Standard PET Imaging Research Articles

Comparison of Measurement and Prognostic Power of SUV Between High-Definition and Standard PET Imaging in Non-Small Cell Lung Cancer Patients.

This study aimed to evaluate the measurement and prognostic ability of the SUVmax of whole-body tumors (SUVmaxwb) in non-small cell lung cancer (NSCLC) patients, comparing high-definition (HD) PET imaging with standard-definition (SD) PET imaging. Methods: The study included 242 consecutive NSCLC patients who underwent baseline 18F-FDG PET/CT from April 2018 to January 2021. Two imaging techniques were used: HD PET (using ordered-subsets expectation maximization with point-spread function modeling and time-of-flight techniques and smaller voxels) and SD PET (with ordered-subsets expectation maximization and time-of-flight techniques). SUVmaxwb was determined by measuring all the tumor lesions in the whole body, and tumor-to-background ratio (TBR) was calculated using the background SUVmean of various body parts. Results: The patient cohort had an average age of 68.3 y, with 59.1% being female. During a median follow-up of 29.6 mo, 83 deaths occurred. SUVmaxwb was significantly higher in HD PET than SD PET, with respective medians of 17.4 and 11.8. The TBR of 1,125 tumoral lesions was also higher in HD PET. Univariate Cox regression analysis showed that SUVmaxwb from both HD and SD PET were significantly associated with overall survival. However, after adjusting for TNM (tumor, node, metastasis) stage, only SUVmaxwb from SD PET remained significantly associated with survival. Conclusion: HD PET imaging in NSCLC patients yields higher SUVmaxwb and TBR, enhancing tumor visibility. Despite this, its prognostic value is less significant than SD PET after adjusting clinical TNM stage. Thus, consideration should be given to using HD PET reconstruction to increase tumor visibility, and SD PET is recommended for NSCLC patient prognostication and therapeutic evaluation, as well as for the classification of lung nodules.

Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.

This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients with lymphoma. A total of 123 cases devoid of lymphoma underwent whole-body 18F-FDG-PET/CT scans to facilitate the development of an advanced SAU2Net model, which combines the advantages of U2Net and attention mechanism. This model integrated inputs from simulated 1/2-dose (0.07 mCi/kg) PET acquisition across multiple slices to generate an estimated standard dose (0.14 mCi/kg) PET scan. Additional 39 cases with confirmed lymphoma pathology were utilized to evaluate the model's clinical performance. Assessment criteria encompassed peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), a 5-point Likert scale rated by two experienced physicians, SUV features, image noise in the liver, and contrast-to-noise ratio (CNR). Diagnostic outcomes, including lesion numbers and Deauville score, were also compared. Images enhanced by the proposed DL method exhibited superior image quality (P<0.001) in comparison to low-dose acquisition. Moreover, they illustrated equivalent image quality in terms of subjective image analysis and lesion maximum standardized uptake value (SUVmax) as compared to the standard acquisition method. A linear regression model with y=1.017x + 0.110 ( ) can be established between the enhanced scans and the standard acquisition for lesion SUVmax. With enhancement, increasedsignal-to-noise ratio (SNR), CNR, and reduced image noise were observed, surpassing those of the standard acquisition. DL-enhanced PET images got diagnostic results essentially equavalent to standard PET images according totwo experienced readers. The proposed DL method could facilitate a 50% reduction in PET imaging duration for lymphoma patients, while concurrently preserving image quality and diagnostic accuracy.

A Biopsy-Controlled Prospective Study of Contrast-Enhancing Diffuse Glioma Infiltration Based on FET-PET and FLAIR.

