00; p = 0.0489). This study showed statistically significant differences in the estimation of semiquantitative parameters using three independent image analysis tools. These findings are important for performing further diagnostic and treatment procedures in lung cancer patients.

Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography?

Purpose

This study aims to provide a detailed investigation on the noise penalization factor in Bayesian penalized likelihood (BPL)‐based algorithm, with the utilization of partial volume effect correction (PVC), so as to offer the suitable beta value and optimum standardized uptake value (SUV) parameters in clinical practice for small pulmonary nodules.

Methods

A National Electrical Manufacturers Association (NEMA) image‐quality phantom was scanned and images were reconstructed using BPL with beta values ranged from 100 to 1000. The recovery coefficient (RC), contrast recovery (CR), and background variability (BV) were measured to assess the quantification accuracy and image quality. In the clinical assessment, lesions were categorized into sub‐centimeter (<10 mm, n = 7) group and medium size (10–30 mm, n = 16) group. Signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR) were measured to evaluate the image quality and lesion detectability. With PVC was performed, the impact of beta values on SUVs (SUVmax, SUVmean, SUVpeak) of small pulmonary nodules was evaluated. Subjective image analysis was performed by two experienced readers.

Results

With the increasing of beta values, RC, CR, and BV decreased gradually in the phantom work. In the clinical study, SNR and CNR of both groups increased with the beta values (P < 0.001), although the sub‐centimeter group showed increases after the beta value reached over 700. In addition, highly significant negative correlations were observed between SUVs and beta values for both lesion‐size groups before the PVC (P < 0.001 for all). After the PVC, SUVpeak measured from the sub‐centimeter group was no significantly different among different beta values (P = 0.830).

Conclusion

Our study suggests using SUVpeak as the quantification parameter with PVC performed to mitigate the effects of beta regularization. Beta values between 300 and 400 were preferred for pulmonary nodules smaller than 30 mm.

PET Parameters are Useful in Predicting Endometrial Cancer Risk Classes and Prognosis

AIM

 We investigate the role of preoperative PET parameters to determine risk classes and prognosis of endometrial cancer (EC).

METHODS

 We enrolled 81 patients with EC who underwent preoperative F-18 FDG PET/CT. PET parameters (SUVmax, SUVmean, MTV, TLG), grade, histology and size of the primary tumor, stage of the disease, the degree of myometrial invasion (MI), and the presence of lymphovascular invasion (LVI), cervical invasion (CI), distant metastasis (DM) and lymph node metastasis (LNM) were recorded. The relationship between PET parameters, clinicopathological risk factors and overall survival (OS) was evaluated.

RESULTS

 The present study included 81 patients with EC (mean age 60). Of the total sample, 21 patients were considered low risk (endometrioid histology, stage 1A, grade 1 or 2, tumor diameter < 4 cm, and LVI negative) and 60 were deemed high risk. All of the PET parameters were higher in the presence of a high-risk state, greater tumor size, deep MI, LVI and stage 1B-4B. MTV and TLG values were higher in the patients with non-endometrioid histology, CI, grade 3 and LNM. The optimum cut-off levels for differentiating between the high and low risk patients were: 11.1 for SUVmax (AUC = 0.757), 6 for SUVmean (AUC = 0.750), 6.6 for MTV(AUC = 0.838) and 56.2 for TLG(AUC = 0.835). MTV and TLG values were found as independent prognostic factors for OS, whereas SUVmax and SUVmean values were not predictive.

CONCLUSIONS

 The PET parameters are useful in noninvasively differentiating between risk groups of EC. Furthermore, volumetric PET parameters can be predictive for OS of EC.

CT texture analysis compared to Positron Emission Tomography (PET) and mutational status in resected melanoma metastases

PURPOSE

To evaluate the potential of CT texture analysis parameters and metabolic characteristics of melanoma metastases in ¹⁸F- FDG PET/CT to predict relevant mutations of tumour cells for targeted therapy in metastatic melanoma patients in correlation with histopathologic specimen.

