Feasibility Study of Single-Photon Emission Computed Tomography with Iodine-123 Labeled Metaiodobenzylguanidine for Preclinical Evaluation of Labetalol as a ß-Adrenergic Receptor Blocker.

Among clinically used radiopharmaceuticals, iodine-123 labeled metaiodobenzylguanidine ([123I]mIBG) serves for diagnosing neuroendocrine tumors and obtaining images of myocardial sympathetic innervation. mIBG, a structural analogue of norepinephrine (NE), a neurotransmitter acting in peripheral and central nerves, follows a pathway similar to NE, transmitting signals through the NE transporter (NET) located at synaptic terminals. It moves through the body without decomposing, enabling noninvasive image evaluation. In this study, we aimed to quantify [123I]mIBG uptake in the adrenal glands using small animal single-photon emission computed tomography/computed tomography (SPECT/CT) images post [123I]mIBG administration. We investigated the possibility of assessing the effectiveness of ß-adrenergic receptor blockers by quantifying SPECT/CT images and biodistribution results to determine the degree of [123I]mIBG uptake in the adrenal glands treated with labetalol, a known ß-adrenergic receptor blocker. Upon intravenous administration of [123I]mIBG to mice, SPECT/CT images were acquired over time to confirm the in vivo distribution pattern, revealing a clear uptake in the adrenal glands. Labetalol inhibited the uptake of [123I]mIBG in cell lines expressing NET. A decrease in [123I]mIBG uptake in the adrenal glands was observed in the labetalol-treated group compared with the normal group through SPECT/CT imaging and biodistribution studies. These results demonstrate that SPECT/CT imaging with [123I]mIBG could be applicable for evaluating the preclinical efficacy of new antihypertensive drug candidates such as labetalol, a ß-adrenergic receptor blocker.

Prognostic Value of Radiomic Analysis Using Pre- and Post-Treatment 18F-FDG-PET/CT in Patients with Laryngeal Cancer and Hypopharyngeal Cancer.

BACKGROUND: The prognostic value of conducting 18F-FDG PET/CT imaging has yielded different results in patients with laryngeal cancer and hypopharyngeal cancer, but these results are controversial, and there is a lack of dedicated studies on each type of cancer. This study aimed to evaluate whether combining radiomic analysis of pre- and post-treatment 18F-FDG PET/CT imaging features and clinical parameters has additional prognostic value in patients with laryngeal cancer and hypopharyngeal cancer. METHODS: From 2008 to 2016, data on patients diagnosed with cancer of the larynx and hypopharynx were retrospectively collected. The patients underwent pre- and post-treatment 18F-FDG PET/CT imaging. The values of ΔPre-Post PET were measured from the texture features. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the most predictive features to formulate a Rad-score for both progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curve analysis and Cox regression were employed to assess PFS and OS. Then, the concordance index (C-index) and calibration plot were used to evaluate the performance of the radiomics nomogram. RESULTS: Study data were collected for a total of 91 patients. The mean follow-up period was 71.5 mo. (8.4-147.3). The Rad-score was formulated based on the texture parameters and was significantly associated with both PFS (p = 0.024) and OS (p = 0.009). When predicting PFS, only the Rad-score demonstrated a significant association (HR 2.1509, 95% CI [1.100-4.207], p = 0.025). On the other hand, age (HR 1.116, 95% CI [1.041-1.197], p = 0.002) and Rad-score (HR 33.885, 95% CI [2.891-397.175], p = 0.005) exhibited associations with OS. The Rad-score value showed good discrimination when it was combined with clinical parameters in both PFS (C-index 0.802-0.889) and OS (C-index 0.860-0.958). The calibration plots also showed a good agreement between the observed and predicted survival probabilities. CONCLUSIONS: Combining clinical parameters with radiomics analysis of pre- and post-treatment 18F-FDG PET/CT parameters in patients with laryngeal cancer and hypopharyngeal cancer might have additional prognostic value.

Automatic Quantitative Assessment for Diagnostic and Therapeutic Response in Rodent Myocardial Infarct Model.