Accurately defining glioma infiltration is crucial for optimizing radiotherapy and surgery, but glioma infiltration is heterogeneous and MRI imperfectly defines the tumor extent. Currently, it is impossible to determine the tumor infiltration gradient within a FLAIR signal. O-(2-[18F]fluoroethyl)-L-tyrosine (FET)-PET often reveals high-grade glioma infiltration beyond contrast-enhancing areas on MRI. Here, we studied FET uptake dynamics in tumor and normal brain structures by dual-timepoint (10 min and 40-60 min post-injection) acquisition to optimize analysis protocols for defining glioma infiltration. Over 300 serial stereotactic biopsies from 23 patients (mean age 47, 12 female/11 male) of diffuse contrast-enhancing gliomas were taken from areas inside and outside contrast enhancement or outside the FET hotspot but inside FLAIR. The final diagnosis was G4 in 11, grade 3 in 10, and grade 2 in 2 patients. The target-to-background (TBRs) ratios and standardized uptake values (SUVs) were calculated in areas used for biopsy planning and in background structures. The optimal method and threshold values were determined to find a preferred strategy for defining glioma infiltration. Standard thresholding (1.6× uptake in the contralateral brain) in standard acquisition PET images differentiated a tumor of any grade from astrogliosis, although the uptake in astrogliosis and grade 2 glioma was similar. Analyzing an optimal strategy for infiltration volume definition astrogliosis could be accurately differentiated from tumor samples using a choroid plexus as a background. Early acquisition improved the AUC in many cases, especially within FLAIR, from 56% to 90% sensitivity and 41% to 61% specificity (standard TBR 1.6 vs. early TBR plexus). The current FET-PET evaluation protocols for contrast-enhancing gliomas are limited, especially at the tumor border where grade 2 tumor and astrogliosis have similar uptake, but using choroid plexus uptake in early acquisitions as a background, we can precisely define a tumor within FLAIR that was outside of the scope of current FET-PET protocols.

Intelligent ultrafast total-body PET for sedation-free pediatric [18F]FDG imaging.

This study aims to develop deep learning techniques on total-body PET to bolster the feasibility of sedation-free pediatric PET imaging. A deformable 3D U-Net was developed based on 245 adult subjects with standard total-body PET imaging for the quality enhancement of simulated rapid imaging. The developed method was first tested on 16 children receiving total-body [18F]FDG PET scans with standard 300-s acquisition time with sedation. Sixteen rapid scans (acquisition time about 3 s, 6 s, 15 s, 30 s, and 75 s) were retrospectively simulated by selecting the reconstruction time window. In the end, the developed methodology was prospectively tested on five children without sedation to prove the routine feasibility. The approach significantly improved the subjective image quality and lesion conspicuity in abdominal and pelvic regions of the generated 6-s data. In the first test set, the proposed method enhanced the objective image quality metrics of 6-s data, such as PSNR (from 29.13 to 37.09, p < 0.01) and SSIM (from 0.906 to 0.921, p < 0.01). Furthermore, the errors of mean standardized uptake values (SUVmean) for lesions between 300-s data and 6-s data were reduced from 12.9 to 4.1% (p < 0.01), and the errors of max SUV (SUVmax) were reduced from 17.4 to 6.2% (p < 0.01). In the prospective test, radiologists reached a high degree of consistency on the clinical feasibility of the enhanced PET images. The proposed method can effectively enhance the image quality of total-body PET scanning with ultrafast acquisition time, leading to meeting clinical diagnostic requirements of lesion detectability and quantification in abdominal and pelvic regions. It has much potential to solve the dilemma of the use of sedation and long acquisition time that influence the health of pediatric patients.

Abstract 5049: Pb-203 image guided Pb-212 receptor targeted alpha particle therapy for cancer - a powerful emerging paradigm

