Synthesis, radiolabeling and evaluation of [99mTc][Tc-HYNIC/EDDA]-Met(O) as a early agent for amino acid metabolic imaging in C6 glioblastoma tumor.

Amino acid metabolism is recognized as a target for medical imaging due to its increase in malignant cells. Several radiotracers with primary achievement and possible subsequent chances have been designed and tested to image amino acid metabolism. Here, we report a new amino acid conjugate, with the purpose of extending [99mTc][Tc-HYNIC/EDDA]-Met(O) for single photon emission tomography (SPECT) imaging. The S-oxo-l-methionine (Met(O)) amino acid hydrazinonicotinamide (HYNIC) chelator conjugate (HYNIC-Met(O)) was prepared, using Fmoc solid-phase synthesis, and was radiolabeled with [99mTc]technetium pertechnetate, using tricine and ethylenediamine-N,N-diacetic acid (EDDA) as co-ligands. In vitro cellular uptake profile and saturation binding of radiotracer were determined on C6 glioma cells. Biodistribution and imaging studies were carried out on rat bearing C6 tumor tissue grafts. [99mTc][Tc-HYNIC/EDDA]-Met(O) was prepared in high yield and radiochemical purity (>98 %). The partition coefficient result showed that radioconjugate was very hydrophilic. The radioconjugate indicated both high cell uptake and in vitro internalization. Low nanomolar dissociation constant (66.02 nM) in C6 glioma cells was obtained for it as well. [99mTc][Tc-HYNIC/EDDA]-Met(O) revealed magnificent tumor uptake at early time points, with 1.98 ± 0.33 % injected activity per gram tumor (% IA/g) at 30 min post injection. The tumor uptake continued for 1 and 2 h and was 0.45 ± 0.33 % IA/g at 4 h. The uptake in other organs decreased much more rapidly causing high tumor to normal organ ratios so that the highest ratio of 13.25 of tumor-to-muscle at 60 min after injection was obtained with high contrast in gamma imaging. These results point out a very favorable [99mTc]Tc-labeled amino acid for targeting amino acid metabolism through target system L amino acid transporter (LAT1) in malignant cells especially C6 glioma cells. [99mTc][Tc-HYNIC/EDDA]-Met(O) manifests extremely good distribution, excretion and imaging attributes. So it seems to be an appropriate nominate for clinical imaging.

Longitudinal clustering analysis and prediction of Parkinson's disease progression using radiomics and hybrid machine learning.

BACKGROUND: We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson's disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. METHODS: We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. RESULTS: We identified 3 distinct progression trajectories. Hotelling's t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. CONCLUSIONS: This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data.

Feature selection and machine learning methods for optimal identification and prediction of subtypes in Parkinson's disease.

OBJECTIVES: The present work focuses on assessment of Parkinson's disease (PD), including both PD subtype identification (unsupervised task) and prediction (supervised task). We specifically investigate optimal feature selection and machine learning algorithms for these tasks. METHODS: We selected 885 PD subjects as derived from longitudinal datasets (years 0-4; Parkinson's Progressive Marker Initiative), and investigated 981 features including motor, non-motor, and imaging features (SPECT-based radiomics features extracted using our standardized SERA software). Two different hybrid machine learning systems (HMLS) were constructed and applied to the data in order to select optimal combinations in both tasks: (i) identification of subtypes in PD (unsupervised-clustering), and (ii) prediction of these subtypes in year 4 (supervised-classification). From the original data based on years 0 (baseline) and 1, we created new datasets as inputs to the prediction task: (i,ii) CSD0 and CSD01: cross-sectional datasets from year 0 only and both years 0 & 1, respectively; (iii) TD01: timeless dataset from both years 0 & 1. In addition, PD subtype in year 4 was considered as outcome. Finally, high score features were derived via ensemble voting based on their prioritizations from feature selector algorithms (FSAs). RESULTS: In clustering task, the most optimal combinations (out of 981) were selected by individual FSAs to enable high correlation compared to using all features (arriving at 547). In prediction task, we were able to select optimal combinations, resulting in an accuracy >90% only for timeless dataset (TD01); there, we were able to select the most optimal combination using 77 features, directly selected by FSAs. In both tasks, however, using combination of only high score features from ensemble voting did not enable acceptable performances, showing optimal feature selection via individual FSAs to be more effective. CONCLUSION: Combining non-imaging information with SPECT-based radiomics features, and optimal utilization of HMLSs, can enable robust identification of subtypes as well as appropriate prediction of these subtypes in PD patients. Moreover, use of timeless dataset, beyond cross-sectional datasets, enabled predictive accuracies over 90%. Overall, we showed that radiomics features extracted from SPECT images are important in clustering as well as prediction of PD subtypes.

Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning.

OBJECTIVES: It is important to subdivide Parkinson's disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features. METHODS: We applied multiple feature-reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 feature-reduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). We subsequently performed: i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to the size of the samples. RESULTS: When using no radiomics features, the clusters were not robust to variations in features, whereas, utilizing radiomics information enabled consistent generation of clusters through ensemble analysis of trajectories. We arrived at 3 distinct subtypes, confirmed using the training and testing process of k-means, as well as Hotelling's T2 test. The 3 identified PD subtypes were 1) mild; 2) intermediate; and 3) severe, especially in terms of dopaminergic deficit (imaging), with some escalating motor and non-motor manifestations. CONCLUSION: Appropriate hybrid systems and independent statistical tests enable robust identification of 3 distinct PD subtypes. This was assisted by utilizing radiomics features from SPECT images (segmented using MRI). The PD subtypes provided were robust to the number of the subjects, and features.

Machine learning methods for optimal prediction of motor outcome in Parkinson's disease.

PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus, it is critical to improve prediction of outcome in PD patients. METHODS: We systematically probed a range of robust predictor algorithms, aiming to find best combinations of features for significantly improved prediction of motor outcome (MDS-UPDRS-III) in PD. We analyzed 204 PD patients with 18 features (clinical measures; dopamine-transporter (DAT) SPECT imaging measures), performing different randomized arrangements and utilizing data from 64%/6%/30% of patients in each arrangement for training/training validation/final testing. We pursued 3 approaches: i) 10 predictor algorithms (accompanied with automated machine learning hyperparameter tuning) were first applied on 32 experimentally created combinations of 18 features, ii) we utilized Feature Subset Selector Algorithms (FSSAs) for more systematic initial feature selection, and iii) considered all possible combinations between 18 features (262,143 states) to assess contributions of individual features. RESULTS: A specific set (set 18) applied to the LOLIMOT (Local Linear Model Trees) predictor machine resulted in the lowest absolute error 4.32 ± 0.19, when we firstly experimentally created 32 combinations of 18 features. Subsequently, 2 FSSAs (Genetic Algorithm (GA) and Ant Colony Optimization (ACO)) selecting 5 features, combined with LOLIMOT, reached an error of 4.15 ± 0.46. Our final analysis indicated that longitudinal motor measures (MDS-UPDRS-III years 0 and 1) were highly significant predictors of motor outcome. CONCLUSIONS: We demonstrate excellent prediction of motor outcome in PD patients by employing automated hyperparameter tuning and optimal utilization of FSSAs and predictor algorithms.

Novel 175Yb-Poly (L-Lactic Acid) Microspheres for Transarterial Radioembolization of Unrespectable Hepatocellular Carcinoma.

Novel biodegradable Poly (L-lactic acid) (PLLA) microspheres containing ytterbium were designed for intra-tumoral radiotherapy, especially for radioembolization. 175Yb possess both therapeutic beta and diagnostic gamma radiations. In this work, a process of making ready radiomicrospheres 175Yb (acac)3-loaded PLLA for more consideration has been investigated. The radiomicrospheres were prepared with approximate size of 20-40 µm, and radionuclidic purity > 92%. The radiomicrospheres were stable in-vitro for up to 72 h in normal saline, and also in human serum albumin (HSA). Biodistribution in mice bearing 4T1 tumor showed specific radionuclide uptake over 48 h. Tumor necrosis was also observed at the injection site up to 12 days after injection. These data indicated that 175Yb-PLLA microspheres could be prepared and considered further for radiomicrospheres tumor therapy.

Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease.