The purpose of this study was to investigate the most appropriate methodological approach for the automatic measurement of rodent myocardial infarct polar map using histogram-based thresholding and unsupervised deep learning (DL)-based segmentation. A rat myocardial infarction model was induced by ligation of the left coronary artery. Positron emission tomography (PET) was performed 60 min after the administration of 18F-fluoro-deoxy-glucose (18F-FDG), and PET was performed after injecting 64Cu-pyruvaldehyde-bis(N4-methylthiosemicarbazone). Single photon emission computed tomography was performed 60 min after injection of 99mTc-hexakis-2-methoxyisobutylisonitrile and 201Tl. Delayed contrast-enhanced magnetic resonance imaging was performed after injecting Gd-DTPA-BMA. Three types of thresholding methods (naive thresholding, Otsu's algorithm, and multi-Gaussian mixture model (MGMM)) were used. DL segmentation methods were based on a convolution neural network and trained with constraints on feature similarity and spatial continuity of the response map extracted from images by the network. The relative infarct sizes measured by histology and estimated R2 for 18F-FDG were 0.8477, 0.7084, 0.8353, and 0.9024 for naïve thresholding, Otsu's algorithm, MGMM, and DL segmentation, respectively. DL-based method improved the accuracy of MI size assessment.

Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics.

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.

Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Patients Using Harmonized Radiomics of Multcenter 18F-FDG-PET Image.

We developed machine and deep learning models to predict chemoradiotherapy in rectal cancer using 18F-FDG PET images and harmonized image features extracted from 18F-FDG PET/CT images. Patients diagnosed with pathologic T-stage III rectal cancer with a tumor size > 2 cm were treated with neoadjuvant chemoradiotherapy. Patients with rectal cancer were divided into an internal dataset (n = 116) and an external dataset obtained from a separate institution (n = 40), which were used in the model. AUC was calculated to select image features associated with radiochemotherapy response. In the external test, the machine-learning signature extracted from 18F-FDG PET image features achieved the highest accuracy and AUC value of 0.875 and 0.896. The harmonized first-order radiomics model had a higher efficiency with accuracy and an AUC of 0.771 than the second-order model in the external test. The deep learning model using the balanced dataset showed an accuracy of 0.867 in the internal test but an accuracy of 0.557 in the external test. Deep-learning models using 18F-FDG PET images must be harmonized to demonstrate reproducibility with external data. Harmonized 18F-FDG PET image features as an element of machine learning could help predict chemoradiotherapy responses in external tests with reproducibility.

Deep Neural Network-Based Automatic Dicentric Chromosome Detection Using a Model Pretrained on Common Objects.

Dicentric chromosome assay (DCA) is one of the cytogenetic dosimetry methods where the absorbed dose is estimated by counting the number of dicentric chromosomes, which is a major radiation-induced change in DNA. However, DCA is a time-consuming task and requires technical expertise. In this study, a neural network was applied for automating the DCA. We used YOLOv5, a one-stage detection algorithm, to mitigate these limitations by automating the estimation of the number of dicentric chromosomes in chromosome metaphase images. YOLOv5 was pretrained on common object datasets. For training, 887 augmented chromosome images were used. We evaluated the model using validation and test datasets with 380 and 300 images, respectively. With pretrained parameters, the trained model detected chromosomes in the images with a maximum F1 score of 0.94 and a mean average precision (mAP) of 0.961. Conversely, when the model was randomly initialized, the training performance decreased, with a maximum F1 score and mAP of 0.82 and 0.873%, respectively. These results confirm that the model could effectively detect dicentric chromosomes in an image. Consequently, automatic DCA is expected to be conducted based on deep learning for object detection, requiring a relatively small amount of chromosome data for training using the pretrained network.

Deep Learning-Based Delayed PET Image Synthesis from Corresponding Early Scanned PET for Dosimetry Uptake Estimation.

The acquisition of in vivo radiopharmaceutical distribution through imaging is time-consuming due to dosimetry, which requires the subject to be scanned at several time points post-injection. This study aimed to generate delayed positron emission tomography images from early images using a deep-learning-based image generation model to mitigate the time cost and inconvenience. Eighteen healthy participants were recruited and injected with [18F]Fluorodeoxyglucose. A paired image-to-image translation model, based on a generative adversarial network (GAN), was used as the generation model. The standardized uptake value (SUV) mean of the generated image of each organ was compared with that of the ground-truth. The least square GAN and perceptual loss combinations displayed the best performance. As the uptake time of the early image became closer to that of the ground-truth image, the translation performance improved. The SUV mean values of the nominated organs were estimated reasonably accurately for the muscle, heart, liver, and spleen. The results demonstrate that the image-to-image translation deep learning model is applicable for the generation of a functional image from another functional image acquired from normal subjects, including predictions of organ-wise activity for specific normal organs.

Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer.