Abstract Objective: Pb-203 and Pb-212 have emerged as a promising elementally-matched theranostic pair for imaging-guided alpha-particle radiotherapy for cancer. A somatostatin receptor 2 (SSTR2)-targeted peptide coupled with novel Pb specific chelator (PSC) has been developed (PSC-PEG2-TOC). Here, preclinical in vitro and in vivo evaluation of 203Pb/212Pb-labeled PSC-PEG2-TOC was conducted including head to head therapy of SSTR2+ tumors in mice vs standard of care beta particle therapy and first in humans clinical imaging of SSTR2+ tumors. Method: [203Pb]PSC-PEG2-TOC and [212Pb]PSC-PEG2-TOC was prepared by published methods. Radiochemical stability of [203Pb]PSC-PEG2-TOC, [212Pb]PSC-PEG2-TOC and [212Bi]PSC-PEG2-TOC in human serum was determined. Binding affinity of [203Pb]PSC-PEG2-TOC was determined by binding assays in AR42J cells. In vivo SSTR2-mediated tumor targeting of [203Pb]PSC-PEG2-TOC was determined by SPECT imaging in athymic nude mice bearing AR42J xenografts. In vivo biodistribution of [212Pb]PSC-PEG2-TOC in normal organs and potential redistribution of 212Bi daughter were determined in CD-1 Elite mice. Clinical imaging was conducted under appropriate regulations to determine the biodistribution of the agent in patients bearing SSTR2+ tumors as determined by gold standard 68Ga-DOTATOC PET imaging. Results: Rapid incorporation of 203Pb and 212Pb in PSC-PEG2-TOC was observed after reactions at temperatures as low as 4°C within 15 min. Greater than 95% radiochemical stability was observed for both [203Pb]PSC-PEG2-TOC and [212Pb]PSC-PEG2-TOC after incubation in human serum for up to 55 hours. Stable decay product [212Bi]PSC-PEG2-TOC with minimal free 212Bi was observed. Agents demonstrated superior binding affinity to SSTR2 with Kd=0.59 nM. In in vivo studies, rapid tumor uptake and renal clearance of [203Pb]PSC-PEG2-TOC were observed in athymic nude mice bearing AR42J xenografts. In the biodistribution study, nearly identical biodistribution profiles of [212Pb]PSC-PEG2-TOC and progeny [212Bi]PSC-PEG2-TOC were found. No redistribution of 212Bi activity was identified, indicating that 212Bi remained co-localized with parent [212Pb]PSC-PEG2-TOC in vivo. Therapeutic studies in this same mouse model resulted in 100% complete responses for both a single dose 4.4 MBq administration of [212Pb]PSC-PEG2-TOC or a fractionated regimen of 4 × 1.1 MBq. First in humans clinical imaging showed PK properties that included rapid tumor accumulation and retention at over 20 h post administration, coupled with fast renal clearance of residual agent. Conclusion: [203Pb]PSC-PEG2-TOC and [212Pb]PSC-PEG2-TOC have promising potential for image-guide alpha-particle therapy for SSTR2 positive tumors. Agents have received a Fastrack designation by the US FDA and a safe to proceed for a Phase 1 trial. Citation Format: Michael King Schultz, Yusuf Menda, Dijie Liu, Mengshi Li, Frances Johnson, Brianna Cagle, Nicholas Baumhover, Ivy Vance. Pb-203 image guided Pb-212 receptor targeted alpha particle therapy for cancer - a powerful emerging paradigm. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5049.

Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy.

ObjectivesAround 30% of patients undergoing surgical resection for drug‐resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG‐PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice.MethodsEighty two patients with drug resistant MTLE were scanned with FDG‐PET pre‐surgery and T1‐weighted MRI pre‐ and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks.ResultsIn the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug‐resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow‐up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75–.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59–.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance.SignificanceCollectively, these results indicate that "acceptable" to "good" patient‐specific prognostication for drug‐resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.

Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on 68Ga-PSMA-11 PET/CT in patients with primary prostate cancer.

68Ga-PSMA PET/CT has high specificity and sensitivity for the detection of both intraprostatic tumor focal lesions and metastasis. However, approximately 10% of primary prostate cancer are invisible on PSMA-PET (exhibit no or minimal uptake). In this work, we investigated whether machine learning-based radiomics models derived from PSMA-PET images could predict invisible intraprostatic lesions on 68Ga-PSMA-11 PET in patients with primary prostate cancer. In this retrospective study, patients with or without prostate cancer who underwent 68Ga-PSMA PET/CT and presented negative on PSMA-PET image at either of two different institutions were included: institution 1 (between 2017 and 2020) for the training set and institution 2 (between 2019 and 2020) for the external test set. Three random forest (RF) models were built using selected features extracted from standard PET images, delayed PET images, and both standard and delayed PET images. Then, subsequent tenfold cross-validation was performed. In the test phase, the three RF models and PSA density (PSAD, cut-off value: 0.15ng/ml/ml) were tested with the external test set. The area under the receiver operating characteristic curve (AUC) was calculated for the models and PSAD. The AUCs of the radiomics model and PSAD were compared. A total of 64 patients (39 with prostate cancer and 25 with benign prostate disease) were in the training set, and 36 (21 with prostate cancer and 15 with benign prostate disease) were in the test set. The average AUCs of the three RF models from tenfold cross-validation were 0.87 (95% CI: 0.72, 1.00), 0.86 (95% CI: 0.63, 1.00), and 0.91 (95% CI: 0.69, 1.00), respectively. In the test set, the AUCs of the three trained RF models and PSAD were 0.903 (95% CI: 0.830, 0.975), 0.856 (95% CI: 0.748, 0.964), 0.925 (95% CI:0.838, 1.00), and 0.662 (95% CI: 0.510, 0.813). The AUCs of the three radiomics models were higher than that of PSAD (0.903, 0.856, and 0.925 vs. 0.662, respectively; P = .007, P = .045, and P = .005, respectively). Random forest models developed by 68Ga-PSMA-11 PET-based radiomics features were proven useful for accurate prediction of invisible intraprostatic lesion on 68Ga-PSMA-11 PET in patients with primary prostate cancer and showed better diagnostic performance compared with PSAD.

