1,720,994 research outputs found
Is there a role for PET imaging in the early evaluation of prostate cancer relapse?
No full textThe patient population with a rising prostate specific antigen (PSA) post-therapy with no evidence of disease on standard imaging studies currently represents the second largest group of prostate cancer patients. Little information is still available regarding the specificity and sensitivity of positron emission tomography (PET) tracers in the assessment of early biochemical recurrence. Ideally, PET imaging would allow one to accurately discriminate between local vs nodal vs distant relapse, thus enabling appropriate selection of patients for salvage local therapy. The vast majority of studies show a relatively poor yield of positive scans with PSA values < 4 ng ml(-1). So far, no tracer has been shown to be able to detect local recurrence within the clinically useful 1 ng ml(-1) PSA threshold, clearly limiting the use of PET imaging in the post-surgical setting. Preliminary evidence, however, suggests that 11C-choline PET may be useful in selecting out patients with early biochemical relapse (PSA < 2 ng ml(-1)) who have pelvic nodal oligometastasis potentially amenable to local treatment. The role of PET imaging in prostate cancer is gradually evolving but still remains within the experimental realm. Well-conducted studies comparing the merits of different tracers are needed
FDG-CT/PET false positive case in hip prosthesis: a clue to avoid error
No full textA 62 years old woman 6 months after left total hip prosthesis referred to our institution for persistent pain and warm, stiff, and swollen joint. 18F-FDG CT/PET Images showed an intense focal uptake corresponding to the external margin of inter-trochanteric region of prosthesis and inside the stem inferiorly, but common decision was to reconstruct PET images without attenuation correction and now showed a complete and unexpected disappearance of focal and pathological FDG uptake. This case shows the potential propagation of CT artifacts into PET emission data close to metal implants and should be taken in account together to SUV values
PET/CT in thyroid cancer - The importance of BRAF mutations
No full textThyroid cancer (TC) represents less than 1% of all newly diagnosed malignancies. In some selected cases, with a high clinical suspicion for disease but negative I-131 scan, positron emission tomography/computed tomography (PET) with F-18-Fluorodeoxyglucose (FDG) could be helpful in the detection of disease and the definition of its extent. FDG PET/CT, better if performed after TSH stimulation analogously to patient preparation done for radioiodine scintigraphy, could be useful mainly in the detection of metastatic and recurrent disease since the uptake and diagnostic sensitivity of FDG are increased by TSH stimulation. Recently, the role of oncogenic mutations in the tumorigenesis of TCs has become clearer. Among such mutations, BRAFV600E represents the most common genetic alteration. Mutated BRAF may define a more aggressive papillary carcinoma with poorer prognosis and therefore its analysis has been extensively studied as a rule-in test for thyroid carcinoma. In this paper, we try to outline the possible role of FDG PET/CT in the management of patients with TC and positive BRAF mutations and the impact that it could have on their therapeutic algorithm, in terms of thyroidectomy and radioactive iodine (RAI) therapy
Correction to: Repeated amino acid PET imaging for longitudinal monitoring of brain tumors (Clinical and Translational Imaging, (2022), 10, 5, (457-465), 10.1007/s40336-022-00504-w)
No full textThe article “Repeated amino acid PET imaging for longitudinalmonitoring of brain tumors”, written by Francesco Cicone, et al., was originally published Online First without Open Access. After publication in volume 10, issue 5, page 457–465 the author decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the copyright of the article has been changed to © Author(s) 2022 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit. The original article has been corrected
AutoSPET: An SPM plugin to automatize neuroimages PET analysis
No full textNeuroimaging analysis supports clinicians in the diagnosis of neurological diseases by extracting information from digital images. Due to the large number of images generated by new devices (e.g. PET ones), there is a lot of effort in defining computer-based tools to analyze and classify (brain) radiological images. Statistical tools, such as SPM (for Statistical Parametric Mapping), are largely used by physicians for image analysis. Nevertheless, large datasets analysis requires repetitive steps, due to the lack of automatic procedures. E.g. SPM requires human intervention during long and complex steps.
