Optimization of supervised cluster analysis for extracting reference tissue input curves in (R)-[(11)C]PK11195 brain PET studies.
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ABSTRACT:
SUBMITTER: Yaqub M
PROVIDER: S-EPMC3421099 | biostudies-other | 2012 Aug
REPOSITORIES: biostudies-other
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Project description:BACKGROUND:Several scan parameters for PET imaging with 18F-PSMA-11 such as dosage, acquisition time and scan duration were evaluated to determine the most appropriate scan protocol, as well as the effect of furosemide administration on lesion visualization. Forty-four patients were randomly assigned to a dosage group (2.0?±?0.2 or 4.0?±?0.4?MBq/kg 18F-PSMA-11). All patients received a full-body PET/CT 1?h and 3?h after radiotracer injection with a scan duration of 3?min/bed position. For comparison of the scan duration, images were reconstructed for 1.5 and 3?min/bed position. Patients were intravenously administered 0.5?mg/kg furosemide with a maximum dose of 40?mg. To evaluate the furosemide effect, 22 additional patients were recruited and received one full-body PET/CT 1?h after administration of 2.0?±?0.2?MBq/kg 18F-PSMA-11 with a scan duration of 3?min/bed position. To this group, no furosemide was administered. Images were scored on image quality using a 7-point scale and each suspicious lesion was described. To assess interrater reliability, two nuclear physicians scored all scans independently and described all observed suspicious lesions. RESULTS:The 4?MBq/kg group received for all reconstructed images (60?min p.i., 1.5 and 3?min/bed position and 180?min p.i., 1.5 and 3?min/bed position) the highest median image quality score compared to the 2?MBq/kg group (p values <?0.01). When comparing all reconstructed images, the highest image quality score was given to images at 60?min p.i., 3?min/bed position for both dosage groups (score 5 and 6 for 2 and 4?MBq/kg, respectively). The addition of furosemide administration decreased the interference score with one point (p =?0.01106) and facilitated the evaluation of lesions in proximity to the ureters. The interrater reliability for the comparison of each lesion separately after more than 40 18F-PSMA-11 scan readings showed an increasing ? value from 0.78 (95% CI, 0.65-0.92) to 0.94 (95% CI, 0.87-1). CONCLUSION:Although the results indicate an administered activity of 4.0?±?0.4?MBq/kg, preference will be given to 2.0?± 0.2?MBq/kg due to the small difference in absolute score (max 1 point) and the ALARA principle. For evaluation of lesions in proximity to the ureters, the co-administration of a diuretic can be useful. The increase of the ? value from 0.78 to 0.94 suggests a learning curve in the interpretation of 18F-PSMA-11 images. TRIAL REGISTRATION:Clinicaltrials.gov, NCT03573011. Retrospectively registered 28 June 2018.
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Project description:Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation.MethodsThe conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored.ResultsThe estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method.ConclusionThe proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.
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Project description:PurposeRisk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning.MethodsFifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses.ResultsThe area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively.ConclusionOur results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.
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