Positronium life-time as a new approach for cardiac masses imaging

Abstract Background/Introduction Positron Emission Tomography (PET), also a standard in cardiovascular imaging, enables semi-quantitative myocardial perfusion evaluation and assessment of atherosclerosis. PET is based on positron-electro annihilation inside a patient's body and in about 40% events it proceeds via formation of positronium. Positronium is an exotic atom created from an electron and a positron in a patient during standard PET imaging. Positronium is not stable and its life-time depends on molecular environment. Recently, it was shown that with the new generation of high sensitivity long-axial field of view PET systems is possible to perform simultaneous imaging of the metabolism rate of administered pharmaceutical and imaging of positronium life-time. Positronium life-time informs about tissue nanostructure and allows distinguishing pathology. Purpose Purpose of the presented study is the evaluation of positronium life-time as a biomarker for cardiac masses, namely cardiac myxoma (CM) and normal tissues, obtained from symptomatic patients. Methods CM and adipose tissue samples were obtained during a surgery from six symptomatic patients with ECHO confirmed cardiac masses in a left atrium. Immediately after sampling, sample were analyzed with the dedicated positronium-life time measurement system. Samples were irradiated with positrons emitted from the 22Na radionuclide. The system enabled to measure the time-difference spectrum between the formation and annihilation of positronium atom within the studied samples. The life-time spectrum was used for the extraction of a mean positronium life-time. The sample underwent histopathological and micro-computer-tomography (micro-CT) examinations, which confirmed a diagnosis and revealed microstructure of a cardiac mass. Results It was established that positronium life-time in CM tissues is significantly (at the level of 10 standard deviations) smaller than in adipose tissue. At the same time, the determined positronium life-time in CM samples from different patients was the same within two standard deviations. Micro-CT imaging showed that CM samples had different calcification spots, but did not revealed existing microvasculature. Conclusion Obtained results confirm that positronium life-time differentiates between cardiac pathologies (CM) and normal tissues. And may be used as a biomarker in cardiac masses assessment available during PET examination. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Foundation for Polish Science through the TEAM POIR.04.04.00-00-4204/17 Programme, the National Science Centre.

Complete Evaluation of Dementia: PET and MRI Correlation and Diagnosis for the Neuroradiologist.

This article will familiarize neuroradiologists with the pathophysiology, clinical findings, and standard MR imaging and PET imaging features of multiple forms of dementia as well as new emerging techniques. Cases were compiled from multiple institutions with the goal of improved diagnostic accuracy and improved patient care as well as information about biomarkers on the horizon. Dementia topics addressed include the following: Alzheimer disease, frontotemporal dementia, cerebral amyloid angiopathy, Lewy body dementia, Parkinson disease and Parkinson disease variants, amyotrophic lateral sclerosis, multisystem atrophy, Huntington disease vascular dementia, and Creutzfeldt-Jakob disease.

Functional 4-D clustering for characterizing intratumor heterogeneity in dynamic imaging: evaluation in FDG PET as a prognostic biomarker for breast cancer

PurposeProbe-based dynamic (4-D) imaging modalities capture breast intratumor heterogeneity both spatially and kinetically. Characterizing heterogeneity through tumor sub-populations with distinct functional behavior may elucidate tumor biology to improve targeted therapy specificity and enable precision clinical decision making.MethodsWe propose an unsupervised clustering algorithm for 4-D imaging that integrates Markov-Random Field (MRF) image segmentation with time-series analysis to characterize kinetic intratumor heterogeneity. We applied this to dynamic FDG PET scans by identifying distinct time-activity curve (TAC) profiles with spatial proximity constraints. We first evaluated algorithm performance using simulated dynamic data. We then applied our algorithm to a dataset of 50 women with locally advanced breast cancer imaged by dynamic FDG PET prior to treatment and followed to monitor for disease recurrence. A functional tumor heterogeneity (FTH) signature was then extracted from functionally distinct sub-regions within each tumor. Cross-validated time-to-event analysis was performed to assess the prognostic value of FTH signatures compared to established histopathological and kinetic prognostic markers.ResultsAdding FTH signatures to a baseline model of known predictors of disease recurrence and established FDG PET uptake and kinetic markers improved the concordance statistic (C-statistic) from 0.59 to 0.74 (p = 0.005). Unsupervised hierarchical clustering of the FTH signatures identified two significant (p < 0.001) phenotypes of tumor heterogeneity corresponding to high and low FTH. Distributions of FDG flux, or Ki, were significantly different (p = 0.04) across the two phenotypes.ConclusionsOur findings suggest that imaging markers of FTH add independent value beyond standard PET imaging metrics in predicting recurrence-free survival in breast cancer and thus merit further study.

Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure.

To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3-30years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics. The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p< 0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers' diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa = 0.942) than 6.25% dose scans (kappa = 0.650). Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements.

Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features

This study aimed to evaluate the diagnostic value of a support vector machine (SVM) model built with texture features based on standard 2-[F]fluoro-2-deoxy-D-glucose (F-FDG) PET in patients with solitary pulmonary nodules (SPNs) at a volume larger than 5 mL. The PET results of 82 patients diagnosed with SPNs between 2014 and 2018 were retrospectively analysed. The volumes of interest (VOIs) of the SPNs were automatically segmented using threshold techniques from the standard PET imaging. Then, a large number of texture features were extracted from the VOIs using texture-analysis software. Next, an optimized SVM machine-learning model that was trained on standard PET images using texture features was employed to identify the optimal discrimination between malignant and benign nodules. Diagnostic models based on the maximum standardized uptake value (SUVmax) and the metabolic tumour volume (MTV) were compared with the SVM model with regard to the SPN diagnostic power. Compared with the SUVmax and MTV models, the texture-based SVM model provided an improvement of approximately 20% in diagnostic accuracy, positive predictive value, negative predictive value and the area under the operating characteristic curve. The receiver operating characteristic curve of the SVM model showed a significant improvement compared with the MTV model (P = 0.0345 < 0.05) and the SUVmax model (P = 0.01 < 0.05). Standard F-FDG PET imaging can increase the differentiation of benign and malignant SPNs with volumes larger than 5 mL using an SVM model based on texture features.

Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-PET radiomics

BackgroundThe aim of this study is to determine if the choice of the 18F-FDG-PET protocol, especially matrix size and reconstruction algorithm, is of importance to discriminate between immunohistochemical subtypes (luminal versus non-luminal) in breast cancer with textural features (TFs).ProceduresForty-seven patients referred for breast cancer staging in the framework of a prospective study were reviewed as part of an ancillary study. In addition to standard PET imaging (PSFWholeBody), a high-resolution breast acquisition was performed and reconstructed with OSEM and PSF (OSEMbreast/PSFbreast). PET standard metrics and TFs were extracted. For each reconstruction protocol, a prediction model for tumour classification was built using a random forests method. Spearman coefficients were used to seek correlation between PET metrics.ResultsPSFWholeBody showed lower numbers of voxels within VOIs than OSEMbreast and PSFbreast with median (interquartile range) equal to 130 (43–271), 316 (167–1042), 367 (107–1221), respectively (p < 0.0001). Therefore, using LifeX software, 28 (59%), 46 (98%) and 42 (89%) patients were exploitable with PSFWholeBody, OSEMbreast and PSFbreast, respectively.On matched comparisons, PSFbreast reconstruction presented better abilities than PSFwholeBody and OSEMbreast for the classification of luminal versus non-luminal breast tumours with an accuracy reaching 85.7% as compared to 67.8% for PSFwholeBody and 73.8% for OSEMbreast. PSFbreast accuracy, sensitivity, specificity, PPV and NPV were equal to 85.7%, 94.3%, 42.9%, 89.2%, 60.0%, respectively. Coarseness and ZLNU were found to be main variables of importance, appearing in all three prediction models. Coarseness was correlated with SUVmax on PSFwholeBody images (ρ = − 0.526, p = 0.005), whereas it was not on OSEMbreast (ρ = − 0.183, p = 0.244) and PSFbreast (ρ = − 0.244, p = 0.119) images. Moreover, the range of its values was higher on PSFbreast images as compared to OSEMbreast, especially in small lesions (MTV < 3 ml).ConclusionsHigh-resolution breast PET acquisitions, applying both small-voxel matrix and PSF modelling, appeared to improve the characterisation of breast tumours.

Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – Initial results

ObjectivesWe evaluated whether machine learning may be helpful for the detection of lung cancer in FDG-PET imaging in the setting of ultralow dose PET scans. Materials and methodsWe studied the performance of an artificial neural network discriminating lung cancer patients (n = 50) from controls (n = 50) without pulmonary malignancies. A total of 3936 PET slices including images in which the lung tumor is visually present and image slices of patients with no lung cancer were exported. The diagnostic performance of the artificial neural network based on clinical standard dose PET images (PET100%) as well as with a tenfold (PET10%) and thirtyfold (PET3.3%) reduced radiation dose (∼0.11 mSv) was assessed. ResultsThe area under the curve of the deep learning algorithm for lung cancer detection was 0.989, 0.983 and 0.970 for standard dose images (PET100%), and reduced dose PET10%, and PET3.3% reconstruction, respectively. The artificial neural network achieved a sensitivity of 95.9% and 91.5% and a specificity of 98.1% and 94.2%, at standard dose and ultralow dose PET3.3%, respectively. ConclusionOur results suggest that machine learning algorithms may aid fully automated lung cancer detection even at very low effective radiation doses of 0.11 mSv. Further improvement of this technology might improve the specificity of lung cancer screening efforts and could lead to new applications of FDG-PET.

18F]FDG PET/CT features for the molecular characterization of primary breast tumors.

The aim of this study was to evaluate the role of imaging features derived from [18F]FDG-PET/CT to provide in vivo characterization of breast cancer (BC). Images from 43 patients with a first diagnosis of BC were reviewed. Images were acquired before any treatment. Histological data were derived from pretreatment biopsy or surgical histological specimen; these included tumor type, grade, ER and PgR receptor status, lymphovascular invasion, Ki67 index, HER2 status, and molecular subtype. Standard parameters (SUVmean, TLG, MTV) and advanced imaging features (histogram-based and shape and size features) were evaluated. Univariate analysis, hierarchical clustering analysis, and exact Fisher's test were used for statistical analysis of data. Imaging-derived metrics were reduced evaluating the mutual correlation within group of features as well as the mutual correlation between groups of features to form a signature. A significant correlation was found between some advanced imaging features and the histological type. Different molecular subtypes were characterized by different values of two histogram-based features (median and energy). A significant association was observed between the imaging signature and luminal A and luminal B HER2 negative molecular subtype and also when considering luminal A, luminal B HER2-negative and HER2-positive groups. Similar results were found between the signature and all five molecular subtypes and also when considering the histological types of BC. Our results suggest a complementary role of standard PET imaging parameters and advanced imaging features for the in vivo biological characterization of BC lesions.

Cerenkov Luminescence Imaging as a Modality to Evaluate Antibody-Based PET Radiotracers.

The anti-prostate-specific membrane antigen (PSMA) huJ591 antibody (IgG; 150 kDa) and its minibody (Mb; 80 kDa) format were functionalized with the chelator 1,4,7-triazacyclononane-1-glutaric acid-4,7-diacetic acid (NODAGA) and radiolabeled with the positron-emitting radionuclide 64Cu (half-life, 12.7 h). Immunoreactive preparations of the radiolabeled antibodies were injected into NCr nu/nu mice harboring PSMA-positive CWR22Rv1 and PSMA-negative PC-3 tumor xenografts. Tumor targeting was evaluated by both PET and CLI. 64Cu-NODAGA-PSMA-IgG and 64Cu-NODAGA-PSMA-Mb retained the ability to bind cell surface PSMA, and both radiotracers exhibited selective uptake into PSMA-positive tumors. Under the experimental conditions used, PSMA-selective uptake of 64Cu-NODAGA-PSMA-IgG and 64Cu-NODAGA-PSMA-Mb was observed by CLI as early as 3 h after injection, with tumor-to-background ratios peaking at 24 (IgG) and 16 (Mb) h after injection. Targeting data generated by CLI correlated with that generated by PET and necropsy. CLI provided a rapid and simple assessment of the targeting specificity and pharmacokinetics of the antibody-based PET radiotracers that correlated well with the behavior observed by standard PET imaging. Moreover, CLI provided clear discrimination between uptake kinetics of an intact IgG and its small-molecular-weight derivative Mb. These data support the use of CLI for the evaluation of radiotracer performance.

Multimodal Partial-Volume Correction: Application to 18F-Fluoride PET/CT Bone Metastases Studies.