We here present a tool, called AutoSPET (for Automatic SPM analysis for PET images), which allows to perform SPM analyses on large sets of PET images. It works as a meta-component orchestrating interactions with SPM, Matlab and with SPM plugins via a unified user interface. AutoSPET has been tested with real clinical datasets and it is publicly available as an official SPM plugin on the SPM website
Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for nonsmall cell lung cancer (NSCLC)
No full textMethodologies for the analysis and classification of PET neuroimages
No full textNeuroimaging analysis aims to support clinicians in the diagnosis of neurological diseases by using radiological images. Positron emission tomography (PET) is a nuclear medicine imaging technique used to produce three-dimensional images of the human brain for neurological studies. Due to the large number of generated images, there is a lot of effort in defining computer based tools to analyze and classify brain images. Such analyses are used to identify cerebral regions of interest (ROI) related to specific neurodegenerative diseases. Statistical tools, such as SPM (for Statistical Parametric Mapping) and its MarsBar plugin, are largely used by physicians for ROIs identification and for image analysis. Nevertheless, large datasets analysis (e.g. studying pathologies for many patients and for large sets of PET images) requires repetitive SPM procedures for each patient’s image, mainly due to the lack of (i) automatic procedures for analysing set of patients, and (ii) validation of using SPM versus patient magnetic resonance as reference brain templates. Finally, SPM analysis requires human intervention, and there is no automatic system guiding physicians for pathologies identification. As a contribution for the latter issue, we defined an automatic classification tool using topological relations among ROIs to support physicians while studying a new patient. Starting from a set of known pathologies associated to medical annotated PET images (i.e. associated to neurological pathologies), we used SPM and MarsBaR tools to define a reference PET images dataset; ROIs extracted from input PET images have been compared with known dataset and classified, suggesting physicians with (a subset of) pathologies associated to those PET images. Experiments showed that the classifier performs well. Moreover, in order to improve the repeatability of experiments with large datasets, we use an SPM plugin called AutoSPET, which allows to perform SPM analysis on a large PET images dataset, using different SPM plugins within a unified user interface, and allowing to simply run statistical analyses. AutoSPET is available on our server and also as an SPM plugin on the SPM website. Finally we report experiments to validate the use of the standard T1 SPM template versus the magnetic resonance ones
18F-PSMA-1007 salivary gland dosimetry: comparison between different methods for dose calculation and assessment of inter- and intra-patient variability
No full textObjective. Simplified calculation approaches and geometries are usually adopted for salivary glands (SGs) dosimetry. Our aims were (i) to compare different dosimetry methods to calculate SGs absorbed doses (ADs) following [18F]-PSMA-1007 injection, and (ii) to assess the AD variation across patients and single SG components. Approach. Five patients with prostate cancer underwent sequential positron-emission tomography/computed tomography (PET/CT) acquisitions of the head and neck, 0.5, 2 and 4 h after [18F]-PSMA-1007 injection. Parotid and submandibular glands were segmented on CT to derive SGs volumes and masses, while PET images were used to derive Time-Integrated Activity Coefficients. Average ADs to single SG components or total SG (tSG) were calculated with the following methods: (i) direct Monte Carlo simulation with GATE/GEANT4 considering radioactivity in the entire PET/CT field-of-view (MC) or in the SGs only (MCsgo); (ii) spherical model (SM) of OLINDA/EXM 2.1, adopting either patient-specific or standard ICRP89 organ masses (SMstd); (iii) ellipsoidal model (EM); (iv) MIRD approach with organ S-factors from OLINDA/EXM 2.1 and OpenDose collaboration, with or without contribution from cross irradiation originating outside the SGs. The maximum percent AD difference across SG components (δ max) and across patients (Δmax) were calculated. Main results. Compared to MC, ADs to single SG components were significantly underestimated by all methods (average relative differences ranging between −11.9% and −30.5%). δ max values were never below 25%. The highest δ max (=702%) was obtained with SMstd. Concerning tSG, results within 10% of the MC were obtained only if cross-irradiation from the remainder of the body or from the remainder of the head was accounted for. The Δmax ranged between 58% and 78% across patients. Significance. Simple geometrical models for SG dosimetry considerably underestimated ADs compared to MC, particularly if neglecting cross-irradiation from neighboring regions. Specific masses of single SG components should always be considered given their large intra- and inter-patient variability
A Methodology to Measure Glucose Metabolism by Quantitative Analysis of PET Images
No full textPositron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) tracer is the standard clinical technique to measure myocardial and vessel metabolism and viability and to investigate the metabolic syndrome associated with cardiovascular diseases. The quantitative analysis of PET images allows one to study the cardiovascular physiological processes, by extracting quantitative parameters from the analysis of the tracer kinetic. Here, we propose a new methodology to quantify and evaluate the evolution of glucose metabolism inside the myocardium and the large vascular structures over time. We merge and analyze PET and CT cardiac images, extracting different volumes of interest (VOI) and performing quantitative measurements. To validate it, we apply the methodology to merge images of the aorta vessel for patients affected by metabolic syndrome. The application of the proposed approach to the use case reveals a correlation between administered drugs and metabolic syndrome, measuring the glucose metabolic rate (MRGlu) in both the myocardium and aorta. The proposed methodology can be used to evaluate some cardiovascular risk indexes of diabetic patients, too. The proposed methodology can also be deployed to analyze other application domains
Repeated amino acid PET imaging for longitudinal monitoring of brain tumors
No full textPurpose: Amino acid PET is a useful complement to MRI in a number of clinical settings for the evaluation of brain tumors. However, amino acid PET is rarely used repeatedly over the course of the disease. We reviewed the existing literature on the use of repeated amino acid PET imaging for monitoring primary or secondary brain tumors. Methods: A comprehensive literature search of articles describing the use of longitudinal amino acid PET imaging of brain tumors was performed on PubMed/MEDLINE using multiple search terms. Additional literature was retrieved from the reference lists of identified studies or based on the authors’ personal knowledge and experience. Results: With regard to primary tumors, two main clinical settings were identified in whom the performance of repeated amino acid PET imaging was most commonly assessed. These include the detection of malignant progression of patients with grade II or III glioma characterized according to older WHO classifications, and the early response assessment of various treatment options in glioma patients. For patients with brain metastases, only a few studies were identified using longitudinal amino acid PET for the diagnosis of post-treatment changes after stereotactic radiosurgery. The analyzed studies reported that longitudinal amino acid PET imaging frequently anticipate or even outperform the diagnostic performance provided by conventional MRI in these settings. Conclusions: The available literature suggests that conventional MRI should be accompanied by longitudinal amino acid PET monitoring in these clinical settings. Nevertheless, more reliable evidence derived from larger, prospective multicenter studies is warranted
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