MATERIAL AND METHODS

66 melanoma patients, examined with contrast-enhanced ¹⁸F-FDG PET/CT before scheduled metastasectomy without any prior systemic therapy, were included in this single-centre retrospective analysis under IRB waiver. The largest, resected metastasis in each patient was assessed with CT texture analysis and semiquantitative ¹⁸F-FDG PET parameters. Correlation between imaging parameters and histopathological mutations (BRAF- and NRAS- genes) were calculated.

RESULTS

Attenuation standard deviation (SD) within target lesion indicated a weak correlation with its SUV_peak (rho -0.292, p 0.017). However, no correlation between CT texture analysis, metabolic ¹⁸F-FDG PET parameters and tumour cell mutation could be established.

CONCLUSION

CT texture parameters cannot replace the diagnostic value of ¹⁸F- FDG PET/CT for metabolic information in melanoma patients. Discrimination between BRAF- and NRAS mutation status was not feasible with CT texture analysis in this exploratory study.

Timing of fluorodeoxyglucose positron emission tomography maximum standardized uptake value for diagnosis of local recurrence of non-small cell lung cancer after stereotactic body radiation therapy

Introduction

After treatment with stereotactic body radiation therapy (SBRT), local recurrence of non‐small cell cancer (NSCLC) can be difficult to differentiate from radiation‐induced changes. Maximum standardized uptake value (SUVmax), measured with 18‐F‐Fluorodeoxyglucose positron emission tomography (FDG‐PET), can have false positives due to acute radiation inflammation. The primary study objective was to determine the utility of SUVmax > 5 to identify local recurrence later than 9 months after SBRT.

Method

A retrospective review was performed of FDG‐PET scans for suspicious CT findings after SBRT treatment of stage 1 NSCLC. SUVmax was measured including surrounding opacification. Outcome measures were local recurrence, progression free survival, and overall survival. Receiver operator curve analysis, sensitivity, specificity, and Kaplan‐Meier analysis were performed.

Results

Of 118 patients treated, 42 patients had eligible FDG‐PET scans. They received SBRT (48‐60Gy in 3‐8 fractions) for 49 NSCLC and had 101 follow‐up PET scans. The median time to first PET scan was 9.3 months, and the median follow‐up period was 22.4 months. Local recurrence was diagnosed in 12 patients, at a median of 16 months. Due to selection bias, the included patients had poorer outcomes than the entire cohort, with progression free survival (PFS) at 1, 2, and 3 years of 82.7%, 57.8%, and 45.8%; and overall survival of 97.9%, 79.9%, and 59.1%, respectively. Thirty FDG‐PET scans were performed within 9 months, of which 17% were false positives. A total of 71 FDG‐PET scans were performed beyond 9 months, and the median SUVmax was significantly higher for patients with local recurrence (7.48 vs. 2.14, P < .0001). SUVmax > 5 has a sensitivity of 91% (95% CI 62%‐99.8%) and 100% (89.1%‐100%).

Conclusion

For local recurrence of NSCLC, SUVmax > 5 on FDG‐PET scan has good sensitivity and specificity after 6 months, but is highest beyond 9 months after SBRT.

Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks

BACKGROUND

Attenuation correction (AC) of PET data is usually performed using a second imaging for the generation of attenuation maps. In certain situations however-when CT- or MR-derived attenuation maps are corrupted or CT acquisition solely for the purpose of AC shall be avoided-it would be of value to have the possibility of obtaining attenuation maps only based on PET information. The purpose of this study was to thus develop, implement, and evaluate a deep learning-based method for whole body [18F]FDG-PET AC which is independent of other imaging modalities for acquiring the attenuation map.