(18)F-fluoride PET/CT offers the opportunity for accurate skeletal metastasis staging, compared with conventional imaging methods. (18)F-fluoride is a bone-specific tracer whose uptake depends on osteoblastic activity. Because of the resulting increase in bone mineralization and sclerosis, the osteoblastic process can also be detected morphologically in CT images. Although CT is characterized by high resolution, the potential of PET is limited by its lower spatial resolution and the resulting partial-volume effect. In this context, the synergy between PET and CT presents an opportunity to resolve this limitation using a novel multimodal approach called synergistic functional-structural resolution recovery (SFS-RR). Its performance is benchmarked against current resolution recovery technology using the point-spread function (PSF) of the scanner in the reconstruction procedure. The SFS-RR technique takes advantage of the multiresolution property of the wavelet transform applied to both functional and structural images to create a high-resolution PET image that exploits the structural information of CT. Although the method was originally conceived for PET/MR imaging of brain data, an ad hoc version for whole-body PET/CT is proposed here. Three phantom experiments and 2 datasets of metastatic bone (18)F-fluoride PET/CT images from primary prostate and breast cancer were used to test the algorithm performances. The SFS-RR images were compared with the manufacturer's PSF-based reconstruction using the standardized uptake value (SUV) and the metabolic volume as metrics for quantification. When compared with standard PET images, the phantom experiments showed a bias reduction of 14% in activity and 1.3 cm(3) in volume estimates for PSF images and up to 20% and 2.5 cm(3) for the SFS-RR images. The SFS-RR images were characterized by a higher recovery coefficient (up to 60%) whereas noise levels remained comparable to those of standard PET. The clinical data showed an increase in the SUV estimates for SFS-RR images up to 34% for peak SUV and 50% for maximum SUV and mean SUV. Images were also characterized by sharper lesion contours and better lesion detectability. The proposed methodology generates PET images with improved quantitative and qualitative properties. Compared with standard methods, SFS-RR provides superior lesion segmentation and quantification, which may result in more accurate tumor characterization.

Comparing Respiratory Gated With Delayed Scans in the Detection of Colorectal Carcinoma Hepatic and Pulmonary Metastases With 18F-FDG PET-CT

In patients experiencing colorectal carcinoma, exhaustive analysis of indicates extent of hepatic and pulmonary surgery that prolongs survival of patients. To localize metastasis, we compared 2 F-FDG PET-CT imaging protocols, early respiratory gated scan, and delayed scan, to standard PET imaging procedure. SUVmax and lesion-to-healthy tissue ratio were measured in 60 pulmonary and 21 hepatic lesions by each of the 2 imaging protocols. In the liver, metastatic lesion-to-healthy tissue ratios significantly increased on delayed scans as compared with early scans (P < 0.001). Better ratios could not be obtained when using respiratory gated scans, whereas more lesions were detected on delayed scans. In the lungs, metastatic lesion- to-healthy tissue ratio increased significantly on delayed scans for largest lesions (P < 0.001). Ratios were not better when exploring smallest metastatic lesions or when using respiratory gated scans. Factors interfering with F-FDG PET-CT sensitivity are discussed, such as respiratory motion and high FDG physiological uptake in healthy liver parenchyma. Our studies indicate that routine acquisitions should use delayed hepatic scans for all patients referred for evaluation, recurrence check, or monitoring of colorectal adenocarcinoma. Delayed pulmonary scans are useful for the largest metastatic lesions and should be used in addition to early scan. In our experience, respiratory gated scans seem to be less convenient because of a low sensitivity in detection, as compared with the delayed technique; in addition, this technique is currently complicated by some technical issues, although these might be overcome with new gated protocols.

Validating and improving CT ventilation imaging by correlating with ventilation 4D-PET/CT using 68 Ga-labeled nanoparticles

