Dual time-point [18F]FDG PET imaging for quantification of metabolic uptake rate: Evaluation of a simple, clinically feasible method

PURPOSE

We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (Ki) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy.

METHODS

We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.). We determined Ki for a total of 81 lesions. Ki quantification from full dynamic PET data (Patlak-Ki) and Ki from DTP (DTP-Ki) were compared. In addition, we scaled a population-based input function (PBIFscl) with the image-derived blood pool activity sampled at different time points to assess the best scaling time-point for Ki quantifications in the simulation data.

RESULTS

In the simulation study, Ki estimated using DTP via (30,60-min), (30,90-min), (60,90-min), and (60,120-min) samples showed strong correlations (r ≥ 0.944, P < 0.0001) with the true value of Ki. The DTP results with the PBIFscl at 60-min time-point in (30,60-min), (60,90-min), and (60,120-min) were linearly related to the true Ki with a slope of 1.037, 1.008, 1.013 and intercept of -6 × 10-4, 2 × 10-5, 5 × 10-5, respectively. In a clinical study, strong correlations (r ≥ 0.833, P < 0.0001) were observed between Patlak-Ki and DTP-Ki. The Patlak-derived mean values of Ki, tumor-to-background-ratio, signal-to-noise-ratio, and contrast-to-noise-ratio were linearly correlated with the DTP method.

CONCLUSIONS

Besides calculating the retention index as a commonly used quantification parameter inDTP imaging,our DTP method can accurately estimate Ki.

Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade

Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on 68 Ga-PSMA-11 PET/CT images in prostate cancer patients. This study included 90 eligible participants out of 244 biopsy-proven prostate cancer patients who underwent staging 68Ga-PSMA-11 PET/CT imaging. The patients were divided into high and low-intermediate groups based on their Gleason scores. The PET-only images were manually segmented, both lesion-based and whole prostate, by two experienced nuclear medicine physicians. Four feature selection algorithms and five classifiers were applied to Combat-harmonized and non-harmonized datasets. To evaluate the model's generalizability across different institutions, we performed leave-one-center-out cross-validation (LOOCV). The metrics derived from the receiver operating characteristic curve were used to assess model performance. In the whole prostate segmentation, combining the ANOVA algorithm as the feature selector with Random Forest (RF) and Extra Trees (ET) classifiers resulted in the highest performance among the models, with an AUC of 0.78 and 083, respectively. In the lesion-based segmentation, the highest AUC was achieved by MRMR feature selector + Linear Discriminant Analysis (LDA) and Logistic Regression (LR) classifiers (0.76 and 0.79, respectively). The LOOCV results revealed that both the RF_ANOVA and ET_ANOVA models showed high levels of accuracy and generalizability across different centers, with an average AUC value of 0.87 for the ET_ANOVA combination. Machine learning-based analysis of radiomics features extracted from 68Ga-PSMA-11 PET/CT scans can accurately classify prostate tumors into low-risk and intermediate- to high-risk groups.

Quantifying partial volume effect in SPECT and planar imaging: optimizing region of interest for activity concentration estimation in different sphere sizes

INTRODUCTION

To quantify the partial volume effect in single photon emission tomography (SPECT) and planar images of Carlson phantom as well as providing an optimum region of interest (ROI) required to more accurately estimate the activity concentration for different sphere sizes.

METHODS

131 I solution with the 161.16 kBq/ml concentration was uniformly filled into the different spheres of Carlson phantom (cold background condition) with the diameters of 7.3, 9.2, 11.4, 14.3, 17.9, 22.4 and 29.9 mm, and there was no background activity. In the hot background condition, the spheres were filled with the solution of 131 I with the 1276.5 kBq/ml addition to the background activity concentration of 161.16 kBq/ml in all the phantoms. The spheres were mounted inside the phantom and underwent SPECT and planar images. ROI was drawn closely on the boundary of each sphere image and it was extended to extract the true count.

RESULTS

In the cold background condition, the recovery coefficient (RC) value for SPECT images ranged between 0.8 and 1.03. However, in planar imaging, the RC value was 0.72 for the smallest sphere size and it increased for larger spheres until 0.98 for 29.9 mm. In the hot background condition, the RC value for sphere diameters larger than 20 mm was overestimated more than in the cold background condition. The ROI/size required to more accurately determine activity concentration for the cold background ranged from 1.18 to 2.7. However, in the hot background condition, this ratio varied from 1.34 to 4.05.

CONCLUSION

In the quantification of partial volume effects, the spill-out effect seems to play a crucial role in the distribution of the image counts beyond the boundaries of the image pixels. However, more investigations are needed to accurately characterize limitations regarding the object size, background levels, and other factors.

An Update on the Role of mpMRI and 68Ga-PSMA PET Imaging in Primary and Recurrent Prostate Cancer

The objective of this work was to review comparisons of the efficacy of 68Ga-PSMA-11 (prostate-specific membrane antigen) PET/CT and multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer among patients undergoing initial staging prior to radical prostatectomy or experiencing recurrent prostate cancer, based on histopathological data. A comprehensive search was conducted in PubMed and Web of Science, and relevant articles were analyzed with various parameters, including year of publication, study design, patient count, age, PSA (prostate-specific antigen) value, Gleason score, standardized uptake value (SUVmax), detection rate, treatment history, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and PI-RADS (prostate imaging reporting and data system) scores. Only studies directly comparing PSMA-PET and mpMRI were considered, while those examining combined accuracy or focusing on either modality alone were excluded. In total, 24 studies comprising 1717 patients were analyzed, with the most common indication for screening being staging, followed by relapse. The findings indicated that 68Ga-PSMA-PET/CT effectively diagnosed prostate cancer in patients with suspected or confirmed disease, and both methods exhibited comparable efficacy in identifying lesion-specific information. However, notable heterogeneity was observed, highlighting the necessity for standardization of imaging and histopathology systems to mitigate inter-study variability. Future research should prioritize evaluating the combined diagnostic performance of both modalities to enhance sensitivity and reduce unnecessary biopsies. Overall, the utilization of PSMA-PET and mpMRI in combination holds substantial potential for significantly advancing the diagnosis and management of prostate cancer.

Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR

BACKGROUND

Prostate-specific membrane antigen (PSMA) PET/CT imaging is widely used for quantitative image analysis, especially in radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC). Unknown features influencing PSMA biodistribution can be explored by analyzing segmented organs at risk (OAR) and lesions. Manual segmentation is time-consuming and labor-intensive, so automated segmentation methods are desirable. Training deep-learning segmentation models is challenging due to the scarcity of high-quality annotated images. Addressing this, we developed shifted windows UNEt TRansformers (Swin UNETR) for fully automated segmentation. Within a self-supervised framework, the model's encoder was pre-trained on unlabeled data. The entire model was fine-tuned, including its decoder, using labeled data.

METHODS

In this work, 752 whole-body [68Ga]Ga-PSMA-11 PET/CT images were collected from two centers. For self-supervised model pre-training, 652 unlabeled images were employed. The remaining 100 images were manually labeled for supervised training. In the supervised training phase, 5-fold cross-validation was used with 64 images for model training and 16 for validation, from one center. For testing, 20 hold-out images, evenly distributed between two centers, were used. Image segmentation and quantification metrics were evaluated on the test set compared to the ground-truth segmentation conducted by a nuclear medicine physician.

RESULTS

The model generates high-quality OARs and lesion segmentation in lesion-positive cases, including mCRPC. The results show that self-supervised pre-training significantly improved the average dice similarity coefficient (DSC) for all classes by about 3%. Compared to nnU-Net, a well-established model in medical image segmentation, our approach outperformed with a 5% higher DSC. This improvement was attributed to our model's combined use of self-supervised pre-training and supervised fine-tuning, specifically when applied to PET/CT input. Our best model had the lowest DSC for lesions at 0.68 and the highest for liver at 0.95.