METHODS

The proposed method is investigated on whole body [18F]FDG-PET data using a Generative Adversarial Networks (GAN) deep learning framework. It is trained to generate pseudo CT images (CTGAN) based on paired training data of non-attenuation corrected PET data (PETNAC) and corresponding CT data. Generated pseudo CTs are then used for subsequent PET AC. One hundred data sets of whole body PETNAC and corresponding CT were used for training. Twenty-five PET/CT examinations were used as test data sets (not included in training). On these test data sets, AC of PET was performed using the acquired CT as well as CTGAN resulting in the corresponding PET data sets PETAC and PETGAN. CTGAN and PETGAN were evaluated qualitatively by visual inspection and by visual analysis of color-coded difference maps. Quantitative analysis was performed by comparison of organ and lesion SUVs between PETAC and PETGAN.

RESULTS

Qualitative analysis revealed no major SUV deviations on PETGAN for most anatomic regions; visually detectable deviations were mainly observed along the diaphragm and the lung border. Quantitative analysis revealed mean percent deviations of SUVs on PETGAN of - 0.8 ± 8.6% over all organs (range [- 30.7%, + 27.1%]). Mean lesion SUVs showed a mean deviation of 0.9 ± 9.2% (range [- 19.6%, + 29.2%]).

CONCLUSION

Independent AC of whole body [18F]FDG-PET is feasible using the proposed deep learning approach yielding satisfactory PET quantification accuracy. Further clinical validation is necessary prior to implementation in clinical routine applications.

Application of integrated positron emission tomography/magnetic resonance imaging in evaluating the prognostic factors of head and neck squamous cell carcinoma with positron emission tomography, diffusion-weighted imaging, dynamic contrast enhancement and combined model

OBJECTIVES

This study was designed to investigate the distribution of the independent parameters of PET and MR in tumour differentiation and staging and to evaluate the diagnostic efficiency of the independent parameters and combined model of PET/MR in the tumour differentiation of head and neck squamous cell carcinoma (HNSCC).

METHODS

The patients with the preliminary diagnosis of HNSCC were included and underwent the integrated PET/MR The parameters included the diffusion-weighted imaging, dynamic contrast enhancement and PET. The correlations between different parameters and the distribution in groups of tumour differentiation and staging were analysed. The combined model was established with complementary PET/MR parameters. The diagnostic efficiency of the independent parameters and combined model in the tumour differentiation were analysed by receiver operating characteristic curve.

RESULTS

The correlations between the parameters of dynamic contrast enhancement and PET were most significant. There were significant differences between the well-differentiated group and the moderately/poorly differentiated group in terms of the mean values of apparent diffusion coefficient (ADC) and standardised uptake value (SUV) (p < 0.05). The distributions among different tumour stage groups were not statistically different in all the parameters. The diagnostic efficiency of tumour differentiation increased in the order of K_epmean, SUVmean, ADCmean, and the combined model.

CONCLUSIONS

Compared with the independent parameter, the combination of multiple parameters with PET/MR can further improve the diagnostic performance of tumour differentiation in HNSCC.

Evolution of heterotopic bone in fibrodysplasia ossificans progressiva: An [18F]NaF PET/CT study

Fibrodysplasia ossificans progressiva (FOP) is a rare, autosomal dominant disorder characterized by heterotopic ossification (HO) in muscles, ligaments and tendons. Flare-ups often precede the formation of HO, resulting in immobilization of joints. Due to progression of the disease without signs of a flare-up, co-existence of a chronic progression of HO has been postulated, but conclusive evidence is lacking. Recently, it has been shown that [¹⁸F]NaF PET/CT is able to identify early ossifying disease activity during flare-ups. Therefore, the purpose of the present study was to assess whether [¹⁸F]NaF PET/CT might also be able to identify the possible presence of chronic progressive HO in FOP. A total of thirteen [¹⁸F]NaF PET/CT scans from five FOP patients were analysed. Scans were acquired over a period of 0.5 to 2 years. Volumes of HO and standardized uptake values (SUV) were obtained based on manual segmentation of CT images. SUV_peak values, defined as the average SUV value of a 1 mL sphere containing the hottest voxel pixels, were obtained. Two out of five patients experienced ≥1 active clinical flare-ups at the time of the [¹⁸F]NaF PET/CT scan. In addition, in four out of five patients, serial scans showed radiological progression of HO (3 to 8 cm³), as assessed by CT volume, in the absence of a clinical flare-up. This volumetric increase was present in 6/47 (12.8%) of identified HO structures and, in all cases, was accompanied by increased [¹⁸F]NaF uptake, with SUV_peak ranging from 8.4 to 17.9. In conclusion, HO may progress without signs of a flare-up. [¹⁸F]NaF PET/CT is able to identify these asymptomatic, but progressive HO lesions, thereby demonstrating the presence of chronic activity in FOP. Consequently, future drugs should not only target new HO formation, but also this chronic HO progression.