CT ventilation imaging is a novel functional lung imaging modality based on deformable image registration. The authors present the first validation study of CT ventilation using positron emission tomography with (68)Ga-labeled nanoparticles (PET-Galligas). The authors quantify this agreement for different CT ventilation metrics and PET reconstruction parameters. PET-Galligas ventilation scans were acquired for 12 lung cancer patients using a four-dimensional (4D) PET/CT scanner. CT ventilation images were then produced by applying B-spline deformable image registration between the respiratory correlated phases of the 4D-CT. The authors test four ventilation metrics, two existing and two modified. The two existing metrics model mechanical ventilation (alveolar air-flow) based on Hounsfield unit (HU) change (VHU) or Jacobian determinant of deformation (VJac). The two modified metrics incorporate a voxel-wise tissue-density scaling (ρVHU and ρVJac) and were hypothesized to better model the physiological ventilation. In order to assess the impact of PET image quality, comparisons were performed using both standard and respiratory-gated PET images with the former exhibiting better signal. Different median filtering kernels (σm = 0 or 3 mm) were also applied to all images. As in previous studies, similarity metrics included the Spearman correlation coefficient r within the segmented lung volumes, and Dice coefficient d20 for the (0 - 20)th functional percentile volumes. The best agreement between CT and PET ventilation was obtained comparing standard PET images to the density-scaled HU metric (ρVHU) with σm = 3 mm. This leads to correlation values in the ranges 0.22 ≤ r ≤ 0.76 and 0.38 ≤ d20 ≤ 0.68, with r = 0.42 ± 0.16 and d20 = 0.52 ± 0.09 averaged over the 12 patients. Compared to Jacobian-based metrics, HU-based metrics lead to statistically significant improvements in r and d20 (p < 0.05), with density scaled metrics also showing higher r than for unscaled versions (p < 0.02). r and d20 were also sensitive to image quality, with statistically significant improvements using standard (as opposed to gated) PET images and with application of median filtering. The use of modified CT ventilation metrics, in conjunction with PET-Galligas and careful application of image filtering has resulted in improved correlation compared to earlier studies using nuclear medicine ventilation. However, CT ventilation and PET-Galligas do not always provide the same functional information. The authors have demonstrated that the agreement can improve for CT ventilation metrics incorporating a tissue density scaling, and also with increasing PET image quality. CT ventilation imaging has clear potential for imaging regional air volume change in the lung, and further development is warranted.

Phase I trial of zirconium 89 (Zr89) radiolabeled J591 in metastatic castration-resistant prostate cancer (mCRPC).

31 Background: Presently, there are no means of accurately and reproducibly imaging bone metastases for patients with mCRPC. We are investigating a prostate-specific imaging method that can directly visualize metastases using PSMA as a target and Zr89 radiolabeled anti-PSMA antibody J591 as a tracer. Following the FDA roadmap for biomarker development, we report the first human data on the tracer and its preliminary analytic validation. Methods: 5 mCi of Zr89-J591 was administered intravenously and 4 serial PET/CT scans were obtained within the following time intervals post-injection: 2-4 hours, 18-30 hours, 2-5 days, and 6-7 days. Pharmacokinetic (PK) sampling occurred at 5, 30, and 60 minutes post-injection, and at each PET scan. Patients underwent standard cross-sectional imaging, bone scintigraphy, and FDG PET scanning. Metastatic sites were biopsied in order of preference: Zr89-J591 and FDG positivity, then Zr89-J591 and FDG mismatch, and lastly standard imaging and any PET mismatch. Results: Ten patients were scanned, 132 Zr89-J591 positive sites were detected, and 12 lesions (5 bone, 6 node, 1 lung) in 8 patients were biopsied. Tissue correlation: 12/12 biopsies were positive for cancer. 11/12 biopsied lesions (5 bone, 5 node, 1 lung) were positive on Zr89-J591 imaging. 10/12 were positive by standard scans or FDG. 1 lesion was negative by Zr89-J591 PET, but positive by other modalities. Immunokinetics: Blood clearance T1/2α: 7 +/- 4.5 h (1.1-14 h); T1/2β: 62 +/- 13 h (51-89 h); Whole body clearance T1/2: 219 +/- 48 h (153-317 h). Dosimetry: Maximum retention in liver 8 +/- 1.5 SUV; Kidney 4.1 +/- 0.8 SUV; Tumor 8.2 +/- 6.5 SUV (mean SUV in bone 11.13 +/- 7.57 vs. soft tissue 4.31 +/- 2.59). Optimal time for patient imaging after injection, in terms of tumor to background ratios was 7 +/- 1 days. Conclusions: Zr89-J591 imaging demonstrates excellent tumor localization, pathology correlation, and can demonstrate the presence of tumor even when lesions are negative by standard imaging or FDG PET imaging. PK properties of the tracer have been defined. Use of Zr89-J591 to direct biopsies of metastases provides excellent yield. Further studies to examine reproducibility and post-treatment effects are planned. Clinical trial information: NCT01543659.