CONCLUSIONS

We developed a state-of-the-art neural network using self-supervised pre-training on whole-body [68Ga]Ga-PSMA-11 PET/CT images, followed by fine-tuning on a limited set of annotated images. The model generates high-quality OARs and lesion segmentation for PSMA image analysis. The generalizable model holds potential for various clinical applications, including enhanced RLT and patient-specific internal dosimetry.

Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement

PURPOSE

Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images.

METHODS

Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients' images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC).

RESULTS

The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value < 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging.

CONCLUSION

The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets.

Comparable assessment of adolescent repeated physical or psychological stress effects on adult cardiac performance in female rats

Extensive evidence highlights a robust connection between various forms of chronic stress and cardiovascular disease (CVD). In today's fast-paced world, with chronic stressors abound, CVD has emerged as a leading global cause of mortality. The intricate interplay of physical and psychological stressors triggers distinct neural networks within the brain, culminating in diverse health challenges. This study aims to discern the unique impacts of chronic physical and psychological stress on the cardiovascular system, unveiling their varying potencies in precipitating CVD. Twenty-one adolescent female rats were methodically assigned to three groups: (1) control (n = 7), (2) physical stress (n = 7), and (3) psychological stress (n = 7). Employing a two-compartment enclosure, stressors were administered to the experimental rats over five consecutive days, each session lasting 10 min. After a 1.5-month recovery period post-stress exposure, a trio of complementary techniques characterized by high specificity or high sensitivity were employed to meticulously evaluate CVD. Echocardiography and single-photon emission computed tomography (SPECT) were harnessed to scrutinize left ventricular architecture and myocardial viability, respectively. Subsequently, the rats were ethically sacrificed to facilitate heart removal, followed by immunohistochemistry staining targeting glial fibrillary acidic protein (GFAP). Rats subjected to psychological stress showed a wider range of significant cardiac issues compared to control rats. This included left ventricular hypertrophy [IVSd: 0.1968 ± 0.0163 vs. 0.1520 ± 0.0076, P < 0.05; LVPWd: 0.2877 ± 0.0333 vs. 0.1689 ± 0.0057, P < 0.01; LVPWs: 0.3180 ± 0.0382 vs. 0.2226 ± 0.0121, P < 0.05; LV-mass: 1.283 ± 0.0836 vs. 1.000 ± 0.0241, P < 0.01], myocardial ischemia [21.30% vs. 32.97%, P < 0.001], and neuroinflammation. This outcome underscores the imperative of prioritizing psychological well-being during adolescence, presenting a compelling avenue to curtail the prevalence of CVD in adulthood. Furthermore, extending such considerations to individuals grappling with CVD might prospectively enhance their overall quality of life.

Vimentin-targeted radiopeptide 99m Tc-HYNIC-(tricine/EDDA)-VNTANST: a promising drug for pulmonary fibrosis imaging

OBJECTIVE

Idiopathic pulmonary fibrosis (IPF) is a fatal disease characterized by the accumulation of extracellular matrix. Because there is no effective treatment for advanced IPF to date, its early diagnosis can be critical. Vimentin is a cytoplasmic intermediate filament that is significantly up-regulated at the surface of fibrotic foci with a crucial role in fibrotic morphological changes.

METHODS

In the present study, VNTANST sequence as a known vimentin-targeting peptide was conjugated to hydrazinonicotinic acid (HYNIC) and labeled with 99m Tc. The stability test in saline and human plasma and log P determination were performed. Next, the biodistribution study and single photon emission computed tomography (SPECT) integrated with computed tomography (CT) scanning were performed in healthy and bleomycin-induced fibrosis mice models.

RESULTS

The 99m Tc-HYNIC-(tricine/EDDA)-VNTANST showed a hydrophilic nature (log P  = -2.20 ± 0.38) and high radiochemical purity > 97% and specific activity (336 Ci/mmol). The radiopeptide was approximately 93% and 86% intact in saline and human plasma within 6 h, respectively. The radiopeptide was substantially accumulated in the pulmonary fibrotic lesions (test vs. control = 4.08 ± 0.08% injected dose per gram (ID/g) vs. 0.36 ± 0.01% ID/g at 90 min postinjection). SPECT-CT images in fibrosis-bearing mice also indicated the fibrotic foci and kidneys.

CONCLUSION

Because there is no available drug for the treatment of advanced pulmonary fibrosis, early diagnosis is the only chance. The 99m Tc-HYNIC-(tricine/EDDA)-VNTANST could be a potential tracer for SPECT imaging of pulmonary fibrosis.

Diagnostic value of [ 68 Ga]Ga-Pentixafor versus [ 18 F]FDG PET/CTs in non-small cell lung cancer: a head-to-head comparative study

OBJECTIVE

In this study, we aimed to compare the diagnostic value of [ 68 Ga]Ga-Pentixafor and [ 18 F]FDG PET/CT in the evaluation of non-small cell lung cancer (NSCLC) patients.

METHODS

Patients with pathology-proven NSCLC were prospectively included. Patients underwent [ 18 F]FDG and [ 68 Ga]Ga-Pentixafor PET/CT within 1 week. All suspicious lesions were interpreted as benign or malignant, and the corresponding PET/CT semi-quantitative parameters were recorded. A two-sided P -value <0.05 was considered significant.

RESULTS

Twelve consecutive NSCLC patients (mean age: 60 ± 7) were included. All patients underwent both [ 18 F]FDG and [ 68 Ga]Ga-Pentixafor PET/CT scans with a median interval of 2 days. Overall, 73 abnormal lesions were detected, from which 58 (79%) were concordant between [ 18 F]FDG and [ 68 Ga]Ga-Pentixafor PET/CT. All primary tumors were clearly detectable in both scans visually. Also, [ 68 Ga]Ga-Pentixafor PET/CT demonstrated rather comparable results with [ 18 F]FDG PET/CT scan in detecting metastatic lesions. However, malignant lesions demonstrated significantly higher SUVmax and SUVmean in [ 18 F]FDG PET/CT ( P -values <0.05). Regarding the advantages, [ 68 Ga]Ga-Pentixafor depicted two brain metastases that were missed by [ 18 F]FDG PET/CT. Also, a highly suspicious lesion for recurrence on [ 18 F]FDG PET/CT scan was correctly classified as benign by subsequent [ 68 Ga]Ga-Pentixafor PET/CT.

CONCLUSION

[ 68 Ga]Ga-Pentixafor PET/CT was concordant with [ 18 F]FDG PET/CT in detecting primary NSCLC tumors and could visualize the majority of metastatic lesions. Moreover, this modality was found to be potentially helpful in excluding tumoural lesions when the [ 18 F]FDG PET/CT was equivocal, as well as in detecting brain metastasis where [ 18 F]FDG PET/CT suffers from poor sensitivity. However, the count statistics were significantly lower.

Radiomics predictive modeling from dual-time-point FDG PET Ki parametric maps: application to chemotherapy response in lymphoma

BACKGROUND

To investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [¹⁸F]FDG PET metabolic uptake rate K_i parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients.

METHODS

We analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP K_i parametric maps. Spearman's rank correlations were calculated between static and DTP features to identify features with potential additional information. We first employed univariate analysis to determine correlations between individual features, and subsequently utilized multivariate analysis to derive predictive models utilizing DTP and static radiomics features before and after ComBat harmonization. For multivariate modeling, we utilized both the minimum redundancy maximum relevance feature selection technique and the XGBoost classifier. To evaluate our model, we partitioned the patient datasets into training/validation and testing sets using an 80/20% split. Different metrics for classification including area under the curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC) were reported in test sets.