Initial experience of dedicated breast PET imaging of ER+ breast cancers using [F-18]fluoroestradiol

Dedicated breast positron emission tomography (dbPET) is an emerging technology with high sensitivity and spatial resolution that enables detection of sub-centimeter lesions and depiction of intratumoral heterogeneity. In this study, we report our initial experience with dbPET using [F-18]fluoroestradiol (FES) in assessing ER+ primary breast cancers. Six patients with >90% ER+ and HER2- breast cancers were imaged with dbPET and breast MRI. Two patients had ILC, three had IDC, and one had an unknown primary tumor. One ILC patient was treated with letrozole, and another patient with IDC was treated with neoadjuvant chemotherapy without endocrine treatment. In this small cohort, we observed FES uptake in ER+ primary breast tumors with specificity to ER demonstrated in a case with tamoxifen blockade. FES uptake in ILC had a diffused pattern compared to the distinct circumscribed pattern in IDC. In evaluating treatment response, the reduction of SUVmax was observed with residual disease in an ILC patient treated with letrozole, and an IDC patient treated with chemotherapy. Future study is critical to understand the change in FES SUVmax after endocrine therapy and to consider other tracer uptake metrics with SUVmax to describe ER-rich breast cancer. Limitations include variations of FES uptake in different ER+ breast cancer diseases and exclusion of posterior tissues and axillary regions. However, FES-dbPET has a high potential for clinical utility, especially in measuring response to neoadjuvant endocrine treatment. Further development to improve the field of view and studies with a larger cohort of ER+ breast cancer patients are warranted.

Diagnostic value of positron-emission tomography textural indices for malignancy of 18F-fluorodeoxyglucose-avid adrenal lesions

BACKGROUND

PET Textural indices could have an add-on diagnostic value for diagnosis of malignancy in patients with FDG-avid adrenal lesions.

METHODS

Consecutive patients referred for a FDG-PET/CT to our nuclear medicine department from June 2012 to June 2017 were retrospectively screened. Inclusion criteria were: patients with a FDG-avid adrenal lesion (uptake≥liver background); malignant/benign lesion confirmed histologically or with follow-up imaging examination. Pheochromocytomas were not included in the analysis. For each adrenal lesion, 5 quantitative PET parameters (SUV<inf>max</inf>, MTV, TLG, TLR<inf>max</inf> and TLRmean<inf>)</inf> were calculated. Thirty-seven textural indices were extracted using LIFEx software^®. Diagnostic performance to determine malignancy was assessed with a ROC analysis. Parameters with a significantly AUC>0.5 were selected and groups of highly correlated (r>0.8) parameters were created. A scoring system combining PET and textural indices was examined.

RESULTS

PET textural indices were calculated for 53 lesions (37 malignant, 16 benign). Three PET metabolic parameters (SUV<inf>max</inf>, TLR<inf>max</inf>, TLRmean) and 13 textural indices had an AUC>0.5. Seven groups of highly correlated parameters (r>0.8) were extracted. For PET parameters, SUV<inf>max</inf> had the best AUC (0.89 95% CI [0.79-0.98]; cut-off=7.0). For textural indices, ZLNU had the best AUC (0.87 95% CI [0.78-0.96]; cut-off=34.7) and specificity of 100%. Three scores combining the best four textural indices alone (Contrast<inf>GLCM</inf>, LRHGE, SZE and ZLNU) or with one PET parameters (SUV<inf>max</inf>, TLR<inf>max</inf>) were developed but did not increase the diagnostic performance (AUC≤0.89). ZLNU was the best parameter to distinguish primary adrenal cancer from adrenal metastases in malignant lesions (P<0.001).