The aim of our retrospective study is to develop and externally validate an 18F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The 18F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.

In vivo tissue pharmacokinetics of ERBB2-specific binding oligonucleotide-based drugs by PET imaging.

Although aptamers have shown excellent target specificity in preclinical and clinical studies either by themselves or as aptamer-drug conjugates, their in vivo tissue pharmacokinetic (PK) analysis is still problematic. We aimed to examine the utility of image-based positron emission tomography (PET) to evaluate in vivo tissue PK, target specificity, and applicability of oligonucleotides. For this, fluorine-18-labeled aptamers with erb-b2 receptor tyrosine kinase 2 (ERBB2)-specific binding were synthesized by base-pair hybridization using a complementary oligonucleotide platform. To investigate the PKs and properties of in vivo tissue, usefulness of in vivo PET imaging in the development of an oligonucleotide-based drug as an assessment tool was evaluated in normal and tumor xenografted mice. ERBB2-cODN-idT-APs-[18 F]F ([18 F]1), injected intravenously showed significant and rapid uptake in most tissues except for the initial brain and muscle; the uptake was highest in the heart, followed by kidneys, liver, lungs, gall bladder, spleen, and stomach. The main route of excretion was through the renal tract ~77.8%, whereas about 8.3% was through the biliary tract of the total dose. The estimated effective dose for an adult woman was 0.00189 mGy/MBq, which might be safe. ERBB2-positive tumor could be well visualized in the KPL4 xenograft animal model by in vivo PET imaging. Consequently, the distribution in each organ including ERBB2 expression could be well determined and quantified by PET with fluorine-18-labeled aptamers. In vivo PK parameters such as terminal half-life, time to maximum concentration, area under the curve, and maximum concentration, were also successfully estimated. These results suggest that image-based PET with radioisotope-labeled aptamers could be provide valuable information on properties of oligonucleotide-based drugs in drug discovery of targeted therapeutics against various diseases.

Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes.

This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein-protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker.

Prognostic Value of Axillary Lymph Node Texture Parameters Measured by Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Locally Advanced Breast Cancer with Neoadjuvant Chemotherapy.

BACKGROUND: This study investigated the prognostic value of axillary lymph node (ALN) heterogeneity texture features through 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in patients with locally advanced breast cancer (LABC). METHODS: We retrospectively analyzed 158 LABC patients with FDG-avid, pathology-proven, metastatic ALN who underwent neoadjuvant chemotherapy (NAC) and curative surgery. Tumor and ALN texture parameters were extracted from pretreatment 18F-FDG PET/CT using Chang-Gung Image Texture Analysis software. The least absolute shrinkage and selection operator regression was performed to select the most significant predictive texture parameters. The predictive impact of texture parameters was evaluated for both progression-free survival and pathologic NAC response. RESULTS: The median follow-up period of 36.8 months and progression of disease (PD) was observed in 36 patients. In the univariate analysis, ALN textures (minimum standardized uptake value (SUV) (p = 0.026), SUV skewness (p = 0.038), SUV bias-corrected Kurtosis (p = 0.034), total lesion glycolysis (p = 0.011)), tumor textures (low-intensity size zone emphasis (p = 0.045), minimum SUV (p = 0.047), and homogeneity (p = 0.041)) were significant texture predictors. On the Cox regression analysis, ALN SUV skewness was an independent texture predictor of PD (p = 0.016, hazard ratio 2.3, 95% confidence interval 1.16-4.58). CONCLUSIONS: ALN texture feature from pretreatment 18F-FDG PET/CT is useful for the prediction of LABC progression.

Predictive Value of 18F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer.

We investigated predictions from 18F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent 18F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians' assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (p < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians.

Synthesis and Evaluation of a 18F-Labeled Ligand for PET Imaging of Colony-Stimulating Factor 1 Receptor.

