PET Information Research Articles

PET/MRI and Novel Targets for Breast Cancer.

Breast cancer, with its global prevalence and impact on women's health, necessitates effective early detection and accurate staging for optimal patient outcomes. Traditional imaging modalities such as mammography, ultrasound, and dynamic contrast-enhanced magnetic resonance imaging (MRI) play crucial roles in local-regional assessment, while bone scintigraphy and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) aid in evaluating distant metastasis. Despite the proven utility of 18F-FDG PET/CT in various cancers, its limitations in breast cancer, such as high false-negative rates for small and low-grade tumors, have driven exploration into novel targets for PET radiotracers, including estrogen receptor, human epidermal growth factor receptor-2, fibroblast activation protein, and hypoxia. The advent of PET/MRI, which combines metabolic PET information with high anatomical detail from MRI, has emerged as a promising tool for breast cancer diagnosis, staging, treatment response assessment, and restaging. Technical advancements including the integration of PET and MRI, considerations in patient preparation, and optimized imaging protocols contribute to the success of dedicated breast and whole-body PET/MRI. This comprehensive review offers the current technical aspects and clinical applications of PET/MRI for breast cancer. Additionally, novel targets in breast cancer for PET radiotracers beyond glucose metabolism are explored.

Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function.

Amyloid-beta (Aβ) plaque is a neuropathological hallmark of Alzheimer's disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility. To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study. ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type). Participants across various sites were involved in the ARMADA study for validating the NIHTB. 199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers). We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity. The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 - 0.76, Sensitivity 0.50, Specificity 0.88) on the held-out test set; higher than the LASSO model (0.68 (95% CI:0.68 - 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words-in-noise test (hearing), pattern comparison processing speed, odor identification, 2-minutes-walk endurance, 4-meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers. Our results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive cases (i.e., low sensitivity).

Predicting Brain Amyloid Status Using the NIH Toolbox for Assessment of Neurological and Behavioral Function (NIHTB)

AbstractBackgroundAmyloid‐beta (Ab) plaque is a pathophysiological hallmark of Alzheimer’s disease (AD). As anti‐amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility. Here, we predicted brain amyloid status in the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study using machine learning.MethodsARMADA, a multisite study, aimed to validate the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive abilities (normal, MCI, early AD dementia). We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. Each site used its own criteria to define the amyloid PET positivity (Positive for AD biomarkers, Negative, Visual reading unavailable) or CSF result (Consistent with AD, Not consistent with AD, Borderline, Indeterminate). Amyloid positivity was defined as PET being “Positive for AD biomarkers” or CSF being “Consistent with AD.” We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity.ResultsOf 462 ARMADA participants, 199 had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers). The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 – 0.76, Sensitivity 0.50, Specificity 0.88) on the held‐out test set; higher than the LASSO model (0.68 (95% CI:0.68 – 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words‐in‐noise test (hearing), pattern comparison processing speed, odor identification, 2‐minutes‐walk endurance, 4‐meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers.ConclusionOur results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive (i.e., low sensitivity) cases.

PET image denoising based on denoising diffusion probabilistic model.

Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic model (DDPM) was a distribution learning-based model, which tried to transform a normal distribution into a specific data distribution based on iterative refinements. In this work, we proposed and evaluated different DDPM-based methods for PET image denoising. Under the DDPM framework, one way to perform PET image denoising was to provide the PET image and/or the prior image as the input. Another way was to supply the prior image as the network input with the PET image included in the refinement steps, which could fit for scenarios of different noise levels. 150 brain [[Formula: see text]F]FDG datasets and 140 brain [[Formula: see text]F]MK-6240 (imaging neurofibrillary tangles deposition) datasets were utilized to evaluate the proposed DDPM-based methods. Quantification showed that the DDPM-based frameworks with PET information included generated better results than the nonlocal mean, Unet and generative adversarial network (GAN)-based denoising methods. Adding additional MR prior in the model helped achieved better performance and further reduced the uncertainty during image denoising. Solely relying on MR prior while ignoring the PET information resulted in large bias. Regional and surface quantification showed that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference achieved the best performance. DDPM-based PET image denoising is a flexible framework, which can efficiently utilize prior information and achieve better performance than the nonlocal mean, Unet and GAN-based denoising methods.