RESULTS

Via Spearman's rank correlations, there was negligible to moderate correlation between 32 out of 65 DTP features and some static features (ρ < 0.7); all the other 33 features showed high correlations (ρ ≥ 0.7). In univariate modeling, no significant difference between AUC of DTP and static features was observed. GLRLM_RLNU from static features demonstrated a strong correlation (AUC = 0.75, p value = 0.0001, q value = 0.0007) with therapy response. The most predictive DTP features were GLCM_Energy, GLCM_Entropy, and Uniformity, each with AUC = 0.73, p value = 0.0001, and q value < 0.0005. In multivariate analysis, the mean ranges of AUCs increased following harmonization. Use of harmonization plus combining DTP and static features was shown to provide significantly improved predictions (AUC = 0.97 ± 0.02, accuracy = 0.89 ± 0.05, sensitivity = 0.92 ± 0.09, and specificity = 0.88 ± 0.05). All models depicted significant performance in terms of AUC, ACC, SEN, and SPE (p < 0.05, Mann-Whitney test).

CONCLUSIONS

Our results demonstrate significant value in harmonization of radiomics features as well as combining DTP and static radiomics models for predicting response to chemotherapy in lymphoma patients.

QCard-NM: Developing a semiautomatic segmentation method for quantitative analysis of the right ventricle in non-gated myocardial perfusion SPECT imaging

BACKGROUND

Recent studies have shown that the right ventricular (RV) quantitative analysis in myocardial perfusion imaging (MPI) SPECT can be beneficial in the diagnosis of many cardiopulmonary diseases. This study proposes a new algorithm for right ventricular 3D segmentation and quantification.

METHODS

The proposed Quantitative Cardiac analysis in Nuclear Medicine imaging (QCard-NM) algorithm provides RV myocardial surface estimation and creates myocardial contour using an iterative 3D model fitting method. The founded contour is then used for quantitative RV analysis. The proposed method was assessed using various patient datasets and digital phantoms. First, the physician's manually drawn contours were compared to the QCard-NM RV segmentation using the Dice similarity coefficient (DSC). Second, using repeated MPI scans, the QCard-NM's repeatability was evaluated and compared with the QPS (quantitative perfusion SPECT) algorithm. Third, the bias of the calculated RV cavity volume was analyzed using 31 digital phantoms using the QCard-NM and QPS algorithms. Fourth, the ability of QCard-NM analysis to diagnose coronary artery diseases was assessed in 60 patients referred for both MPI and coronary angiography.

RESULTS

The average DSC value was 0.83 in the first dataset. In the second dataset, the coefficient of repeatability of the calculated RV volume between two repeated scans was 13.57 and 43.41 ml for the QCard-NM and QPS, respectively. In the phantom study, the mean absolute percentage errors for the calculated cavity volume were 22.6% and 42.2% for the QCard-NM and QPS, respectively. RV quantitative analysis using QCard-NM in detecting patients with severe left coronary system stenosis and/or three-vessel diseases achieved a fair performance with the area under the ROC curve of 0.77.

CONCLUSION

A novel model-based iterative method for RV segmentation task in non-gated MPI SPECT is proposed. The precision, accuracy, and consistency of the proposed method are demonstrated by various validation techniques. We believe this preliminary study could lead to developing a framework for improving the diagnosis of cardiopulmonary diseases using RV quantitative analysis in MPI SPECT.

Attenuation Correction for Dedicated Cardiac SPECT Imaging Without Using Transmission Data

Objectives

Attenuation correction (AC) using transmission scanning-like computed tomography (CT) is the standard method to increase the accuracy of cardiac single-photon emission computed tomography (SPECT) images. Recently developed dedicated cardiac SPECT do not support CT, and thus, scans on these systems are vulnerable to attenuation artifacts. This study presented a new method for generating an attenuation map directly from emission data by segmentation of precisely non-rigid registration extended cardiac-torso (XCAT)-digital phantom with cardiac SPECT images.

Methods

In-house developed non-rigid registration algorithm automatically aligns the XCAT- phantom with cardiac SPECT image to precisely segment the contour of organs. Pre-defined attenuation coefficients for given photon energies were assigned to generate attenuation maps. The CT-based attenuation maps were used for validation with which cardiac SPECT/CT data of 38 patients were included. Segmental myocardial counts of a 17-segment model from these databases were compared based on the basis of the paired t-test.

Results

The mean, and standard deviation of the mean square error and structural similarity index measure of the female stress phase between the proposed attenuation maps and the CT attenuation maps were 6.99±1.23% and 92±2.0%, of the male stress were 6.87±3.8% and 96±1.0%. Proposed attenuation correction and computed tomography based attenuation correction average myocardial perfusion count was significantly higher than that in non-AC in the mid-inferior, mid-lateral, basal-inferior, and lateral regions (p<0.001).

Conclusion

The proposed attenuation maps showed good agreement with the CT-based attenuation map. Therefore, it is feasible to enable AC for a dedicated cardiac SPECT or SPECT standalone scanners.

Multicenter quantitative 18F-fluorodeoxyglucose positron emission tomography performance harmonization: use of hottest voxels towards more robust quantification

Background

Harmonization methods reduce variability between different make and models of positron emission tomography (PET) scanners. The study aims to explore harmonization strategies that lead to comparable and robust quantitative metrics in a multicenter setting.

Methods

NEMA IEC Phantom data acquisition was performed for low and high spheres-to-background ratios (SBR4:1 and 10:1) on six PET/CT (computed tomography) scanners. Different reconstruction sets, including the number of sub-iterations, number of subsets, and full width at half maximum (FWHM) for each scanner, were evaluated towards optimized and harmonized reconstruction settings. Recovery coefficients (RCs) of four quantitative metrics, including standardized uptake value (SUV)_max, SUV_ISO-50 (SUV_mean in 50% isocontour), SUV_peak, and mean uptake of 10 highest concentration voxels were evaluated as RC_max, RC_ISO-50, RC_peak, and RC_10V, representing percent difference relative to the static ground truth case as functions of sphere sizes. A set of image reconstruction parameters was proposed for harmonized reconstruction to minimize variability between scanners. The root mean square error (RMSE), curvature, and reproducibility were examined. The proposed reconstruction protocols for harmonization and standard clinical reconstruction settings were compared to each other across all scanners.

Results

A significant difference (P value <0.0001) was observed in the aforementioned quantitative metrics between SBR10 and SBR4. Reconstruction parameter sets with the smallest RMSE and RC values within 10% bias were identified as the best candidate for harmonization. The coefficient of variation of the mean value of RCs (CV_MRC) shows a remarkable reduction of about 28%, 26%, 32%, and 19% in harmonized reconstruction settings for MRC_max, MRC_ISO-50, MRC_peak, and MRC_10V, respectively. CV_MRC for MRC_10V in the harmonized reconstruction setting was 5.9% in SBR4, while the smallest value in SBR10 belongs to MRC_peak, with a value of 5.8%. The reproducibility of RC is improved by deriving the value from ten hottest voxels and is equally reproducible with RC_peak. Compared to RC_max and RC_ISO-50, the variability is reduced by 18% and 22% if ten voxels are pooled.

Conclusions

Harmonizing PET/CT systems with and without point spread function/time of flight (PSF/TOF) using various vendor-developed image reconstruction algorithms improves the quantification reproducibility. RC_10V, likewise RC_peak, is superior to the rest of the quantitative indices in terms of accuracy and reproducibility and helpful in quantifying lesion volume below 1 mL.

Breathing-induced Errors in Quantification and Description of Dominant Intra-Prostatic Lesions (Dils) in PET Images: A Simulation Study by Means of The 4D NCAT Phantom

BACKGROUND

Respiratory movement and the motion range of the diaphragm can affect the quality and quantity of prostate images.

OBJECTIVE

This study aimed to investigate the magnitude of respiratory-induced errors to determine Dominant Intra- prostatic Lesions (DILs) in positron emission tomography (PET) images.

MATERIAL AND METHODS

In this simulation study, we employed the 4D NURBS-based cardiac-torso (4D-NCAT) phantom with a realistic breathing model to simulate the respiratory cycles of a patient to assess the displacement, volume, maximum standardized uptake value (SUV_max), mean standardized uptake value (SUV_mean), signal to noise ratio (SNR), and the contrast of DILs in frames within the respiratory cycle.