CONCLUSIONS

Our study highlighted excellent diagnostic performance of several PET textural indices comparable to that of PET metabolic parameters. However, our results did not find any additional diagnostic value of textural indices when combined with metabolic parameters.

qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68Ga-PSMA11 PET/CT

Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using ⁶⁸Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/CT images in DICOM format. Its pipeline was written using Python and C++ languages. A bone mask based on CT and a normal-uptake mask including organs with physiologic ⁶⁸Ga-PSMA11 uptake are automatically computed. An SUV threshold of 3 and a liver-based threshold are used to segment bone and soft-tissue lesions, respectively. Manual corrections can be applied using different tools. Multiple output parameters are computed, that is, PSMA ligand-positive tumor volume (PSMA-TV), PSMA ligand-positive total lesion (PSMA-TL), PSMA SUV_mean, and PSMA SUV_max Twenty ⁶⁸Ga-PSMA11 PET/CT data sets were used to validate and evaluate the performance characteristics of qPSMA. Four analyses were performed: validation of the semiautomatic algorithm for liver background activity determination, assessment of intra- and interobserver variability, validation of data from qPSMA by comparison with Syngo.via, and assessment of computational time and comparison of PSMA PET-derived parameters with serum prostate-specific antigen. Results: Automatic liver background calculation resulted in a mean relative difference of 0.74% (intraclass correlation coefficient [ICC], 0.996; 95%CI, 0.989;0.998) compared with METAVOL. Intra- and interobserver variability analyses showed high agreement (all ICCs > 0.990). Quantitative output parameters were compared for 68 lesions. Paired t testing showed no significant differences between the values obtained with the 2 software packages. The ICC estimates obtained for PSMA-TV, PSMA-TL, SUV_mean, and SUV_max were 1.000 (95%CI, 1.000;1.000), 1.000 (95%CI, 1.000;1.000), 0.995 (95%CI, 0.992;0.997), and 0.999 (95%CI, 0.999;1.000), respectively. The first and second reads for intraobserver variability resulted in mean computational times of 13.63 min (range, 8.22-25.45 min) and 9.27 min (range, 8.10-12.15 min), respectively (P = 0.001). Highly significant correlations were found between serum prostate-specific antigen value and both PSMA-TV (r = 0.72, P < 0.001) and PSMA-TL (r = 0.66, P = 0.002). Conclusion: Semiautomatic analyses of whole-body tumor burden in ⁶⁸Ga-PSMA11 PET/CT is feasible. qPSMA is a robust software package that can help physicians quantify tumor load in heavily metastasized prostate cancer patients.

Diagnostic Value of FDG PET-CT Quantitative Parameters and Deauville-Like 5 Point-Scale in Predicting Malignancy of Focal Thyroid Incidentaloma

Objective: To evaluate the diagnostic value of FDG PET-CT metabolic parameters and Deauville-like 5 point-scale to predict malignancy in a population of patients presenting focal thyroid incidentaloma (fTI).

Design: This retrospective study included 41 fTI, classified according to cytological and histological data as benign (BL) or malignant lesion (ML). FDG PET-CT semi-quantitative parameters (SUVmax, SUVmean, SUVpeak, MTV, TLG), tumor to liver SUVmean ratio (TLRmax and TLRmean), tumor to blood-pool SUVmean ratio (TBRmax and TBRmean) were calculated. Each fTI was also classified on a Deauville-like 5-point scale (DS) currently used in lymphoma. Comparison between BL and ML was performed for each parameter and a ROC analysis was conducted.

Results: All quantitative PET metabolic parameters (SUV parameters, volume based parameters and SUV ratio) were higher in ML compared with BL, yet no significant difference was reported. fTI (uptake) malignancy rate according to DS grades 2, 3, 4, and 5 was, respectively, 25% (1 of 4), 28.6% (2 of 7), 8.3% (1 of 12), and 33.3% (6 of 18) with no significant difference between ML and BL groups. Results of ROC analysis showed that mean TBR had the highest AUC in our cohort (0.66 95%CI [0.41; 0.91]) with a cut-off value of 2.2. Specificity of MTV and TLG was 100% (cut-off values: MTV 9.6 ml, TLG 22.9 g) and their sensitivity was 30 and 40%, respectively.