Prostate-specific membrane antigen radioguided surgery with negative histopathology: an in-depth analysis

PurposeTo identify reasons for negative histopathology of specimens from prostate-specific membrane antigen (PSMA) radioguided surgery (PSMA-RGS) in recurrent prostate cancer (PCa) after prostatectomy.MethodsOf 302 patients who underwent PSMA-RGS, 17 (5.6%) demonstrated a negative histopathology. Preoperative data, PSMA PET, PSMA SPECT, and follow-up information were analyzed retrospectively to differentiate true/false positive (TP/FP) from true/false negative (TN/FN) lesions.ResultsThe median prostate-specific antigen at PET was 0.4 ng/ml (interquartile range [IQR] 0.3–1.2). Twenty-five index lesions (median short axis 7 mm, IQR 5–8; median long-axis 12 mm, IQR 8–17) had a median SUVmax of 4 (IQR 2.6–6; median PSMA expression score 1, IQR 1–1). Six lesions were TP, twelve were FP, one was TN, and six remained unclear. All TP lesions were in the prostatic fossa or adjacent to the internal iliac arteries. Three suspected local recurrences were FP. All FP lymph nodes were located at the distal external iliac arteries or outside the pelvis. A low PSMA-expressing TN node was identified next to a common iliac artery. Unclear lesions were located next to the external iliac arteries or outside the pelvis.ConclusionIn most cases with a negative histopathology from PSMA-RGS, lesions were FP on PSMA PET. Unspecific uptake should be considered in low PSMA-expressing lymph nodes at the distal external iliac arteries or outside the pelvis, especially if no PSMA-positive lymph nodes closer to the prostatic fossa are evident. Rarely, true positive metastases were missed by surgery or histopathology.

Multimodal fusion workflow for target delineation in cardiac radioablation of ventricular tachycardia.

Cardiac radioablation (CR) is an innovative treatment to ablate cardiac arrythmia sources by radiation therapy. CR target delineation is a challenging task requiring the exploitation of highly different imaging modalities, including cardiac electro-anatomical mapping (EAM). In this work, a data integration process is proposed to alleviate the tediousness of CR target delineation by generating a fused representation of the heart, including all the information of interest resulting from the analysis and registration of electro-anatomical data, PET scan and planning computed tomography (CT) scan. The proposed process was evaluated by cardiologists during delineationtrials. The data processing pipeline was composed of the following steps. The cardiac structures of interest were segmented from cardiac CT scans using a deep learning method. The EAM data was registered to the cardiac CT scan using a point cloud based registration method. The PET scan was registered using rigid image registration. The EAM and PET information, as well as the myocardium thickness, were projected on the surface of the 3D mesh of the left ventricle. The target was identified by delineating a path on this surface that was further projected to the thickness of the myocardium to create the target volume. This process was evaluated by comparison with a standard slice-by-slice delineation with mental EAM registration. Four cardiologists delineated targets for three patients using both methods. The variability of target volumes, and the ease of use of the proposed method, wereevaluated. All cardiologists reported being more confident and efficient using the proposed method. The inter-clinician variability in delineated target volume was systematically lower with the proposed method (average dice score of 0.62 vs. 0.32 with a classical method). Delineation times were alsoimproved. A data integration process was proposed and evaluated to fuse images of interest for CR target delineation. It effectively reduces the tediousness of CR target delineation, while improving inter-clinician agreement on target volumes. This study is still to be confirmed by including more clinicians and patient data to theexperiments.

