1,720,991 research outputs found
Education-Adjusted Normality Thresholds for FDG-PET in the Diagnosis of Alzheimer Disease
No full text<b><i>Background:</i></b> A corollary of the reserve hypothesis is that what is regarded as pathological cortical metabolism in patients might vary according to education. <b><i>Objective:</i></b> The aim of this study is to assess the incremental diagnostic value of education-adjusted over unadjusted thresholds on the diagnostic accuracy of FDG-PET as a biomarker for Alzheimer disease (AD). <b><i>Methods:</i></b> We compared cortical metabolism in 90 healthy controls and 181 AD patients from the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The AUC of the ROC curve did not differ significantly between the whole group and the higher-education patients or the lower-education subjects. <b><i>Results:</i></b> The threshold of wMetaROI values providing 80% sensitivity was lower in higher-education patients and higher in the lower-education patients, compared to the standard threshold derived over the whole AD collective, without, however, significant changes in sensitivity and specificity. <b><i>Conclusion:</i></b> These data show that education, as a proxy of reserve, is not a major confounder in the diagnostic accuracy of FDG-PET in AD and the adoption of education-adjusted thresholds is not required in daily practice.</jats:p
L'impact du phénomène de réserve cognitive dans le diagnostic de la maladie d'Alzheimer
No full textLe concept de réserve dans la maladie d'Alzheimer (MA) a été introduit suite à l'observation que certains facteurs, notamment l'éducation, peuvent moduler l'expression clinique des lésions neuropathologiques, grâce au développement de ressources et réseaux alternatifs, qui fournissent une capacité compensatoire. Les biomarqueurs d'imagerie moléculaire, parmi lesquels le PET au 18F-Fluorodesoxyglucose (FDG-PET), contribuent au diagnostic de la MA en permettant d'estimer in vivo la dysfonction neuronale associée aux lésions neuropathologiques. Pour une validation systématique des biomarqueurs dans la pratique clinique il est crucial de mesurer l'impact des différents facteurs modulateurs sur la performance diagnostique. Dans cette étude, nous avons pu démontrer que le niveau d'éducation des patients module la relation entre la dysfonction neuronale mesurée au FDG-PET et la sévérité clinique, en démontrant donc une réserve associée à l'éducation, toutefois sans diminuer la performance diagnostique du FDG-PET, qui reste excellente chez l'ensemble des patients
New Frontiers in Clinical PET imaging of Breast Cancer
No full textThe use of 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) has been a cornerstone in the metabolic imaging of breast cancer. However, its limitations in specific subtypes and clinical scenarios have driven the exploration of new frontiers in clinical PET imaging. This review highlights the role of emerging alternatives, such as PET [18F]16α-Fluoroestradiol ([18F]FES), Human Epidermal Growth Factor Receptor 2 (HER2)-targeted imaging, and Fibroblast Activation Protein (FAP) imaging. PET [18F]FES can play a pivotal role in the assessment of estrogen receptor (ER) expression. Similarly, HER2-targeted PET imaging facilitates non-invasive, whole-body assessment of HER2 expression. These tracers allow the detection of intra-patient, inter-lesional heterogeneity, synchronous and metachronous, offering valuable insights into equivocal lesions and also hold a predictive value, guiding treatment decisions and optimizing therapeutic strategies. PET FAP, which targets cancer-associated fibroblasts, provides unique information on tumor microenvironment, with potential clinical application for an improved staging and restaging in tumors with low [18F]FDG uptake, such as lobular breast cancer. Complementary to [18F]FDG PET, these tracers enable a more personalized approach to breast cancer management with a significant potential to advance precision oncology.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Explainable AI for automated respiratory misalignment detection in PET/CT imaging
Full text linkPurpose : Positron emission tomography (PET) image quality can be affected by artifacts emanating from PET, CT, or artifacts due to misalignment between PET and CT images. Automated detection of misalignment artifacts can be helpful both in data curation and in facilitating clinical workflow. This study aimed to develop an explainable machine learning approach to detect misalignment artifacts in PET/CT imaging.
Approach : This study included 1216 PET/CT images. All images were visualized and images with respiratory misalignment artifact (RMA) detected. Using previously trained models, four organs including the lungs, liver, spleen, and heart were delineated on PET and CT images separately. Data were randomly split into cross-validation (80%) and test set (20%), then two segmentations performed on PET and CT images were compared and the comparison metrics used as predictors for a random forest framework in a 10-fold scheme on cross-validation data. The trained models were tested on 20% test set data. The model’s performance was calculated in terms of specificity, sensitivity, F1-Score and area under the curve (AUC).