Neuroinflammation involves activation of glial cells in the brain, and activated microglia play a particularly important role in neurodegenerative diseases such as Alzheimer's disease (AD). In this study, we developed 5-cyano-N-(4-(4-(2-[18F]fluoroethyl)piperazin-1-yl)-2-(piperidin-1-yl)phenyl)furan-2-carboxamide ([18F]1) for PET imaging of colony-stimulating factor 1 receptor (CSF1R), an emerging target for neuroinflammation imaging. Non-radioactive ligand 1 exhibited binding affinity comparable to that of a known CSF1R inhibitor, 5-cyano-N-(4-(4-methylpiperazin-1-yl)-2-(piperidin-1-yl)phenyl)furan-2-carboxamide (CPPC). Therefore, we synthesized radioligand [18F]1 by radiofluorination of chlorine-substituted precursor 7 in 13-15% decay-corrected radiochemical yield. Dynamic PET/CT images showed higher uptake in the lipopolysaccharide (LPS)-treated mouse brain than in control mouse brain. Ex vivo biodistribution study conducted at 45 min after radioligand injection showed that the brain uptake in LPS mice increased by 78% compared to that of control mice and was inhibited by 22% in LPS mice pretreated with CPPC, indicating specificity of [18F]1 for CSF1R. A metabolism study demonstrated that the radioligand underwent little metabolism in the mouse brain. Taken together, these results suggest that [18F]1 may hold promise as a radioligand for CSF1R imaging.

The Feasibility of 64Cu-PSMA I&T PET for Prostate Cancer.

Background: The goal of this research was to investigate the feasibility of 64Cu labeling in prostate-specific membrane antigen imaging and therapy (PSMA I&T) for PSMA positron emission tomography (PET) imaging and biodistribution evaluation. Materials and Methods: PSMA I&T was labeled with 64Cu, and stability in human and mouse sera was evaluated. Prostate cancer cell lines were used for specific uptake assays (22RV1 for PSMA-positive, PC-3 for -negative). Both PC-3 and 22RV1 cells were transplanted into the left and right thighs in a mouse for PET/computed tomography (CT) imaging. Biodistribution was performed using 22RV1 tumor models. Results: Labeling yield (decay corrected) of 64Cu-PSMA I&T was more than 95% compared to the free 64Cu peak. The serum stability of 64Cu-PSMA I&T was maintained at more than 90% until 60 h. Regarding the specific binding of 64Cu-PSMA I&T was 7.5-fold higher to 22RV1 cells than PC-3 cells (p < 0.001). On PET/CT imaging, more specific 64Cu-PSMA I&T uptake was observed to 22RV1 tumors than to PC-3 tumors. In the PSMA blocking study using 2-phosphonomethoxypropyl adenine (2-PMPA), the 64Cu-PSMA I&T signal significantly decreased in the 22RV1 tumor region. In the biodistribution study, the kidney uptake was the highest among all organs at 2 h (52.6 ± 20.8%ID/g) but sharply decreased at 24 and 48 h. Also, the liver showed similar uptake over time (range, 10-12%ID/g). On the contrary, 64Cu-PSMA I&T uptake of the tumors increased with time and peaked at 48 h (5.6 ± 0.1%ID/g). Conclusions: PSMA I&T labeled with 64Cu showed the feasibility of the PSMA specific PET imaging through in vitro and in vivo studies. Furthermore, 64Cu-PSMA I&T might be considered as the candidate of future clinical trial.

Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Using Convolutional Neural Network of Tumor Center 18F-FDG PET Images.

We compared the accuracy of prediction of the response to neoadjuvant chemotherapy (NAC) in osteosarcoma patients between machine learning approaches of whole tumor utilizing fluorine-18fluorodeoxyglucose (18F-FDG) uptake heterogeneity features and a convolutional neural network of the intratumor image region. In 105 patients with osteosarcoma, 18F-FDG positron emission tomography/computed tomography (PET/CT) images were acquired before (baseline PET0) and after NAC (PET1). Patients were divided into responders and non-responders about neoadjuvant chemotherapy. Quantitative 18F-FDG heterogeneity features were calculated using LIFEX version 4.0. Receiver operating characteristic (ROC) curve analysis of 18F-FDG uptake heterogeneity features was used to predict the response to NAC. Machine learning algorithms and 2-dimensional convolutional neural network (2D CNN) deep learning networks were estimated for predicting NAC response with the baseline PET0 images of the 105 patients. ML was performed using the entire tumor image. The accuracy of the 2D CNN prediction model was evaluated using total tumor slices, the center 20 slices, the center 10 slices, and center slice. A total number of 80 patients was used for k-fold validation by five groups with 16 patients. The CNN network test accuracy estimation was performed using 25 patients. The areas under the ROC curves (AUCs) for baseline PET maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and gray level size zone matrix (GLSZM) were 0.532, 0.507, 0.510, and 0.626, respectively. The texture features test accuracy of machine learning by random forest and support vector machine were 0.55 and 0. 54, respectively. The k-fold validation accuracy and validation accuracy were 0.968 ± 0.01 and 0.610 ± 0.04, respectively. The test accuracy of total tumor slices, the center 20 slices, center 10 slices, and center slices were 0.625, 0.616, 0.628, and 0.760, respectively. The prediction model for NAC response with baseline PET0 texture features machine learning estimated a poor outcome, but the 2D CNN network using 18F-FDG baseline PET0 images could predict the treatment response before prior chemotherapy in osteosarcoma. Additionally, using the 2D CNN prediction model using a tumor center slice of 18F-FDG PET images before NAC can help decide whether to perform NAC to treat osteosarcoma patients.