BACKGROUND: Given the increasing recognition of the significance of non-motor symptoms in Parkinson's disease, we investigate the optimal use of machine learning methods for the prediction of the Montreal Cognitive Assessment (MoCA) score at year 4 from longitudinal data obtained at years 0 and 1. METHODS: We selected n = 184 PD subjects from the Parkinson's Progressive Marker Initiative (PPMI) database (93 features). A range of robust predictor algorithms (accompanied with automated machine learning hyperparameter tuning) and feature subset selector algorithms (FSSAs) were selected. We utilized 65%, 5% and 30% of patients in each arrangement for training, training validation and final testing respectively (10 randomized arrangements). For further testing, we enrolled 308 additional patients. RESULTS: First, we employed 10 predictor algorithms, provided with all 93 features; an error of 1.83 ±â€¯0.13 was obtained by LASSOLAR (Least Absolute Shrinkage and Selection Operator - Least Angle Regression). Subsequently, we used feature subset selection followed by predictor algorithms. GA (Genetic Algorithm) selected 18 features; subsequently LOLIMOT (Local Linear Model Trees) reached an error of 1.70 ±â€¯0.10. DE (Differential evolution) also selected 18 features and coupled with Thiel-Sen regression arrived at a similar performance. NSGAII (Non-dominated sorting genetic algorithm) yielded the best performance: it selected six vital features, which combined with LOLIMOT reached an error of 1.68 ±â€¯0.12. Finally, using this last approach on independent test data, we reached an error of 1.65. CONCLUSION: By employing appropriate optimization tools (including automated hyperparameter tuning), it is possible to improve prediction of cognitive outcome. Overall, we conclude that optimal utilization of FSSAs and predictor algorithms can produce very good prediction of cognitive outcome in PD patients.

Preparation and evaluation of 188 Re sulfide colloidal nanoparticles loaded biodegradable poly (L-lactic acid) microspheres for radioembolization therapy.

Radioembolization with radioactive microspheres has been an effective method for the treatment of liver lesions. The aim of this study was to prepare carrier-free 188 Re loaded poly (L-lactic acid) (PLLA) microspheres through 188 Re sulfide colloidal nanoparticles (188 Re-SC nanoparticles). The formation of 188 Re-SC nanoparticles was confirmed by ultraviolet-visible spectrophotometry. The labeling yield of 188 Re-SC nanoparticles was verified using the RTLC method. Effects of synthesis parameters on morphology and size of prepared 188 Re-sulfide colloidal-PLLA microspheres (188 Re-SC-PLLA microspheres) were studied by scanning electron microscopy. In vitro stability of 188 Re-SC-PLLA microspheres was investigated in normal saline at room temperature and in human serum at 37°C. In vivo distribution studies and gamma camera imaging were performed in healthy BALB/c mice. The microspheres could be prepared with sizes between 13 and 48 µm (modal value 29 µm) and radiolabeling efficiency >99%. After incubation, the microspheres were found stable in vitro up to 72 hours. The biodistribution after intravenous injection in healthy BALB/c mice showed high accumulation in lung as a first capture pathway organ for microsphere followed by great retention over 48 hours for these microspheres. These data show that 188 Re-SC-PLLA microspheres are suitable candidate for clinical studies.

Production, quality control, and determination of human absorbed dose of no carrier added 177 Lu-risedronate for bone pain palliation therapy.

In this study, the radiocomplexation of risedronic acid, a potent bisphosphonate with a no carrier added (NCA) 177 Lu, was investigated and followed by quality control studies, biodistribution evaluation, and dosimetry study for human based on biodistribution data in Wistar rats. The moderate energy ß- emitter, 177 Lu (T½  = 6.7 days, Eßmax  = 497 keV), has been considered as a potential agent for development of bone-seeking radiopharmaceuticals. Because the specific activity of the radiolabeled carrier molecules should be high, the NCA radionuclides have an effective role in nuclear medicine. Many researchers illustrated an NCA 177 Lu production; among these separation techniques, extraction chromatography has been considered more capable than other methods. The NCA 177 Lu was produced with specific activity of 48 Ci/mg and radionuclidic purity of 99.99% by the irradiation of enriched 176 Yb target in thermal neutron flux of 4 × 1013  n·cm-2 ·s-1 for 14 days. The NCA 177 Lu was mixed to a desired amount of sodium risedronate (15 mg/mL, 200 µL) and incubated with stirring at 95°C for 30 minutes. The radiochemical purity of 177 Lu-risedronate was determined by radio thin-layer chromatography, and high radiochemical purities (>97%) were obtained under optimized reaction conditions. The complex was injected to Wistar rats, and complex biodistribution was performed 4 hours to 7 days postinjections showing high bone uptake (9.8% ± 0.24% ID/g at 48 hours postinjection). Also, modeling the radiation dose delivery by RADAR software for the absorbed dose evaluation of each human organ showed a major accumulation of the radiocomplex in bone tissue.