RESULTS

Respiration in a diaphragm motion resulted in the maximum superior-inferior displacement of 3.9 and 6.1 mm, and the diaphragm motion amplitudes of 20 and 35 mm. In a no-motion image, the volume measurement of DILs had the smallest percentage of errors. Compared with the no-motion method, the percentages of errors in the average method in 20 and 35 mm- diaphragm motion were 25% and 105%, respectively. The motion effect was significantly reduced in terms of the values of SUV_max and SUV_mean in comparison with the values of SUV_max and SUV_mean in no- motion images. The contrast values in respiratory cycle frames were at a range of 3.3-19.2 mm and 6.5-46 for diaphragm movements' amplitudes of 20 and 35 mm.

CONCLUSION

The respiratory movement errors in quantification and delineation of DILs were highly dependent on the range of motion, while the average method was not suitable to precisely delineate DILs in PET/CT in the dose-painting technique.

Evaluation of Dose-Painting in the Dominant Intraprostatic Lesions by Radiobiological Parameters using 68Ga- PSMA PET/CT

BACKGROUND

Patients diagnosed with dominant intraprostatic lesions (DIL) may need radiation doses over than 80 Gy. Dose-painting by contours (DPC) is a useful technique which helps the patients. Dose-painting approach need to be evaluated.

OBJECTIVE

To evaluate the DCP technique in the case of boosting the DILs by radiobiological parameters, tumor control probability (TCP), and normal tissue complication probability (NTCP) via PET/CT images traced by ⁶⁸Ga-PSMA.

MATERIAL AND METHODS

In this analytical study, ⁶⁸Ga-PSMA PET/CT images were obtained from patients with DILs that were delineated using the Fuzzy c-mean (FCM) algorithm and thresholding methods. The protocol of therapy included two phases; at the first phase (ph1), a total dose of 72 Gy in 36 fractions were delivered to the planning target volume (PTV1); the seconds phase consisted of the application of variable doses to the PTV2. Moreover, two concepts were also considered to calculate the TCP using the Zaider-Minerbo model.

RESULTS

The lowest volume in DILs belonged to the DIL1 extracted by the FCM method. According to dose-volume parameters of the rectum and bladder, by the increase in the PTV dose higher than 92 Gy, the amounts of rectum and bladder doses are increased. There was no difference between the TCPs of DILs at doses higher than 86 Gy and 100 Gy for ordinary and high clone density, respectively.

CONCLUSION

Consequently, our dose-painting approach for DILs, extracted by the FCM method via PET/CT images, can reduce the total dose for prostate radiation with 100% tumor control and less normal tissue complications.

Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy

PURPOSE

To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy.

METHODS

Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTV_CT , CTV_CT ), PET (GTV_PET40 , CTV_PET40 ), and RFMs (GTV_RFM , CTV_RFM ,). Intratumoral heterogeneity areas were segmented as GTV_PET50-Boost and radiomic boost target volume (RTV_Boost ) on PET and RFMs, respectively. GTV_CT in homogenous tumors and GTV_PET40 in heterogeneous tumors were considered as GTV_{gold standard} (GTV_GS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTV_RFM with GTV_GS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes.

RESULTS

Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTV_RFM-entropy , GTV_RFM-contrast , and GTV_{RFM-H-correlation} with GTV_GS , respectively. The linear regression results showed a positive correlation between GTV_GS and GTV_RFM-entropy (r = 0.98, p < 0.001), between GTV_GS and GTV_RFM-contrast (r = 0.93, p < 0.001), and between GTV_GS and GTV_{RFM-H-correlation} (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTV_RFM-entropy and CTV_RFM-contrast with CTV_GS , respectively. Moreover, we used RFM to determine RTV_Boost in the heterogeneous tumors. Comparison of RTV_Boost with GTV_PET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTV_{Boost-entropy} , RTV_{Boost-contrast} , and RTV_{Boost-H-correlation} , respectively.

CONCLUSIONS

FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

BACKGROUND

We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.

METHODS

Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported.

RESULTS

In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance.

CONCLUSION

Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.

Optimized cocktail of 90Y/177Lu for radionuclide therapy of neuroendocrine tumors of various sizes: a simulation study

BACKGROUND AND OBJECTIVES

There is significant interest and potential in the treatment of neuroendocrine tumors via peptide receptor radionuclide therapy (PRRT) using one or both of 90Y and 177Lu-labeled peptides. Given the presence of different tumor sizes in patients and differing radionuclide dose delivery properties, the present study aims to use Monte Carlo simulations to estimate S-values to spherical tumors of various sizes with 90Y and 177Lu separately and in combination. The goal is to determine ratios of 90Y to 177Lu that result in the largest absorbed doses per decay of the radionuclides and the most suitable dose profiles to treat tumors of specific sizes.

MATERIAL AND METHODS

Particle transfer calculations and simulations were performed using the Monte Carlo GATE simulation software. Spherical tumors of different sizes, ranging from 0.5 to 20 mm in radius, were designed. Activities of 177Lu and 90Y, individually and in combination, were homogeneously placed within the total volume of the tumors. We determined the S-values to the tumors, and to the external volume outside of the tumors (cross-dose) which was used to approximate background tissue. The dose profiles were obtained for each of the different tumor sizes, and the uniformity of dose within each tumor was calculated.

RESULTS

For all tumor sizes, the self-dose and cross-dose per decay from 90Y were higher than that from 177Lu. We observed that 177Lu had the most uniform dose distribution within tumors with radii less than 5 mm. For tumors greater than 5 mm in radius, a ratio of 25% 90Y to 75% 177Lu resulted in the most uniform doses. When the ratio of 177Lu to 90Y was smaller, the uniformity improved more with increasing tumor size. The cross-dose stayed approximately constant for tumors larger than 15 mm for all ratios of 177Lu to 90Y. Finally, as the size of the tumor increased, differences in the S-values between different ratios of 177Lu to 90Y decreased.

CONCLUSION

Our work showed that to achieve a more uniform dose distribution within the tumor, 177Lu alone is more effective for small tumors. For medium and large tumors, a ratio of 90Y to 177Lu with more or less 177Lu, respectively, is recommended.

The effect of magnetic field strength on the positron range and projected annihilation artifact in integrated PET/MR systems: A GATE Monte Carlo study

PURPOSE

With improvements in positron emission tomography/magnetic resonance imaging (PET/MRI) over the last decade, there is a need to investigate the projected annihilation (shine-through) artifact and resolution impact for different PET radiopharmaceuticals, magnetic field (MF) strengths, and tissues.

METHODS

The GATE Monte Carlo (MC) simulation was used to simulate the annihilation distribution of positrons in different tissues and MFs. The positron distribution was studied in magnetic field (MF) intensities up to 15 T for ¹¹ C, ¹³ N, ¹⁵ O, ¹⁸ F, ⁶⁸ Ga, and ⁸² Rb. Moreover, the image quality in terms of the occurrence of projected annihilation artifacts was investigated using the 4D anthropomorphic digital extended cardiac-torso (XCAT) phantom.

RESULTS

Positron ranges were restricted across the directions perpendicular to the MF, but no change along the direction of the MF was detected. The projected annihilation artifacts were observed with the presence of MF in the sagittal and coronal view of PET images prepared from the XCAT phantom. The intensity of artifact was constant in MFs higher than 3 T. The significant effect of the MF on resolution improvement was observed in soft tissue for ⁶⁸ Ga in 7 T and ⁸² Rb in 3 and 7 T, while higher MFs have no impact on resolution. The improvement of resolution in the lung tissue was observed for the medium- and high-energy radionuclides in 7 T MF.

CONCLUSION

The MF can create the projected annihilation artifact in the boundary of air cavities and other tissues for medium- and high-energy radionuclides especially for ⁶⁸ Ga in clinical studies. In addition, the strength of the MFs more than 3 T was ineffective on the intensity of the projected annihilation artifact. In a clinical PET/MR scanner, MF has remarkable spatial resolution improvement in lung tissue, especially for medium- and high-energy radionuclides, and negligible effect in bone and soft tissue for most radionuclides.