Low-Dose PET Imaging of Tumors in Lung and Liver Regions Using Internal Motion Estimation.

Motion estimation and compensation are necessary for improvement of tumor quantification analysis in positron emission tomography (PET) images. The aim of this study was to propose adaptive PET imaging with internal motion estimation and correction using regional artificial evaluation of tumors injected with low-dose and high-dose radiopharmaceuticals. In order to assess internal motion, molecular sieves imitating tumors were loaded with 18F and inserted into the lung and liver regions in rats. All models were classified into two groups, based on the injected radiopharmaceutical activity, to compare the effect of tumor intensity. The PET study was performed with injection of F-18 fluorodeoxyglucose (18F-FDG). Respiratory gating was carried out by external trigger device. Count, signal to noise ratio (SNR), contrast and full width at half maximum (FWHM) were measured in artificial tumors in gated images. Motion correction was executed by affine transformation with estimated internal motion data. Monitoring data were different from estimated motion. Contrast in the low-activity group was 3.57, 4.08 and 6.19, while in the high-activity group it was 10.01, 8.36 and 6.97 for static, 4 bin and 8 bin images, respectively. The results of the lung target in 4 bin and the liver target in 8 bin showed improvement in FWHM and contrast with sufficient SNR. After motion correction, FWHM was improved in both regions (lung: 24.56%, liver: 10.77%). Moreover, with the low dose of radiopharmaceuticals the PET image visualized specific accumulated radiopharmaceutical areas in the liver. Therefore, low activity in PET images should undergo motion correction before quantification analysis using PET data. We could improve quantitative tumor evaluation by considering organ region and tumor intensity.

Tissue pharmacokinetics of DHP107, a novel lipid-based oral formulation of paclitaxel, in mice and patients by positron emission tomography.

DHP107 is a newly developed lipid-based oral formulation of paclitaxel. We evaluated the in vivo tissue pharmacokinetics (PKs) of DHP107 in mice and patients using positron emission tomography (PET). Radioisotope-labeled [3 H]DHP107 and [18 F]DHP107 for oral administration were formulated in the same manner as the manufacturing process of DHP107. In vivo tissue PK were assessed in healthy ICR mice and breast cancer xenografted SCID mice. Two patients with metastatic breast cancer were clinically evaluated for absorption at the target lesion after internal absorbed dose estimation. Whole-body PET/computed tomography data were acquired in healthy and xenografted mice and in patients up to 10-24 h after administration. Tissue [18 F]DHP107 signals were plotted against time and PK parameters were determined. The amounts of radioactivity in various organs and excreta were determined using a beta-counter and are expressed as the percentage of injected dose (ID). Oral [18 F]DHP107 was well-absorbed and reached the target lesion in mice and patients with breast cancer. Significant amounts of radioactivity were found in the stomach, intestine, and liver after oral administration of [3 H]- and [18 F]DHP107 in healthy mice. The [18 F]DHP107 reached a peak distribution of 0.7-0.8%ID in the tumor at 5.6-7.3 h in the xenograft model. The [18 F]DHP107 distribution in patients with metastatic breast cancer was the highest at 3-4 h postadministration. Systemic exposures after administration of a DHP107 therapeutic dose were comparable with those in previous studies. PET using radioisotope-labeled drug candidates is useful for drug development and can provide valuable information that can complement plasma PK data, particularly in early phase clinical trials.

Preliminary Radiogenomic Evidence for the Prediction of Metastasis and Chemotherapy Response in Pediatric Patients with Osteosarcoma Using 18F-FDF PET/CT, EZRIN and KI67.