Production and quality control 177Lu (NCA)-DOTMP as a potential agent for bone pain palliation.

Skeletal uptake of radiolabeled-1, 4, 7, 10-tetraazacyclododecane-1, 4, 7, 10-tetramethylene phosphoric acid (e.g., 177Lu-DOTMP) complex, is used for bone pain palliation. The moderate energy of ß-emitting 177Lu (T½ = 6.7 d, Eßmax = 497keV) has been considered as a potential radionuclide for development of the bone-seeking radiopharmaceutical. Since the specific activity of the radiolabeled carrier molecules should be high, the "no-carrier-added radionuclides" have sig-nificant roles in nuclear medicine. Many researchers illustrated no-carrier-added 177Lu production; among these separation techniques such as ion exchange chromatography, reversed phase ion-pair, and electrochemical method, extraction chromatography has been considered more capable than other methods. In order to optimize the conditions, some effective factors on separation of Lu/Yb were investigated by EXC. The NCA 177Lu, produced by this method, was mixed with 300 µl of DOTMP solution (20 mg in 1 mL of 0.5 M NaHCO3, pH = 8) and incu-bated under stirring at room temperature for 45 min. Radiochemical purity of the 177Lu-DOTMP complex was determined using radio-thin-layer chromatography (RTLC) method. The complex was injected to wild-type rats and biodistribution was then studied for seven days. The NCA 177Lu was produced with specific activ-ity of 48 Ci/mg and with a radinuclidic purity of 99.99% through irradiation of enriched 176Yb target (1 mg) in a thermal neutron flux of 4 × 1013 n.cm-2.s-1 for 14 days. 177Lu-DOTMP was obtained with high radiochemical purities (> 98%) under optimized reaction conditions. The radiolabeled complex exhibited excellent stability at room temperature. Biodistribution of the radiolabeled complex studies in rats showed favorable selective skeletal uptake with rapid clearance from blood along with insignificant accumulation within the other nontargeted organs.

99m Tc-tricarbonyl labeling of paclitaxel as an antimicrotubule agent and its evaluation in B16-F10 melanoma tumor-bearing mice.

In the present study paclitaxel (taxol) was labeled with [99m Tc(CO)3 (H2 O)3 ]+ core. Labeling was optimized, and radiochemical analysis was determined by thin layer chromatography and high performance liquid chromatography. Radiocomplex was evaluated and verified further as a tumor characterization agent in B16-F10 melanoma tumor-bearing mice. The [99m Tc(CO)3 (H2 O)3 ]+ -paclitaxel complex with high specific activity (0.77 GBq/µmol) and labeling yield (96.8 ± 1.3) was obtained. No decrease in labeling was observed up to 6 hours, and the stability of the radiocomplex was found adequate. Our main achievement was high accumulation of radiolabeled paclitaxel in tumor (4.51 ± 0.65 percentage injected dose per gram [%ID/g] at 2-h postinjection) followed by significant reduction (1.86 ± 0.27%ID/g) at 4-hour postinjection. Because paclitaxel is a substrate for multidrug resistance, 99m Tc-tricarbonyl-paclitaxel imaging would be useful for tumor characterization rather than tumor detection.

Production, biodistribution, and dosimetry of (47)Sc-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetramethylene phosphonic acid as a bone-seeking radiopharmaceutical.