Effect of Diet on Physiologic Bowel 18F-FDG Uptake

Intestinal ¹⁸F-FDG uptake is variable in whole-body PET/CT. In cancer patients, particularly those suspected of relapse or metastasis, ¹⁸F-FDG absorption might interfere with scan interpretation. This study evaluated the effect of diet on intestinal ¹⁸F-FDG absorption. Methods: In total, 214 patients referring for oncologic ¹⁸F-FDG PET/CT participated. They were randomly divided into 2 groups and asked to follow either a routine diet (RD) or a low-carbohydrate, high-fat diet (LCHFD) for 24 h before the study. The small bowel and different parts of the colon (the cecum; the ascending, transverse, and descending segments; and the hepatic and splenic flexures) were evaluated and visually interpreted by nuclear medicine experts. Bowel uptake was graded through comparison with that in the liver as absent, mild, moderate, or severe. Results: Significantly higher ¹⁸F-FDG uptake in the descending colon (P = 0.001) and small intestine (P = 0.01) was observed in the RD group than in the LCHFD group. After patients with bowel cancer were omitted from the statistical analysis, no significant differences in the final results were seen. Conclusion: An LCHFD for 24 h before ¹⁸F-FDG PET imaging resulted in lower ¹⁸F-FDG uptake in the descending colon and small bowel than did an RD, assisting the interpreting physician by reducing the intestinal activity interference for more accurate diagnostic interpretation.

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

OBJECTIVE

To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images.

METHODS

Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets.

RESULTS

For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)).

CONCLUSION

Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.

Implementation of dose point kernel (DPK) for dose optimization of 177Lu/90Y cocktail radionuclides in internal dosimetry

BACKGROUND

Due to the importance of choosing the applicable dosimetry method in radionuclide therapy, the present study was conducted to investigate the efficiency of the implementation of Dose Point Kernel (DPK) for dose optimization of ¹⁷⁷Lu/⁹⁰Y Cocktail Radionuclides in internal Dosimetry.

METHODS

In this study, simulations and calculations of DPK were performed using the GATE/GEANT4 Monte Carlo code. For specific liver dosimetry, the NCAT phantom and convolution algorithm-based Fast Fourier Transform method was used by MATLAB software.

RESULTS

The self-dose of ¹⁷⁷Lu and ⁹⁰Y radionuclides in the liver of NCAT phantom were 1.1708E-13, and 4.8420E-11 (Gy/Bq), respectively, and the cross-dose of ¹⁷⁷Lu and ⁹⁰Y radionuclides out of the liver of NCAT phantom were 2.03615E-16, and 0.8422E-13 (Gy/Bq) respectively. Overall results showed that with an increase the value of ⁹⁰Y with quarter steps in a cocktail, the amount of the self-dose increase 1.5, 6, and 29 times respectively, and with an increase the value of ¹⁷⁷Lu in quarter step in a cocktail, the amount of the cross dose decrease 3, 15 and 68 percent respectively.

CONCLUSION

Generally, the present results indicate that the calculated DPK functions of ¹⁷⁷Lu and ⁹⁰Y cocktails can play an important role in choosing the best combination of radionuclide to optimize treatment planning in cocktail radionuclide therapy.

Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test-retest and image registration analyses

PURPOSE

To assess the repeatability of radiomic features in magnetic resonance (MR) imaging of glioblastoma (GBM) tumors with respect to test-retest, different image registration approaches and inhomogeneity bias field correction.

METHODS

We analyzed MR images of 17 GBM patients including T1- and T2-weighted images (performed within the same imaging unit on two consecutive days). For image segmentation, we used a comprehensive segmentation approach including entire tumor, active area of tumor, necrotic regions in T1-weighted images, and edema regions in T2-weighted images (test studies only; registration to retest studies is discussed next). Analysis included N3, N4 as well as no bias correction performed on raw MR images. We evaluated 20 image registration approaches, generated by cross-combination of four transformation and five cost function methods. In total, 714 images (17 patients × 2 images × ((4 transformations × 5 cost functions) + 1 test image) and 2856 segmentations (714 images × 4 segmentations) were prepared for feature extraction. Various radiomic features were extracted, including the use of preprocessing filters, specifically wavelet (WAV) and Laplacian of Gaussian (LOG), as well as discretization into fixed bin width and fixed bin count (16, 32, 64, 128, and 256), Exponential, Gradient, Logarithm, Square and Square Root scales. Intraclass correlation coefficients (ICC) were calculated to assess the repeatability of MRI radiomic features (high repeatability defined as ICC ≥ 95%).

RESULTS

In our ICC results, we observed high repeatability (ICC ≥ 95%) with respect to image preprocessing, different image registration algorithms, and test-retest analysis, for example: RLNU and GLNU from GLRLM, GLNU and DNU from GLDM, Coarseness and Busyness from NGTDM, GLNU and ZP from GLSZM, and Energy and RMS from first order. Highest fraction (percent) of repeatable features was observed, among registration techniques, for the method Full Affine transformation with 12 degrees of freedom using Mutual Information cost function (mean 32.4%), and among image processing methods, for the method Laplacian of Gaussian (LOG) with Sigma (2.5-4.5 mm) (mean 78.9%). The trends were relatively consistent for N4, N3, or no bias correction.

CONCLUSION

Our results showed varying performances in repeatability of MR radiomic features for GBM tumors due to test-retest and image registration. The findings have implications for appropriate usage in diagnostic and predictive models.

Synthesis, biological evaluation and preclinical study of a novel 99mTc-peptide: A targeting probe of amyloid-β plaques as a possible diagnostic agent for Alzheimer's disease

With respect to the main role of amyloid-β (Aβ) plaques as one of the pathological hallmarks in the brain of Alzheimer's patients, the development of new imaging probes for targeted detection of Aβ plaques has attracted considerable interests. In this study, a novel cyclopentadienyl tricarbonyl Technetium-99 m (^99mTc) agent with peptide scaffold, ^99mTc-Cp-GABA-D-(FPLIAIMA)-NH₂, for binding to the Aβ plaques was designed and successfully synthesized using the Fmoc solid-phase peptide synthesis method. This radiopeptide revealed a good affinity for Aβ42 aggregations (K_d = 20 µM) in binding affinity study and this result was confirmed by binding to Aβ plaques in brain sections of human Alzheimer's disease (AD) and rat models using in vitro autoradiography, fluorescent staining, and planar scintigraphy. Biodistribution studies of radiopeptide in AD and normal rats demonstrated a moderate initial brain uptake about 0.38 and 0.35% (ID/g) 2 min post-injection, respectively. Whereas, AD rats showed a notable retention time in the brain (0.23% ID/g at 30 min) in comparison with fast clearance in normal rat brains. Normal rats following treatment with cyclosporine A as a p-glycoprotein inhibitor showed a significant increase in the radiopeptide brain accumulation compared to non-treated ones. There was a good correlation between data gathered from single-photon emission computed tomography/computed tomography (SPECT/CT) imaging and biodistribution studies. Therefore, these findings showed that this novel radiopeptide could be a potential SPECT imaging agent for early detection of Aβ plaques in the brain of patients with AD.

Performance evaluation of developed dedicated breast PET scanner and improvement of the spatial resolution by wobbling: a Monte Carlo study

PURPOSE

Molecular imaging, particularly PET scanning, has become an important cancer diagnostic tool. Whole-body PET is not effective for local staging of cancer because of their declining efficiency in detecting small lesions. The preliminary results of the performance evaluation of designed dedicated breast PET scanner presented.