Conclusion: Our study did not highlight any FDG PET/CT parameter predictor of fTI malignancy.

Impact of PET reconstruction protocols on quantification of lesions that fulfil the PERCIST lesion inclusion criteria

BACKGROUND

The aim of this study was to compare liver and oncologic lesion standardized uptake values (SUV) obtained through two different reconstruction protocols, GE's newest clinical lesion detection protocol (Q.Clear) and the EANM Research Ltd (EARL) harmonization protocol, and to assess the clinical relevance of potential differences and possible implications for daily clinical practice using the PERCIST lesional inclusion criteria. NEMA phantom recovery coefficients (RC) and SUV normalized for lean body mass (LBM), referred to as SUV normalized for LBM (SUL), of liver and lesion volumes of interest were compared between the two reconstruction protocols. Head-to-toe PET/CT examinations and raw data from 64 patients were retrospectively retrieved. PET image reconstruction was carried out twice: once optimized for quantification, complying with EARL accreditation requirements, and once optimized for lesion detection, according to GE's Q.Clear reconstruction settings.

RESULTS

The two reconstruction protocols showed different NEMA phantom RC values for different sphere sizes. Q.Clear values were always highest and exceeded the EARL accreditation maximum for smaller spheres. Comparison of liver SUL_mean showed a statistically significant but clinically irrelevant difference between both protocols. Comparison of lesion SUL_peak and SUL_max showed a statistically significant, and clinically relevant, difference of 1.64 and 4.57, respectively.

CONCLUSIONS

For treatment response assessment using PERCIST criteria, the harmonization reconstruction protocol should be used as the lesion detection reconstruction protocol using resolution recovery systematically overestimates true SUL values.

Phantom, clinical, and texture indices evaluation and optimization of a penalized-likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner

INTRODUCTION

The aim of this study was to evaluate the behavior of a penalized-likelihood image reconstruction method (Q.Clear) under different count statistics and lesion-to-background ratios (LBR) on a BGO scanner, in order to obtain an optimum penalization factor (β value) to study and optimize for different acquisition protocols and clinical goals.

METHODS

Both phantom and patient images were evaluated. Data from an image quality phantom were acquired using different Lesion-to-Background ratios and acquisition times. Then, each series of the phantom was reconstructed using β values between 50 and 500, at intervals of 50. Hot and cold contrasts were obtained, as well as background variability and contrast-to-noise ratio (CNR). Fifteen ¹⁸ F-FDG patients (five brain scans and 10 torso acquisitions) were acquired and reconstructed using the same β values as in the phantom reconstructions. From each lesion in the torso acquisition, noise, contrast, and signal-to-noise ratio (SNR) were computed. Image quality was assessed by two different nuclear medicine physicians. Additionally, the behaviors of 12 different textural indices were studied over 20 different lesions.

RESULTS

Q.Clear quantification and optimization in patient studies depends on the activity concentration as well as on the lesion size. In the studied range, an increase on β is translated in a decrease in lesion contrast and noise. The net product is an overall increase in the SNR, presenting a tendency to a steady value similar to the CNR in phantom data. As the activity concentration or the sphere size increase the optimal β increases, similar results are obtained from clinical data. From the subjective quality assessment, the optimal β value for torso scans is in a range between 300 and 400, and from 100 to 200 for brain scans. For the recommended torso β values, texture indices present coefficients of variation below 10%.

CONCLUSIONS

Our phantom and patients demonstrate that improvement of CNR and SNR of Q.Clear algorithm which depends on the studied conditions and the penalization factor. Using the Q.Clear reconstruction algorithm in a BGO scanner, a β value of 350 and 200 appears to be the optimal value for 18F-FDG oncology and brain PET/CT, respectively.