Deep learning for automatic tumor lesions delineation and prognostic assessment in multi-modality PET/CT: A prospective survey

Tumor lesion segmentation and staging in cancer patients are one of the most challenging tasks for radiologists to recommend better treatment planning like radiation therapy, personalized medicine, and surgery. Recently, Deep Learning (DL) has emerged as an assistive technology to help radiologists to characterize the biology of tumors and manage cancer patients. Positron Emission Tomography/Computed Tomography (PET/CT) multi-modality image-based tumor segmentation has gained tremendous attraction. However, the fusion of PET and CT information exposes numerous serious challenges including intra-class variability, contrast issues, modality discrepancy (difference in shape, and size of tumor), and the blurred boundaries between tumor and normal tissues (low specificity). To address these challenges, various DL-based tumor auto-segmentation methods have been proposed to consider complementary and contradictory anatomical and functional information of multi-modality PET/CT. This survey paper provides an in-depth exploration of these auto-segmentation methods. First, we discuss PET, CT weaknesses, the need for PET/CT, and the challenge of multi-modality PET/CT images. Second, we provide a detailed discussion of the parameters used to evaluate the achievements and limitations of the reviewed methods. Third, we classify the existing solutions into three major groups based on the model architecture design such as single network, multiple networks, and hybrid network models. The multiple networks are further divided into ensembles, multi-task, and Generative Adversarial Network (GAN) models. Furthermore, we present a discussion on these solutions to improve segmentation performance along with their strengths and weaknesses. Finally, we present a discussion on open research challenges and recommend potential future directions.

Functional-structural sub-region graph convolutional network (FSGCN): Application to the prognosis of head and neck cancer with PET/CT imaging

Background and objectiveAccurate risk stratification is crucial for enabling personalized treatment for head and neck cancer (HNC). Current PET/CT image-based prognostic methods include radiomics analysis and convolutional neural network (CNN), while extracting radiomics or deep features in grid Euclidean space has inherent limitations for risk stratification. Here, we propose a functional-structural sub-region graph convolutional network (FSGCN) for accurate risk stratification of HNC. MethodsThis study collected 642 patients from 8 different centers in The Cancer Imaging Archive (TCIA), 507 patients from 5 centers were used for training, and 135 patients from 3 centers were used for testing. The tumor was first clustered into multiple sub-regions by using PET and CT voxel information, and radiomics features were extracted from each sub-region to characterize its functional and structural information, a graph was then constructed to format the relationship/difference among different sub-regions in non-Euclidean space for each patient, followed by a residual gated graph convolutional network, the prognostic score was finally generated to predict the progression-free survival (PFS). ResultsIn the testing cohort, compared with radiomics or FSGCN or clinical model alone, the model PETCTFea_CTROI + Cli that integrates FSGCN prognostic score and clinical parameter achieved the highest C-index and AUC of 0.767 (95% CI: 0.759–0.774) and 0.781 (95% CI: 0.774–0.788), respectively for PFS prediction. Besides, it also showed good prognostic performance on the secondary endpoints OS, RFS, and MFS in the testing cohort, with C-index of 0.786 (95% CI: 0.778–0.795), 0.775 (95% CI: 0.767–0.782) and 0.781 (95% CI: 0.772–0.789), respectively. ConclusionsThe proposed FSGCN can better capture the metabolic or anatomic difference/interaction among sub-regions of the whole tumor imaged with PET/CT. Extensive multi-center experiments demonstrated its capability and generalization of prognosis prediction in HNC over conventional radiomics analysis.