In this study 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetramethylene phosphonic acid (DOTMP) was used as the polyaminophosphonic acid carrier ligand and the therapeutic potential of the bone seeking radiopharmaceutical (47)Sc-DOTMP was assessed by measuring its dosage-dependent skeletal uptake and then the absorbed radiation dose of human organs was estimated. Because of limited availability of (47)Sc we performed some preliminary studies using (46)Sc. (46)Sc was produced with a specific activity of 116.58 MBq/mg (3.15 mCi/mg) and radionuclide purity of 98%. (46)Sc-DOTMP was prepared and an activity of 1.258 MBq (34 µCi) at a chelant-to-metal ratio of 60:1 was administered to five groups of mice with each group containing 3 mice that were euthanized at 4, 24, 48, 96 and 192 h post administration. The heart, lungs, liver, spleen, kidneys, intestine, skin, muscle, and a femur were excised, weighed, and counted. The data were analyzed to determine skeletal uptake and source organ residence times and cumulated activities for (47)Sc-DOTMP. (46)Sc-DOTMP complex was prepared in radiochemical purity about 93%. In vitro stability of complex was evaluated at room temperature for 48 h. Biodistribution studies of complex in mice were studied for 7 days. The data were analyzed to estimate skeletal uptake and absorbed radiation dose of human organs using biodistribution data from mice. By considering the results, (47)Sc-DOTMP is a possible therapeutic agent for using in palliation of bone pain due to metastatic skeletal lesions from several types of primary cancers in prostate, breast, etc.

Preparation, quality control and biodistribution assessment of ¹5³Sm-BPAMD as a novel agent for bone pain palliation therapy.

OBJECTIVE: Various phosphonate ligands labeled with ß(-)-emitting radionuclides have shown good efficacy for bone pain palliation. In this study, a new agent for bone pain palliation has been developed. METHODS: ¹5³Sm-(4-{[(bis(phosphonomethyl))carbamoyl]methyl}-7,10-bis(carboxymethyl)-1,4,7,10-tetraazacyclododec-1-yl) acetic acid (¹5³Sm-BPAMD) complex was prepared using BPAMD ligand and ¹5³SmCl3. The effect of various parameters on the labeling yield of ¹5³Sm-BPAMD including ligand concentration, pH, temperature and reaction time were studied. Radiochemical purity of the radiolabeled complex was checked by instant thin layer chromatography (ITLC). Stability studies of the complex in the final preparation and in the presence of human serum were performed up to 48 h. Partition coefficient and hydroxyapatite (HA) binding of the complex were investigated and biodistribution studies (SPECT imaging and scarification) were performed after injection of the complex to Syrian mice up to 48 h post-injection. The biodistribution of the complex was compared with the biodistribution of the ¹5³Sm cation in the same type mice. RESULTS: ¹5³Sm-BPAMD was prepared in high radiochemical purity >98% and specific activity of 267 GBq/mmol at the optimal conditions. The complex demonstrated significant stability at room temperature and in human serum at least for 48 h. HA binding assay demonstrated that at the amount of more than 5 mg, approximately, all radiolabeled complex was bound to HA. At the pH 7.4, LogP o/w was -1.86 ± 0.02. Both SPECT and scarification showed major accumulation of the labeled compound in the bone tissue. CONCLUSION: The results show that ¹5³Sm-BPAMD has interesting characteristics as an agent for bone pain palliation; however, further biological studies in other mammals are still needed.

Synthesis and evaluation of a 99mTc-labeled tubulin-binding agent for tumor imaging.

Cholchicine and its derivatives are very potent tubulin-binding compounds and can be used as a potential tumor targeting agents. In this study, colchicine was labeled with (99m) Tc via hydrazinonicotinic acid (HYNIC) and was investigated further. HYNIC/cholchicine was synthesized and labeling with (99m)Tc was performed at 95 °C for 15 min and radiochemical analysis included HPLC method. The stability of radiconjugate was checked in the presence of human serum at 37 °C up to 24 h. Biodistribution was studied in breast tumor-bearing mice. Labeling yield of 95.8 ± 0.54% was obtained corresponding to a specific activity of 54 MBq/µmol. Radioconjugate showed good stability in the presence of human serum. Biodistribution studies in tumor-bearing mice showed that (99m) Tc/HYNIC/colchicine conjugate accumulated in tumor with good uptake (3.17 ± 0.14% g/g at 1 h post-injection). The radioconjugate was cleared fast from normal organs and showed clearance through urinary and hepatobiliary systems with accumulation of activity in kidneys and intestine. This radioconjugate may be useful to assess the presence of tumor by imaging.

The effect of rib and lung heterogeneities on the computed dose to lung in Ir-192 high-dose-rate breast brachytherapy: Monte Carlo versus a treatment planning system.