Influence of PET reconstruction technique and matrix size on qualitative and quantitative assessment of lung lesions on [18F]-FDG-PET: A prospective study in 37 cancer patients

PURPOSE

To evaluate the influence of point spread function (PSF)-based reconstruction and matrix size for PET on (1) lung lesion detection and (2) standardized uptake values (SUV).

METHODS

This prospective study included oncological patients who underwent [18F]-FDG-PET/CT for staging. PET data were reconstructed with a 2D ordered subset expectation maximization (OSEM) algorithm, and a 2D PSF-based algorithm (TrueX), separately with two matrix sizes (168×168 and 336×336). The four PET reconstructions (TrueX-168; OSEM-168; TrueX-336; and OSEM-336) were read independently by two raters, and PET-positive lung lesions were recorded. Blinded to the PET findings, a third independent rater assessed lung lesions with diameters of >4mm on CT. Subsequently, PET and CT were reviewed side-by side in consensus. Multi-factorial logistic regression analyses and two-way repeated measures analyses of variance (ANOVA) were performed.

RESULTS

Thirty-seven patients with 206 lung lesions were included. Lesion-based PET sensitivities differed significantly between reconstruction algorithms (P<0.001) and between reconstruction matrices (P=0.022). Sensitivities were 94.2% and 88.3% for TrueX-336; 88.3% and 85.9% for TrueX-168; 67.8% and 66.3% for OSEM-336; and 67.0% and 67.9% for OSEM-168; for rater 1 and rater 2, respectively. SUV_max and SUV_mean were significantly higher for images reconstructed with 336×336 matrices than for those reconstructed with 168×168 matrices (P<0.001).

CONCLUSION

Our results demonstrate that PSF-based PET reconstruction, and, to a lesser degree, higher matrix size, improve detection of metabolically active lung lesions. However, PSF-based PET reconstructions and larger matrix sizes lead to higher SUVs, which may be a concern when PET data from different institutions are compared.

Comparison of 68Ga-labelled PSMA-11 and 11C-choline in the detection of prostate cancer metastases by PET/CT

PURPOSE

Prostate-specific membrane antigen (PSMA) is expressed ubiquitously on the membrane of most prostate tumors and its metastasis. While PET/CT using ¹¹C-choline was considered as the gold standard in the staging of prostate cancer, PET with radiolabelled PSMA ligands was introduced into the clinic in recent years. Our aim was to compare the PSMA ligand ⁶⁸Ga-PSMA-11 with ¹¹C-choline in patients with primary and recurrent prostate cancer.

METHODS

123 patients underwent a whole-body PET/CT examination using ⁶⁸Ga-PSMA-11 and ¹¹C-choline. Suspicious lesions were evaluated visually and semiquantitatively (SUVavg). Out of these, 103 suffered from a confirmed biochemical relapse after prostatectomy and/or radiotherapy (mean PSA level of 4.5 ng/ml), while 20 patients underwent primary staging.

RESULTS

In 67 patients with biochemical relapse, we detected 458 lymph nodes suspicious for metastasis. PET using ⁶⁸Ga-PSMA-11 showed a significantly higher uptake and detection rate than ¹¹C-choline PET. Also ⁶⁸Ga-PSMA-11 PET identified significantly more patients with suspicious lymph nodes as well as affected lymph nodes regions especially at low PSA levels. Bone lesions suspicious for prostate cancer metastasis were revealed in 36 patients' biochemical relapse. Significantly more bone lesions were detected by ⁶⁸Ga-PSMA-11, but only 3 patients had only PSMA-positive bone lesions. Nevertheless, we detected also 29 suspicious lymph nodes and 8 bone lesions, which were only positive as per ¹¹C-choline PET. These findings led to crucial differences in the TNM classification and the identification of oligometastatic patients. In the patients who underwent initial staging, all primary tumors showed uptake of both tracers. Although significantly more suspicious lymph nodes and bone lesions were identified, only 2 patients presented with bone lesions only detected by ⁶⁸Ga-PSMA-11 PET.