A Novel Lung Nodule Accurate Segmentation of PET-CT Images Based on Convolutional Neural Network and Graph Model

Positron Emission Tomography and Computed Tomography(PET/CT) imaging could obtain functional metabolic feature information and anatomical localization information of the patient body. However, tumor segmentation in PET/CT images is significantly challenging for fusing of dual-modality characteristic information. In this work, we have proposed a novel deep learning-based graph model network which can automatically fuse dual-modality information for tumor area segmentation. Our method rationally utilizes the advantage of each imaging modality(PET: the superior contrast, CT: the superior spatial resolution). We formulate this task as a Conditional Random Field(CRF) based on multi-scale fusion and dual-modality co-segmentation of object image with a normalization term which balances the segmentation divergence between PET and CT. This mechanism considers that the spatial varying characteristics acquire different scales, which encode various feature information over different modalities. The ability of our method was evaluated to detect and segment tumor regions with different fusion approaches using a dataset of PET/CT clinical tumor images. The results illustrated that our method effectively integrates both PET and CT modalities information, deriving segmentation accuracy result of 0.86 in DSC and the sensitivity of 0.83, which is 3.61% improvement compared to the W-Net.

Motivation of Portuguese-Speaking Pet Owners Seeking Online Pet Health and Husbandry Information

Internet use for pet health information is increasing in popularity among pet owners. So far, evidence on motivations and topics of interest is limited to surveys on English speaking countries. The objective was to evaluate the motivations of Portuguese-speaking pet owners when visiting a pet information website, written by a certified veterinarian. In situ needs and motivations of visitors were evaluated by classifying online questions (n = 202) and conducting a short questionnaire in the website sidebar (n = 453). Most pet owners were looking for general advice (73.9%), especially on systems with visible disease signs, being motivated by the lack of access to veterinary services due to costs (17.7%), support from other pet owners (11.3%), and complementing the insufficient information provided by the veterinarian (7.7%). Brazilians reported more lack of veterinary access due to costs compared to Portuguese respondents. Thus, Portuguese-speaking pet owners engaged in online searches to seek affordable veterinary care, better information, and emotional support.

Impact of SSTR PET on Inter-Observer Variability of Target Delineation of Meningioma and the Possibility of Using Threshold-Based Segmentations in Radiation Oncology.

Simple SummaryDifferences in tumor segmentations between radiation oncologists is one of the largest sources of uncertainty in radiation therapy planning. This study investigated the influence of additional functional information from somatostatin receptor PET imaging on the inter observer variability in the delineation of meningioma. Further, this study assessed the usability of a simple thresholding approach for lesion delineation. It could be shown, that additional PET information was able to significantly reduce the inter observer variability. The threshold based delineation approach required a relatively low threshold value and showed only moderate agreement with the radiation oncologists.Aim: The aim of this study was to assess the effects of including somatostatin receptor agonist (SSTR) PET imaging in meningioma radiotherapy planning by means of changes in inter-observer variability (IOV). Further, the possibility of using threshold-based delineation approaches for semiautomatic tumor volume definition was assessed. Patients and Methods: Sixteen patients with meningioma undergoing fractionated radiotherapy were delineated by five radiation oncologists. IOV was calculated by comparing each delineation to a consensus delineation, based on the simultaneous truth and performance level estimation (STAPLE) algorithm. The consensus delineation was used to adapt a threshold-based delineation, based on a maximization of the mean Dice coefficient. To test the threshold-based approach, seven patients with SSTR-positive meningioma were additionally evaluated as a validation group. Results: The average Dice coefficients for delineations based on MRI alone was 0.84 ± 0.12. For delineation based on MRI + PET, a significantly higher dice coefficient of 0.87 ± 0.08 was found (p < 0.001). The Hausdorff distance decreased from 10.96 ± 11.98 mm to 8.83 ± 12.21 mm (p < 0.001) when adding PET for the lesion delineation. The best threshold value for a threshold-based delineation was found to be 14.0% of the SUVmax, with an average Dice coefficient of 0.50 ± 0.19 compared to the consensus delineation. In the validation cohort, a Dice coefficient of 0.56 ± 0.29 and a Hausdorff coefficient of 27.15 ± 21.54 mm were found for the threshold-based approach. Conclusions: SSTR-PET added to standard imaging with CT and MRI reduces the IOV in radiotherapy planning for patients with meningioma. When using a threshold-based approach for PET-based delineation of meningioma, a relatively low threshold of 14.0% of the SUVmax was found to provide the best agreement with a consensus delineation.

Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment

BackgroundIn patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer’s disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may therefore be of interest to find suitable biomarkers to preselect patients benefitting most from additional workup of the A-status. In this study, we propose a machine learning–based gatekeeping system for the prediction of A-status on the grounds of pre-existing information on APOE-genotype 18F-FDG PET, age, and sex.MethodsThree hundred and forty-two MCI patients were used to train different machine learning classifiers to predict A-status majority classes among APOE-ε4 non-carriers (APOE4-nc; majority class: amyloid negative (Aβ-)) and carriers (APOE4-c; majority class: amyloid positive (Aβ +)) from 18F-FDG-PET, age, and sex. Classifiers were tested on two different datasets. Finally, frequencies of progression to dementia were compared between gold standard and predicted A-status.ResultsAβ- in APOE4-nc and Aβ + in APOE4-c were predicted with a precision of 87% and a recall of 79% and 51%, respectively. Predicted A-status and gold standard A-status were at least equally indicative of risk of progression to dementia.ConclusionWe developed an algorithm allowing approximation of A-status in MCI with good reliability using APOE-genotype, 18F-FDG PET, age, and sex information. The algorithm could enable better estimation of individual risk for developing AD based on existing biomarker information, and support efficient selection of patients who would benefit most from further etiological clarification. Further potential utility in clinical routine and clinical trials is discussed.

Unsupervised-learning-based method for chest MRI–CT transformation using structure constrained unsupervised generative attention networks

The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner simultaneously acquires metabolic information via PET and morphological information using MRI. However, attenuation correction, which is necessary for quantitative PET evaluation, is difficult as it requires the generation of attenuation-correction maps from MRI, which has no direct relationship with the gamma-ray attenuation information. MRI-based bone tissue segmentation is potentially available for attenuation correction in relatively rigid and fixed organs such as the head and pelvis regions. However, this is challenging for the chest region because of respiratory and cardiac motions in the chest, its anatomically complicated structure, and the thin bone cortex. We propose a new method using unsupervised generative attentional networks with adaptive layer-instance normalisation for image-to-image translation (U-GAT-IT), which specialised in unpaired image transformation based on attention maps for image transformation. We added the modality-independent neighbourhood descriptor (MIND) to the loss of U-GAT-IT to guarantee anatomical consistency in the image transformation between different domains. Our proposed method obtained a synthesised computed tomography of the chest. Experimental results showed that our method outperforms current approaches. The study findings suggest the possibility of synthesising clinically acceptable computed tomography images from chest MRI with minimal changes in anatomical structures without human annotation.

Development of Android-based Petmily Application

Pet is a loyal companion animal that always give accompany and entertainment to people especially pet lovers. Pet lovers treat their pet as one of their family members and always record their every moment in photo and post to the social media as a memory. This is because pet can give the advantage in both emotional and physical to pet lovers, for example, pet plays the role as a loyal companion to pet lovers especially for those are alone and feel lonely. The objective of this project is to develop Petmily application in mobile based android platform. The Petmily application is a social and lifestyle application for pet lovers. It is developed in English version. This application serves as a pet social media platform, and at the same time provides pet information platform regarding to common pet emergency and basic obedience pet training. Task recording platform development like a to-do list also included. The methodology used in this development of this Petmily application is Agile model. Generally, the satisfying results with 93.33% positive feedbacks have been obtained from the testing and it can be concluded that Petmily application is accepted by the target users based on the system usability scale. This application will be monitored from time to time to improve the performance and it is hoped that benefits its target users.