METHODS AND MATERIALS

A new scanner is based on LYSO crystals coupled with SiPM, and it consists of 14 compact modules with a transaxial FOV of 180 mm in diameter. In this study, initial GATE simulation studies were performed to predict the spatial resolution, absolute sensitivity, noise equivalent count rate (NECR) and scatter fraction (SF) of the new design. Spatial wobbling acquisitions were also implemented. Finally, the obtained projections were reconstructed using analytical and iterative algorithms.

RESULTS

The simulation results indicate that absolute sensitivity is 1.42% which is appropriate than other commercial breast PET systems. The calculated SF and NECR in our design are 20.6% and 21.8 kcps. The initial simulation results demonstrate the potential of this design for breast cancer detection. A small wobble motion to improve spatial resolution and contrast.

CONCLUSION

The performance of the dedicated breast PET scanner is considered to be reasonable enough to support its use in breast cancer imaging.

Design, synthesis, radiolabeling and biological evaluation of new urea-based peptides targeting prostate specific membrane antigen

Early diagnosis of Prostate cancer (PCa) plays a vital role in successful treatment increasing the survival rate of patients. Prostate Specific Membrane Antigen (PSMA) is over-expressed in almost all types of PCa. The goal of present study is to introduce new ^99mTc-labeled peptides as a PSMA inhibitor for specific detection of PCa at early stages. Based on published PSMA-targeting compounds, a set of peptides bearing the well-known Glu-Urea-Lys pharmacophore and new non-urea containing pharmacophore were designed and assessed by in silico docking studies. The selected peptides were synthesized and radiolabeled with ^99mTc. The in-vitro tests (log P, stability in normal saline and fresh human plasma, and affinity toward PSMA-positive LNCaP cell line) and in-vivo characterizations of radiopeptides (biodistribution and Single Photon Emission Computed Tomography-Computed Tomography (SPECT-CT) imaging in normal and tumour-bearing mice) were performed. The peptides 1-3 containing Glu-Urea-Lys and Glu-GABA-Asp as pharmacophores were efficiently interacted with crystal structure of PSMA and showed the highest binding energies range from -8 to -11.2 kcal/mol. Regarding the saturation binding test, ^99mTc-labeled peptide 1 had the highest binding affinity (K_d = 13.58 nM) to PSMA-positive cells. SPECT-CT imaging and biodistribution studies showed high kidneys and tumour uptake 1 h post-injection of radiopeptide 1 and 2 (%ID/g tumour = 3.62 ± 0.78 and 1.8 ± 0.32, respectively). ^99mTc-peptide 1 (Glu-urea-Lys-Gly-Ala-Asp-Naphthylalanine-HYNIC-^99mTc) exhibited the highest binding affinity, high radiochemical purity, the most stability and high specific accumulation in prostate tumour lesions. ^99mTc-peptide 1 being of comparable efficacy and pharmacokinetic properties with the well-known PET tracer (⁶⁸Ga-PSMA-11) seems to be applied as a promising SPECT imaging agent to early diagnose of PCa and consequently increase survival rate of patients.

In vitro evaluation of an iodine radionuclide dosimeter (IRD) for continuous patient monitoring

In vivo dosimetry of the patients treated by I-131 is important from patient dosimetry and radiation protection points of view. Knowledge of delivered dose to the target volume and adjacent normal tissues can improve the effectiveness of radioiodine treatment. Herein, design, fabrication, and assessment processes of an iodine radionuclide dosimeter (IRD) are explained. Two CsI(Tl) scintillator crystals coupled to photodiodes were used in IRD fabrication with specifications derived from Monte Carlo (MC) simulation. Linearity, sensitivity, and long-term performance of the system were tested. Delivered dose due to a known administered activity of I-131 was calculated by MC simulation which was validated based on the Medical Internal Radiation Dose (MIRD) Committee formalism, and the calibration factor was provided. Using the current mode signal acquisition method, the system showed a linear response up to 8.2 GBq radioiodine activity to prohibit the pile-up error without a need for correction factor. On the other hand, IRD was sensitive down to the rarely detectable activity of 7.4 MBq. A prototype version of the IRD system has been developed to guide the hospital staff for the safe release of iodine - administered patients and to provide an insight for physicians about the delivered dose to the thyroid and nearby organs. Graphical abstract IRD attached to MIHAN.

Evaluation of the Impact of Out-of-Axial FOV Scattering Medium on Random Coincidence Rates on Discovery 690 PET/CT Scanner: A Simulation Study

Purpose: Positron Emission Tomography (PET) imaging is a nuclear medicine imaging technique based on the recording of two photons as coincidence created by positron annihilation. Materials and Methods: PET coincidence events include true and unwanted coincidences (random, scattered, multiple coincidences). We modeled the Discovery 690 (D-690) PET scanner using the GATE simulation tool and estimated the effect of the diameter of the scattering medium out of the Axial Field Of View (AFOV) on the random coincidence rates. Results: The validation results indicated that the average difference between simulated and measured data for sensitivity and scatter fraction tests are 5% and 3%, respectively. Moreover, the results revealed that the increasing diameter of the scattering medium out of the AFOV has a direct effect on the random coincidence rates within the Field Of View (FOV). Conclusion: The study concluded that the presence of a scattering medium near the FOV increases the rate of random coincidences.  

Preclinical study of a new 177Lu-labeled somatostatin receptor antagonist in HT-29 human colorectal cancer cells

OBJECTIVES

Somatostatin receptor-positive neuroendocrine tumors have been targeted using various peptide analogs radiolabeled with therapeutic radionuclides for years. The better biomedical properties of radioantagonists as higher tumor uptake make these radioligands more attractive than agonists for somatostatin receptor-targeted radionuclide therapy. In this study, we tried to evaluate the efficiency of Luthetium-177 (177Lu) radiolabeled DOTA-Peptide 2 (177Lu-DOTA-Peptide 2) as a new radioantagonist in HT-29 human colorectal cancer in vitro and in vivo.

METHODS

DOTA conjugated antagonistic peptide with the sequence of p-Cl-Phe-Cyclo(D-Cys-L-BzThi-D-Aph-Lys-Thr-Cys)-D-Tyr-NH2 (DOTA-Peptide 2) was labeled with 177Lu. In vitro assays (saturation binding assay and internalization test) and animal biodistribution were performed in human colon adenocarcinoma cells (HT-29) and HT-29 tumor-bearing nude mice.

RESULTS

177Lu-DOTA-Peptide 2 showed high stability in acetate buffer and human plasma (>97%). Antagonistic property of 177Lu-DOTA-Peptide 2 was confirmed by low internalization in HT-29 cells (<5%). The desired dissociation constant (Kd =11.14 nM) and effective tumor uptake (10.89 percentage of injected dose per gram of tumor) showed high binding affinity of 177Lu-DOTA-Peptide 2 to somatostatin receptors.

CONCLUSION

177Lu-DOTA-Peptide 2 demonstrated selective and high binding affinity to somatostatin receptors overexpressed on the surface of HT-29 cancer cells, which could make this radiopeptide suitable for somatostatin receptor-targeted radionuclide therapy.

NEMA NU-4 2008 performance evaluation of Xtrim-PET: A prototype SiPM-based preclinical scanner

PURPOSE

Xtrim-PET is a newly designed Silicon Photomultipliers (SiPMs)-based prototype PET scanner dedicated for small laboratory animal imaging. We present the performance evaluation of the Xtrim-PET scanner following NEMA NU-4 2008 standards to help optimizing scanning protocols which can be achieved through standard and reliable system performance characterization.

METHODS

The performance assessment was conducted according to the National Electrical Manufacturers Association (NEMA) NU-4 2008 standards in terms of spatial resolution, sensitivity, counting rate performance, scatter fraction and image quality. The in vivo imaging capability of the scanner is also showcased through scanning a normal mouse injected with ¹⁸ F-FDG. Furthermore, the performance characteristics of the developed scanner are compared with commercially available systems and current prototypes.