CONCLUSION

Thus, PET using ⁶⁸Ga-PSMA-11 showed a higher detection rate than ¹¹C-choline PET for lymph nodes as well as bone lesions. However, we found lymph nodes and bone lesions which were not concordant applying both tracers.

Impact of PET/CT image reconstruction methods and liver uptake normalization strategies on quantitative image analysis

BACKGROUND

In oncological imaging using PET/CT, the standardized uptake value has become the most common parameter used to measure tracer accumulation. The aim of this analysis was to evaluate ultra high definition (UHD) and ordered subset expectation maximization (OSEM) PET/CT reconstructions for their potential impact on quantification.

PATIENTS AND METHODS

We analyzed 40 PET/CT scans of lung cancer patients who had undergone PET/CT. Standardized uptake values corrected for body weight (SUV) and lean body mass (SUL) were determined in the single hottest lesion in the lung and normalized to the liver for UHD and OSEM reconstruction. Quantitative uptake values and their normalized ratios for the two reconstruction settings were compared using the Wilcoxon test. The distribution of quantitative uptake values and their ratios in relation to the reconstruction method used were demonstrated in the form of frequency distribution curves, box-plots and scatter plots. The agreement between OSEM and UHD reconstructions was assessed through Bland-Altman analysis.

RESULTS

A significant difference was observed after OSEM and UHD reconstruction for SUV and SUL data tested (p < 0.0005 in all cases). The mean values of the ratios after OSEM and UHD reconstruction showed equally significant differences (p < 0.0005 in all cases). Bland-Altman analysis showed that the SUV and SUL and their normalized values were, on average, up to 60 % higher after UHD reconstruction as compared to OSEM reconstruction.

CONCLUSION

OSEM and HD reconstruction brought a significant difference for SUV and SUL, which remained constantly high after normalization to the liver, indicating that standardization of reconstruction and the use of comparable SUV measurements are crucial when using PET/CT.

Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients

PURPOSE

Point-spread function (PSF) or PSF + time-of-flight (TOF) reconstruction may improve lesion detection in oncologic PET, but can alter quantitation resulting in variable standardized uptake values (SUVs) between different PET systems. This study aims to validate a proprietary software tool (EQ.PET) to harmonize SUVs across different PET systems independent of the reconstruction algorithm used.

METHODS

NEMA NU2 phantom data were used to calculate the appropriate filter for each PSF or PSF+TOF reconstruction from three different PET systems, in order to obtain EANM compliant recovery coefficients. PET data from 517 oncology patients were reconstructed with a PSF or PSF+TOF reconstruction for optimal tumour detection and an ordered subset expectation maximization (OSEM3D) reconstruction known to fulfil EANM guidelines. Post-reconstruction, the proprietary filter was applied to the PSF or PSF+TOF data (PSFEQ or PSF+TOFEQ). SUVs for PSF or PSF+TOF and PSFEQ or PSF+TOFEQ were compared to SUVs for the OSEM3D reconstruction. The impact of potential confounders on the EQ.PET methodology including lesion and patient characteristics was studied, as was the adherence to imaging guidelines.

RESULTS

For the 1380 tumour lesions studied, Bland-Altman analysis showed a mean ratio between PSF or PSF+TOF and OSEM3D of 1.46 (95%CI: 0.86-2.06) and 1.23 (95%CI: 0.95-1.51) for SUVmax and SUVpeak, respectively. Application of the proprietary filter improved these ratios to 1.02 (95%CI: 0.88-1.16) and 1.04 (95%CI: 0.92-1.17) for SUVmax and SUVpeak, respectively. The influence of the different confounding factors studied (lesion size, location, radial offset and patient's BMI) was less than 5%. Adherence to the European Association of Nuclear Medicine (EANM) guidelines for tumour imaging was good.

CONCLUSION

These data indicate that it is not necessary to sacrifice the superior lesion detection and image quality achieved by newer reconstruction techniques in the quest for harmonizing quantitative comparability between PET systems.