Correlation Between Quantitative PSMA PET Parameters and Clinical Risk Factors in Non-Metastatic Primary Prostate Cancer Patients

BackgroundPSMA PET is frequently used for staging of prostate cancer patients. Furthermore, there is increasing interest to use PET information for personalized local treatment approaches in surgery and radiotherapy, especially for focal treatment strategies. However, it is not well established which quantitative imaging parameters show highest correlation with clinical and histological tumor aggressiveness.MethodsThis is a retrospective analysis of 135 consecutive patients with non-metastatic prostate cancer and PSMA PET before any treatment. Clinical risk parameters (PSA values, Gleason score and D’Amico risk group) were correlated with quantitative PET parameters maximum standardized uptake value (SUVmax), mean SUV (SUVmean), tumor asphericity (ASP) and PSMA tumor volume (PSMA-TV).ResultsMost of the investigated imaging parameters were highly correlated with each other (correlation coefficients between 0.20 and 0.95). A low to moderate, however significant, correlation of imaging parameters with PSA values (0.19 to 0.45) and with Gleason scores (0.17 to 0.31) was observed for all parameters except ASP which did not show a significant correlation with Gleason score. Receiver operating characteristics for the detection of D’Amico high-risk patients showed poor to fair sensitivity and specificity for all investigated quantitative PSMA PET parameters (Areas under the curve (AUC) between 0.63 and 0.73). Comparison of AUC between quantitative PET parameters by DeLong test showed significant superiority of SUVmax compared to SUVmean for the detection of high-risk patients. None of the investigated imaging parameters significantly outperformed SUVmax.ConclusionOur data confirm prior publications with lower number of patients that reported moderate correlations of PSMA PET parameters with clinical risk factors. With the important limitation that Gleason scores were only biopsy-derived in this study, there is no indication that the investigated additional parameters deliver superior information compared to SUVmax.

Automatic classification of lymphoma lesions in FDG-PET-Differentiation between tumor and non-tumor uptake.

The automatic classification of lymphoma lesions in PET is a main topic of ongoing research. An automatic algorithm would enable the swift evaluation of PET parameters, like texture and heterogeneity markers, concerning their prognostic value for patients outcome in large datasets. Moreover, the determination of the metabolic tumor volume would be facilitated. The aim of our study was the development and evaluation of an automatic algorithm for segmentation and classification of lymphoma lesions in PET. Pre-treatment PET scans from 60 Hodgkin lymphoma patients from the EuroNet-PHL-C1 trial were evaluated. A watershed algorithm was used for segmentation. For standardization of the scan length, an automatic cropping algorithm was developed. All segmented volumes were manually classified into one of 14 categories. The random forest method and a nested cross-validation was used for automatic classification and evaluation. Overall, 853 volumes were segmented and classified. 203/246 tumor lesions and 554/607 non-tumor volumes were classified correctly by the automatic algorithm, corresponding to a sensitivity, a specificity, a positive and a negative predictive value of 83%, 91%, 79% and 93%. In 44/60 (73%) patients, all tumor lesions were correctly classified. In ten out of the 16 patients with misclassified tumor lesions, only one false-negative tumor lesion occurred. The automatic classification of focal gastrointestinal uptake, brown fat tissue and composed volumes consisting of more than one tissue was challenging. Our algorithm, trained on a small number of patients and on PET information only, showed a good performance and is suitable for automatic lymphoma classification.