RESULTS

The volumetric spatial resolution at 5 mm radial offset from the central axis of the scanner is 6.81 µl, whereas a peak absolute sensitivity of 2.99% was achieved using a 250-650 keV energy window and a 10 ns timing window. The peak noise-equivalent count rate (NECR) using a mouse-like phantom is 113.18 kcps at 0.34 KBq/cc with 12.5% scatter fraction, whereas the NECR peaked at 82.76 kcps for an activity concentration level of 0.048 KBq/cc with a scatter fraction of 25.8% for rat-like phantom. An excellent uniformity (3.8%) was obtained using NEMA image quality phantom. Recovery coefficients of 90%, 86%, 68%, 40% and 12% were calculated for rod diameters of 5, 4, 3, 2 and 1 mm, respectively. Spill-over ratios for air-filled and water-filled chambers were 35% and 25% without applying any correction for attenuation and Compton scattering effects.

CONCLUSION

Our findings revealed that beyond compactness, lightweight, easy installation and good energy resolution, the Xtrim-PET prototype presents a reasonable performance making it suitable for preclinical molecular imaging-based research.

Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)

OBJECTIVE

To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network.

METHODS

Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images.

RESULTS

Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUV_mean was - 0.10 ± 2.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, - 0.83 to 1.18). SUV_max had mean RE (%) of - 3.87 ± 2.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of - 3.99 ± 2.11 (range, - 8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 ± 2.99.

CONCLUSIONS

Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners.

KEY POINTS

• We demonstrate direct emission-based attenuation correction of PET images without using anatomical information. • We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images. • Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.

Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients

OBJECTIVES

The aim of this study was to investigate and validate the performance of individual and ensemble machine learning models (EMLMs) based on magnetic resonance imaging (MRI) to predict neo-adjuvant chemoradiation therapy (nCRT) response in rectal cancer patients. We also aimed to study the effect of Laplacian of Gaussian (LOG) filter on EMLMs predictive performance.

METHODS

98 rectal cancer patients were divided into a training (n = 53) and a validation set (n = 45). All patients underwent MRI a week before nCRT. Several features from intensity, shape and texture feature sets were extracted from MR images. SVM, Bayesian network, neural network and KNN classifiers were used individually and together for response prediction. Predictive performance was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC).

RESULTS

Patients' nCRT responses included 17 patients with Grade 0, 28 with Grade 1, 34 with Grade 2, and 19 with Grade 3 according to AJCC/CAP pathologic grading. In without preprocessing MR Image the best result was for Bayesian network classifier with AUC and accuracy of 75.2% and 80.9% respectively, which was confirmed in the validation set with an AUC and accuracy of 74% and 79% respectively. In EMLMs the best result was for 4 (SVM.NN.BN.KNN) classifier EMLM with AUC and accuracy of 97.8% and 92.8% in testing and 95% and 90% in validation set respectively.

CONCLUSIONS

In conclusion, we observed that machine learning methods can used to predict nCRT response in patients with rectal cancer. Preprocessing LOG filters and EL models can improve the prediction process.

68Ga-radiolabeled bombesin-conjugated to trimethyl chitosan-coated superparamagnetic nanoparticles for molecular imaging: preparation, characterization and biological evaluation

INTRODUCTION

Nowadays, nanoparticles (NPs) have attracted much attention in biomedical imaging due to their unique magnetic and optical characteristics. Superparamagnetic iron oxide nanoparticles (SPIONs) are the prosperous group of NPs with the capability to apply as magnetic resonance imaging (MRI) contrast agents. Radiolabeling of targeted SPIONs with positron emitters can develop dual positron emission tomography (PET)/MRI agents to achieve better diagnosis of clinical conditions.

METHODS

In this work, N,N,N-trimethyl chitosan (TMC)-coated magnetic nanoparticles (MNPs) conjugated to S-2-(4-isothiocyanatobenzyl)-1,4,7,10-tetraazacyclododecane tetraacetic acid (DOTA) as a radioisotope chelator and bombesin (BN) as a targeting peptide (DOTA-BN-TMC-MNPs) were prepared and validated using fourier transform infrared (FTIR) spectroscopy, transmission electron microscopy (TEM), thermogravimetric analysis (TGA), vibrating sample magnetometer (VSM), and powder X-ray diffraction (PXRD) tests. Final NPs were radiolabeled with gallium-68 (68Ga) and evaluated in vitro and in vivo as a potential PET/MRI probe for breast cancer (BC) detection.

RESULTS

The DOTA-BN-TMC-MNPs with a particle size between 20 and 30 nm were efficiently labeled with 68Ga (radiochemical purity higher than 98% using thin layer chromatography (TLC)). The radiolabeled NPs showed insignificant toxicity (>74% cell viability) and high affinity (IC50=8.79 µg/mL) for the gastrin-releasing peptide (GRP)-avid BC T-47D cells using competitive binding assay against 99mTc-hydrazinonicotinamide (HYNIC)-gamma-aminobutyric acid (GABA)-BN (7-14). PET and MRI showed visible uptake of NPs by T-47D tumors in xenograft mouse models.

CONCLUSION

68Ga-DOTA-BN-TMC-MNPs could be a potential diagnostic probe to detect BC using PET/MRI technique.

Design, Synthesis, Radiolabeling, and Biologic Evaluation of Three 18F-FDG-Radiolabeled Targeting Peptides for the Imaging of Apoptosis

Background: Early detection of apoptosis is very important for therapy and follow-up treatment in various pathologic conditions. Annexin V interacts strongly and specifically with phosphatidylserine, specific biomarkers of apoptosis with some limitations. Small peptides are suitable alternatives to annexin V. A reliable and noninvasive in vivo technique for the detection of apoptosis is in great demand. Based on our previous studies, three new peptide analogs of LIKKPF (Leu-Ile-Lys-Lys-Pro-Phe) as apoptosis imaging agents were developed. Materials and Methods: Aoa-LIKKP-Cl-F, Aoe-LIKKP-Pyr-F, and Aoe-LIKKP-Nap-F were synthesized, functionalized with aminooxy, and radiolabeled with ¹⁸F-FDG. Their biologic properties were evaluated in vitro using apoptotic Jurkat cells. ¹⁸F-FDG-Aoe-LIKKP-Pyr-F peptide was injected into normal and apoptotic mice models for biodistribution and in vivo positron emission tomography/computed tomography imaging studies. Results: ¹⁸F-FDG-Aoe-LIKKP-Pyr-F peptide showed higher affinity for apoptotic cells. The localization of peptide in apoptotic liver mice was confirmed in biodistribution and imaging studies. Conclusion: The results showed that Aoe-LIKKP-Pyr-F peptide is an auspicious agent for molecular imaging of apoptosis.

Radiolabeling of Preformed Niosomes with [99mTc]: In Vitro Stability, Biodistribution, and In Vivo Performance

Nanocarriers radiolabeled with [^99mTc] can be used for diagnostic imaging and radionuclide therapy, as well as tracking their pharmacokinetic and biodistribution characteristics. Due to the advantages of niosomes as an ideal drug delivery system, in this study, the radiolabeling procedure of niosomes by [^99mTc]-HMPAO complexes was investigated and optimized. Glutathione (GSH)-loaded niosomes were prepared using a thin-film hydration method. To label the niosomes with [^99mTc], the preformed GSH-loaded niosomes were incubated with the [^99mTc]-HMPAO complex and were characterized for particle size, size distribution, zeta potential, morphology, and radiolabeling efficiency (RE). The effects of GSH concentration, incubation time, incubation temperature, and niosomal composition on RE were investigated. The biodistribution profile and in vivo SPECT/CT imaging of the niosomes and free [^99mTc]-HMPAO were also studied. Based on the results, all vesicles had nano-sized structure (160-235 nm) and negative surface charge. Among the different experimental conditions that were tested, including various incubation times, incubation temperatures, and GSH concentrations, the optimum condition that resulted in a RE of 92% was 200-mM GSH and 15-min incubation at 40°C. The in vitro release study in plasma showed that about 20% of radioactivity was released after 24 h, indicating an acceptable radiolabeling stability in plasma. The biodistribution of niosomes was clearly different from the free radiolabel. Niosomes carrying radionuclide were successfully used for tracking the in vivo disposition of these carriers and SPECT/CT imaging in rats. Furthermore, biodistribution studies in tumor-bearing mice revealed higher tumor accumulation of the niosomal formulation as compared with [^99mTc]-HMPAO.