AIMS: This study investigates to what extent the dose received by lungs from a commercially available treatment planning system, Ir-192 high-dose-rate (HDR), in breast brachytherapy, is accurate, with the emphasis on tissue heterogeneities, and taking into account the presence of ribs, in dose delivery to the lung. MATERIALS AND METHODS: A computed tomography (CT) scan of a breast was acquired and transferred to the 3-D treatment planning system and was also used to construct a patient-equivalent phantom. An implant involving 13 plastic catheters and 383 programmed source dwell positions were simulated, using the Monte Carlo N-Particle eXtended (MCNPX) code. The Monte Carlo calculations were compared with the corresponding commercial treatment planning system (TPS) in the form of percentage isodose and cumulative dose-volume histogram (DVH) in the breast, lungs, and ribs. RESULTS: The comparison of the Monte Carlo results and the TPS calculations showed that a percentage of isodose greater than 75% in the breast, which was located rather close to the implant or away from the breast curvature surface and lung boundary, were in good agreement. TPS calculations overestimated the dose to the lung for lower isodose contours that were lying near the breast surface and the boundary of breast and lung and were relatively away from the implant. CONCLUSIONS: Taking into account the ribs and entering the actual data for breasts, ribs, and lungs, revealed an average overestimation of the dose by a factor of 8% in the lung for TPS calculations. Therefore, the accuracy of the TPS results may be limited to regions near the implants where the treatment is planned, and is a more conservative approach for regions at boundaries with curvatures or tissues with a different material than that in the breast.

Correction of head movements in positron emission tomography using point source tracking system: a simulation study.

AIM: The motion of the head during brain positron emission tomography (PET) acquisitions has been identified as a source of artifact in the reconstructed image. In this study, a method is described to develop an image-based motion correction technique for correcting the post-acquisition data without using external optical motion-tracking system such as POLARIS. METHOD: In this technique, GATE has been used to simulate PET brain scan using point sources mounted around the head to accurately monitor the position of the head during the time frames. RESULT: The measurement of head motion in each frame showed a transformation in the image frame matrix, resulting in a fully corrected data set. CONCLUSION: Using different kinds of phantoms and motions, the accuracy of the correction method is tested and its applicability to experimental studies is demonstrated as well.

Preclinical evaluation of a new bombesin analog for imaging of gastrin-releasing peptide receptors.

Bombesin (BBN) is a peptide showing high affinity for the gastrin-releasing peptide receptor. Tumors such as prostate, small cell lung cancer, breast, gastric, and colon cancer are known to over express receptors to BBN and gastrin-releasing peptide (GRP). The goal of this study was to evaluate a new (67)Ga radiolabeled BBN analog based on the bifunctional chelating ligand DOTA (1, 4, 7, 10-tetraazacyclododecane-1, 4, 7, 10-tetraacetic acid), which could be used as a tool for diagnosis of GRP receptor-positive tumors. DOTA-GABA-BBN (7-14) NH(2) was synthesized using a standard Fmoc strategy. Labeling with (67)Ga was performed at 95°C for 30 minutes in ammonium acetate buffer (pH = 4.8). Radiochemical analysis involved ITLC and HPLC methods. The stability of radiopeptide was examined in the presence of human serum at 37°C up to 24 hours. The receptor-bound internalization and externalization rates were studied in GRP receptor expressing PC-3 cells. Biodistribution of radiopeptide was studied in nude mice bearing PC-3 tumor. Labeling yield of >90% was obtained corresponding to a specific activity of approximatrly 2.6 MBq/nmol. Peptide conjugate showed good stability in the presence of human serum. The radioligand showed a good and specific internalization into PC-3 cells (16.13% ± 0.71% at 4 hours). After 4 hours, a considerable amount of activity (52.42% ± 1.86%) was externalized. In animal biodistribution studies, a receptor-specific uptake of radioactivity was observed in GRP-receptor-positive organs. After 4 hours, the uptake in mouse tumor and pancreas was 1.30% ± 0.18% ID/g (percentage of injected dose per gram of tissue) and 1.21% ± 0.13% ID/g, respectively. These data show that [(67)Ga]-DOTA-GABA-BBN (7-14) NH2 is a specific radioligand for GRP receptor positive tumors and is a suitable candidate for clinical studies.