FLT-PET/CT in Non-Small Cell Lung Cancer Treated With SBRT- A Pilot Study

<h3>Purpose/Objective(s)</h3> To prospectively assess the value of FLT-PET in differentiating tumor recurrence from radiation-induced lung fibrosis. The primary objective was to compare FLT uptake in three cohort of patients: 1) before SBRT, 2) stable lung fibrosis after SBRT, and 3) suspicious/proven local recurrence. The secondary objective was to optimize FLT PET imaging sequences for lung cancer patients and compare FLT uptake in 4D (respiratory sorted) versus free-breathing FLT-PET scans. <h3>Materials/Methods</h3> Early-stage (T1-2N0M0 or T3N0M0 limited to the chest wall as per 7^th edition American Joint committee on cancer staging manual) presumed or biopsy-proven non-small cell lung carcinoma, aged more than 18 years and planned for SBRT at the institution or followed up after SBRT with radiographic findings of fibrosis or recurrence were considered eligible for the study. Patients were assigned to one of three study cohorts. All patients underwent imaging with FLT PET CT- prior to SBRT in cohort 1 and at fibrosis or recurrence in cohort 2 and 3. The planned sample size was 20 patients in each cohort, 60 patients in total. The FLT PET SUV variables like SUV max, SUV mean, SUV2Dpeak, SUV3Dpeak, SUV 50 and SUV95 were compared between the three cohorts using Kruskal Wallis test. The correlation of 4D phased matched and 3D PET SUV variables was performed using spearman correlation coefficient. Statistical significance was set at p<0.05. <h3>Results</h3> Forty one patients were accrued in the study (20 in cohort 1, 16 in cohort 2 and 5 in cohort3). The median age of the entire cohort was 77 years (range 46-89). Majority were diagnosed with stage I lung cancer (86%) and 48Gy in 4 fractions was the most commonly used dose fractionation (59%). A 4D FLT PET was performed in 35 patients. There was no significant difference in the FLT uptake SUV variables between 4D (respiratory sorted) versus free-breathing scans (r=0.8-1). Using the 3D FLT PET information, SUV peak, SUV mean, SUV max were significantly lower in fibrosis cohort compared to recurrence and pretreatment cohorts (Table 1). The delayed FLT PET values SUV 50 and 95 values in recurrence cohort were significantly similar to pretreatment cohort and significantly different compared to fibrosis. <h3>Conclusion</h3> FLT PET may be helpful in differentiating SBRT related fibrosis from recurrence. This study was limited by the small sample size in recurrence cohort and larger validation studies are needed to ascertain utility of FLT PET to confirm the findings.

The speed limits of tau spreading: The contribution of regional amyloid and education

AbstractBackgroundHere, we investigated the contribution of education and regional amyloid as potential de‐ and accelerators in the spread of tau pathology.Method85 amyloid‐positive subjects (age=76.80±1.08, education=16.70 ±.50 years, M/F=42/43) were included, for whom [18F]AV‐1451 PET at two timepoints, baseline [18F]AV‐45 PET, ApoE‐status and clinical information were available at ADNI. [18F]AV‐1451 PETs were intensity‐standardized (reference: inferior cerebellum) and z‐transformed (control sample: 35 amyloid‐negative subjects). Baseline and follow‐up tau‐PET z‐maps were converted to volume‐maps (z‐score threshold &gt; 1.96). Based on these volume‐maps, tau‐changes over time were assessed: 1) tau speed (i.e. newly affected volume at follow‐up), 2) tau level rise (i.e. tau increase in previously affected volume). These two dependent variables were used in regression analyses including education, global amyloid, baseline tau, ApoE, cognitive function, sex, and age as predictors. Additionally, baseline amyloid‐burden was regionally correlated in 32 Freesurfer‐ROIs with newly tau‐affected volume and with mean z‐score change in five Meta‐ROIs (entorhinal cortex, inferior‐temporal, precuneus, middle‐occipital, oribito‐frontal), correcting for multiple comparisons.ResultEducation contributed as decelerator (β=‐.322, p=.037), whereas global amyloid (β=.262, p=.029) and baseline tau (β=.283, p=.028) facilitated tau speed. Tau level rise was positively associated with age (β=‐.211, p=.017) and baseline tau (β=.710, p&lt;.001). Tau spread to the precuneus was related with greater cingulate amyloid‐burden, whereas tau level rise in temporal, parietal and occipital regions was associated with orbito‐frontal and cingulate amyloid‐burden.ConclusionA combination of de‐ and accelerating factors and regional dissociations likely contribute to the temporo‐spatial evolution of tau pathology.