[18F]FDG-Labeled CGPRPPC Peptide Serving as a Small Thrombotic Lesions Probe, Including a Comparison with [99mTc]-Labeled Form

Purpose: Fibrin is a perfect target for specific imaging of all types of thrombotic lesions. Cyclic peptides were introduced as the best scaffolds out of the different types of probes for thrombi detection. This study was conducted to label previously synthesized peptide-targeting fibrin with [¹⁸F]FDG and its in vitro and in vivo assessments. Materials and Methods: CGPRPPC peptide functionalized with 6-hydrazinonicotinamide and Eei-NHS was synthesized and cyclized using air oxidation method. The cyclic sequences were labeled with [¹⁸F]FDG at 85°C within 30 min. The stability studies were performed in human plasma. Fibrin-binding and platelet aggregation tests were performed in vitro. Biodistribution and scintigraphy imaging in normal mice and carotid thrombotic rat model were considered as in vivo studies. Results: Radiolabeled peptides show a good stability in human plasma and also high-affinity binding for human fibrin. Platelet aggregation test confirmed specific binding of radiopeptides to fibrin. A key problem with the authors' previous research was inability to detect small-vessel thrombi. The results of positron emission tomography/computed tomography scanning show high specific uptake of [¹⁸F]FDG-labeled CGPRPPC in small-sized thrombosis. Conclusion: The experiment revealed that radiolabeling of cyclic peptide (CGPRPPC) with [¹⁸F]FDG enables us to detect small thrombotic lesions in small animal models with high resolution.

Prediction of dose to the relatives of patients treated with radioiodine-131 using neural networks

In this study, the effective dose received by the family members and caregivers of 52 thyroid cancer patients, who had been treated with radioiodine I-131, was measured to investigate the ability of the neural network to predict the doses to the relatives. The effectiveness of this method to predict the relatives who will receive doses of more than 1 mSv was evaluated. The effective doses were measured by TLD. The inputs of the neural network include 13 different parameters that can potentially affect the dose, and the output was the dose to the family members. The neural networks in this study were feed-forward with a sigmoid activation function and one hidden layer. The mean and median of the measured doses were 0.45 and 0.28 mSv and its range was 0.1-3.64 mSv. The mean square error of the predicted doses by the neural network and the measured doses by TLD (mean squared error) for 99 individuals was 0.142. The optimum neural network was able to predict all the relatives who received doses of more than 1 mSv. The area under the receiver operating characteristic curve for the trained neural network was 0.957, showing its ability to distinguish these groups. Predicting the dose to a patient's relatives before release is a helpful strategy for future optimisation. Using neural networks is a promising method for predicting the dose to the family members and defining high-risk patients and relatives. Patient-specific criteria for release and patient-specific advice and consultation can be used to reduce the dose to each family member.

A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [18F]FDG-PET Imaging

PURPOSE

Determination of intra-tumor high-uptake area using 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography (PET) imaging is an important consideration for dose painting in radiation treatment applications. The aim of our study was to develop a framework towards automated segmentation and labeling of homogeneous vs. heterogeneous tumors in clinical lung [¹⁸F]FDG-PET with the capability of intra-tumor high-uptake region delineation.

PROCEDURES

We utilized and extended a fuzzy random walk PET tumor segmentation algorithm to delineate intra-tumor high-uptake areas. Tumor textural feature (TF) analysis was used to find a relationship between tumor type and TF values. Segmentation accuracy was evaluated quantitatively utilizing 70 clinical [¹⁸F]FDG-PET lung images of patients with a total of 150 solid tumors. For volumetric analysis, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) measures were extracted with respect to gold-standard manual segmentation. A multi-linear regression model was also proposed for automated tumor labeling based on TFs, including cross-validation analysis.

RESULTS

Two-tailed t test analysis of TFs between homogeneous and heterogeneous tumors revealed significant statistical difference for size-zone variability (SZV), intensity variability (IV), zone percentage (ZP), proposed parameters II and III, entropy and tumor volume (p < 0.001), dissimilarity, high intensity emphasis (HIE), and SUV_min (p < 0.01). Lower statistical differences were observed for proposed parameter I (p = 0.02), and no significant differences were observed for SUV_max and SUV_mean. Furthermore, the Spearman rank analysis between visual tumor labeling and TF analysis depicted a significant correlation for SZV, IV, entropy, parameters II and III, and tumor volume (0.68 ≤ ρ ≤ 0.84) and moderate correlation for ZP, HIE, homogeneity, dissimilarity, parameter I, and SUV_min (0.22 ≤ ρ ≤ 0.52), while no correlations were observed for SUV_max and SUV_mean (ρ < 0.08). The multi-linear regression model for automated tumor labeling process resulted in R ² and RMSE values of 0.93 and 0.14, respectively (p < 0.001), and generated tumor labeling sensitivity and specificity of 0.93 and 0.89. With respect to baseline random walk segmentation, the results showed significant (p < 0.001) mean DSC, HD, and SUV_mean error improvements of 21.4 ± 11.5 %, 1.4 ± 0.8 mm, and 16.8 ± 8.1 % in homogeneous tumors and 7.4 ± 4.4 %, 1.5 ± 0.6 mm, and 7.9 ± 2.7 % in heterogeneous lesions. In addition, significant (p < 0.001) mean DSC, HD, and SUV_mean error improvements were observed for tumor sub-volume delineations, namely 5 ± 2 %, 1.5 ± 0.6 mm, and 7 ± 3 % for the proposed Fuzzy RW method compared to RW segmentation.

CONCLUSION

We proposed and demonstrated an automatic framework for significantly improved segmentation and labeling of homogeneous vs. heterogeneous tumors in lung [¹⁸F]FDG-PET images.

Targeted Delivery System Based on Gemcitabine-Loaded Silk Fibroin Nanoparticles for Lung Cancer Therapy

Here, a targeted delivery system was developed based on silk fibroin nanoparticles (SFNPs) for the systemic delivery of gemcitabine (Gem) to treat induced lung tumor in a mice model. For targeting the tumorigenic lung tissue, SP5-52 peptide was conjugated to Gem-loaded SFNPs. Different methods were used to characterize the structural and physicochemical properties of the SFNPs. The prepared nanoparticles (NPs) showed suitable characteristics in terms of size, zeta potential, morphology, and structural properties. Moreover, the targeted Gem-loaded SFNPs showed higher cytotoxicity, cellular uptake, and accumulation in the lung tissue in comparison to the nontargeted SFNPs and control groups. Afterward, a mice model with induced lung tumor was developed by intratracheal injection of Lewis lung carcinoma (LL/2) cells into the lungs for assessing the therapeutic efficacy of the prepared drug delivery system. The histopathological assessments and single-photon-emission computed tomography-CT radiographs showed successful lung tumor induction. Moreover, the obtained results showed higher potential of targeted Gem-loaded SFNPs in treating induced lung tumor compared with that of the control groups. Higher survival rate, less mortality, and no sign of metastasis were also observed in those animals treated with targeted NPs based on the histological and radiological analyses. This study presented an effective anticancer drug delivery system for specific targeting of induced lung tumor that could be useful in treating malignant lung cancers in future.

The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies

OBJECTIVES

The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings.

METHODS

Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV).

RESULTS

Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively.

CONCLUSIONS

Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies.

KEY POINTS

• PET/CT image radiomics is a quantitative approach assessing different aspects of tumour uptake. • Radiomic features robustness is an important issue over different image reconstruction settings. • Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent. • Robust radiomic features can be considered as good candidates for tumour quantification.