Chemotherapy response and metastasis prediction play important roles in the treatment of pediatric osteosarcoma, which is prone to metastasis and has a high mortality rate. This study aimed to estimate the prediction model using gene expression and image texture features. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of 52 pediatric osteosarcoma patients were used to estimate the machine learning algorithm. An appropriate algorithm was selected by estimating the machine learning accuracy. 18F-FDG PET/CT images of 21 patients were selected for prediction model development based on simultaneous KI67 and EZRIN expression. The prediction model for chemotherapy response and metastasis was estimated using area under the curve (AUC) maximum image texture features (AUC_max) and gene expression. The machine learning algorithm with the highest test accuracy in chemotherapy response and metastasis was selected using the random forest algorithm. The chemotherapy response and metastasis test accuracy with image texture features was 0.83 and 0.76, respectively. The highest test accuracy and AUC of chemotherapy response with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.89, respectively. The highest test accuracy and AUC of metastasis with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.8, respectively. The metastasis prediction accuracy increased by 10% using radiogenomics data.

A Phase 0 Microdosing PET/CT Study Using O-[18F]Fluoromethyl-d-Tyrosine in Normal Human Brain and Brain Tumor.

PURPOSE: The aim of the present study was to obtain information about distribution, radiation dosimetry, toxicity, and pharmacokinetics of O-[18F]fluoromethyl-d-tyrosine (d-18F-FMT), an amino acid PET tracer, in patients with brain tumors. PATIENTS AND METHODS: A total of 6 healthy controls (age = 19-25 years, 3 males and 3 females) with brain PET images and radiation dosimetry and 12 patients (median age = 60 years, 6 males and 6 females) with primary (n = 5) or metastatic brain tumor (n = 7) were enrolled. We acquired 60-minute dynamic brain PET images after injecting 370 MBq of d-18F-FMT. Time-activity curves of d-18F-FMT uptake in normal brain versus brain tumors and tumor-to-background ratio were analyzed for each PET data set. RESULTS: Normal cerebral uptake of d-18F-FMT decreased from 0 to 5 minutes after injection, but gradually increased from 10 to 60 minutes. Tumoral uptake of d-18F-FMT reached a peak before 30 minutes. Tumor-to-background ratio peaked at less than 15 minutes for 8 patients and more than 15 minutes for 4 patients. The mean effective dose was calculated to be 13.2 µSv/MBq. CONCLUSIONS: Using d-18F-FMT as a PET radiotracer is safe. It can distinguish brain tumor from surrounding normal brain tissues with a high contrast. Early-time PET images of brain tumors should be acquired because the tumor-to-background ratio tended to reach a peak within 15 minutes after injection.

Targeted alpha immunotherapy of CD20-positive B-cell lymphoma model: dosimetry estimate of 225Ac-DOTA-rituximab using 64Cu-DOTA-rituximab.

OBJECTIVE: The aim of this study was to evaluate the radiation dosimetry of alpha-emitter 225Ac-DOTA-rituximab using Monte Carlo simulation of 64Cu-DOTA-rituximab. METHODS: CD20 expression was evaluated in lymphoma cell lines (Jurkat and Raji). DOTA-rituximab was conjugated and then chelated by 64Cu. Tumor xenograft models were established in BALB/c-nu mice. Animal PET/CT imaging was obtained after tail vein injection with and without a pre-dose of 2 mg of cold rituximab. Specific binding of tumors was evaluated by an organ distribution assay and autoradiography. CD20 expression in tumor tissues was evaluated by immunohistochemistry. The residence time was calculated using 64Cu-DOTA-rituximab PET/CT acquisition data using OLINDA/EXM software. 225Ac-DOTA-rituximab tumor dosimetry was performed using Monte Carlo simulation with 64Cu-DOTA-rituximab PET/CT images. RESULTS: Specific binding of Raji cells (CD20 positive) was 90 times that of Jurkat cells (CD20 negative) (p < 0.0001). Immunoreactivity was more than 75%. PET/CT imaging with 64Cu-DOTA-rituximab was specifically observed in tumors. The radioactivity of the tumor was much higher than that of other organs, and tumor uptake was related to CD20 expression. The predicted human dose for the administration of 64Cu-DOTA-rituximab was measured as the effective dose (1.07E-02 mSv/MBq). In the tumor region, equivalent doses of 225Ac-DOTA-rituximab (14 SvRBE5/MBq) were much higher (74-fold) than those of 64Cu-DOTA-rituximab (0.19 SvRBE5/MBq) (p < 0.01). CONCLUSION: Tumor dosimetry of 225Ac-DOTA-rituximab can be estimated via the Monte Carlo simulation of 64Cu-DOTA-rituximab. 225Ac-DOTA-rituximab can be employed for lymphoma as targeted alpha therapy.