A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

PET imaging is an important diagnostic tool for management of patients with cancer and other diseases. Medical decisions based on quantitative PET information could potentially benefit from the availability of tools for evaluation of associated uncertainties. Raw PET data can be viewed as a sample from an inhomogeneous Poisson process so there is the possibility to directly apply bootstrapping to raw projection-domain list-mode data. Unfortunately this is computationally impractical, particularly if data reconstruction is iterative or the acquisition protocol is dynamic. We develop a flexible statistical linear model analysis to be used with multi-frame PET image data to create valid bootstrap samples. The technique is illustrated using data from dynamic PET studies with fluoro-deoxyglucose (FDG) and fluoro-thymidine (FLT) in brain and breast cancer patients. As is often the case with dynamic PET studies, data have been archived without raw list-mode information. Using the bootstrapping technique maps of kinetic parameters and associated uncertainties are obtained. The quantitative performance of the approach is assessed by simulation. The proposed image-domain bootstrap is found to substantially match the projection-domain alternative. Analysis of results points to a close relation between relative uncertainty in voxel-level kinetic parameters and local reconstruction error. This is consistent with statistical theory.

Improving Tumor Hypoxia Location in 18F-Misonidazole PET with Dynamic Contrast-enhanced MRI Using Quantitative Electron Paramagnetic Resonance Partial Oxygen Pressure Images.

To enhance the spatial accuracy of fluorine 18 (18F) misonidazole (MISO) PET imaging of hypoxia by using dynamic contrast-enhanced (DCE) MR images as a basis for modifying PET images and by using electron paramagnetic resonance (EPR) partial oxygen pressure (pO2) as the reference standard. Mice (n = 10) with leg-borne MCa4 mammary carcinomas underwent EPR imaging, T2-weighted and DCE MRI, and 18F-MISO PET/CT. Images were registered to the same space for analysis. The thresholds of hypoxia for PET and EPR images were tumor-to-muscle ratios greater than or equal to 2.2 mm Hg and less than or equal to 14 mm Hg, respectively. The Dice similarity coefficient (DSC) and Hausdorff distance (d H ) were used to quantify the three-dimensional overlap of hypoxia between pO2 EPR and 18F-MISO PET images. A training subset (n = 6) was used to calculate optimal DCE MRI weighting coefficients to relate EPR to the PET signal; the group average weights were then applied to all tumors (from six training mice and four test mice). The DSC and d H were calculated before and after DCE MRI-corrected PET images were obtained to quantify the improvement in overlap with EPR pO2 images for measuring tumor hypoxia. The means and standard deviations of the DSC and d H between hypoxic regions in original PET and EPR images were 0.35 mm ± 0.23 and 5.70 mm ± 1.7, respectively, for images of all 10 mice. After implementing a preliminary DCE MRI correction to PET data, the DSC increased to 0.86 mm ± 0.18 and the d H decreased to 2.29 mm ± 0.70, showing significant improvement (P < .001) for images of all 10 mice. Specifically, for images of the four independent test mice, the DSC improved with correction from 0.19 ± 0.28 to 0.80 ± 0.29 (P = .02), and the d H improved from 6.40 mm ± 2.5 to 1.95 mm ± 0.63 (P = .01). Using EPR information as a reference standard, DCE MRI information can be used to correct 18F-MISO PET information to more accurately reflect areas of hypoxia.Keywords: Animal Studies, Molecular Imaging, Molecular Imaging-Cancer, PET/CT, MR-Dynamic Contrast Enhanced, MR-Imaging, PET/MR, Breast, Oncology, Tumor Mircoenvironment, Electron Paramagnetic ResonanceSupplemental material is available for this article.© RSNA, 2021.