Main Results : Sensitivity, specificity, and AUC of 0.82, 0.85, and 0.91 were achieved in ten-fold data split. F1_score, sensitivity, specificity, and AUC of 84.5 vs 82.3, 83.9 vs 83.8, 87.7 vs 83.5, and 93.2 vs 90.1 were achieved for cross-validation vs test set, respectively. The liver and lung were the most important organs selected after feature selection.&#xD;Significance. We developed an automated pipeline to segment four organs from PET and CT images separately and used the match between these segmentations to decide about the presence of misalignment artifact. This methodology may follow the same logic as a reader detecting misalignment through comparing the contours of organs on PET and CT images. The proposed method can be used to clean large datasets or integrated into a clinical scanner to indicate artifactual cases.</p
Projection Space Implementation of Deep Learning-Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space
Full text linkOur purpose was to assess the performance of full-dose (FD) PET image synthesis in both image and sinogram space from low-dose (LD) PET images and sinograms without sacrificing diagnostic quality using deep learning techniques. Methods: Clinical brain PET/CT studies of 140 patients were retrospectively used for LD-to-FD PET conversion. Five percent of the events were randomly selected from the FD list-mode PET data to simulate a realistic LD acquisition. A modified 3-dimensional U-Net model was implemented to predict FD sinograms in the projection space (PSS) and FD images in image space (PIS) from their corresponding LD sinograms and images, respectively. The quality of the predicted PET images was assessed by 2 nuclear medicine specialists using a 5-point grading scheme. Quantitative analysis using established metrics including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), regionwise SUV bias, and first-, second- and high-order texture radiomic features in 83 brain regions for the test and evaluation datasets was also performed. Results: All PSS images were scored 4 or higher (good to excellent) by the nuclear medicine specialists. PSNR and SSIM values of 0.96 +/- 0.03 and 0.97 +/- 0.02, respectively, were obtained for PIS, and values of 31.70 +/- 0.75 and 37.30 +/- 0.71, respectively, were obtained for PSS. The average SUV bias calculated over all brain regions was 0.24% +/- 0.96% and 1.05% +/- 1.44% for PSS and PIS, respectively. The Bland-Altman plots reported the lowest SUV bias (0.02) and variance (95% confidence interval, -0.92 to 10.84) for PSS, compared with the reference FD images. The relative error of the homogeneity radiomic feature belonging to the gray-level cooccurrence matrix category was -1.07 +/- 1.77 and 0.28 +/- 1.4 for PIS and PSS, respectively. Conclusion: The qualitative assessment and quantitative analysis demonstrated that the FD PET PSS led to superior performance, resulting in higher image quality and lower SUV bias and variance than for FD PET PIS.</p
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Deep Learning–Powered CT-Less Multitracer Organ Segmentation From PET Images
Full text linkPurpose : The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task. Recent advances in CT-less PET imaging further highlight the necessity for an effective PET organ segmentation pipeline that does not rely on CT images. Therefore, the goal of this study was to develop a CT-less multitracer PET segmentation framework.
Patients and Methods : We collected 2062 PET/CT images from multiple scanners. The patients were injected with either 18 F-FDG (1487) or 68 Ga-PSMA (575). PET/CT images with any kind of mismatch between PET and CT images were detected through visual assessment and excluded from our study. Multiple organs were delineated on CT components using previously trained in-house developed nnU-Net models. The segmentation masks were resampled to coregistered PET images and used to train 4 different deep learning models using different images as input, including noncorrected PET (PET-NC) and attenuation and scatter-corrected PET (PET-ASC) for 18 F-FDG (tasks 1 and 2, respectively using 22 organs) and PET-NC and PET-ASC for 68 Ga tracers (tasks 3 and 4, respectively, using 15 organs). The models’ performance was evaluated in terms of Dice coefficient, Jaccard index, and segment volume difference.
Results : The average Dice coefficient over all organs was 0.81 ± 0.15, 0.82 ± 0.14, 0.77 ± 0.17, and 0.79 ± 0.16 for tasks 1, 2, 3, and 4, respectively. PET-ASC models outperformed PET-NC models ( P < 0.05) for most of organs. The highest Dice values were achieved for the brain (0.93 to 0.96 in all 4 tasks), whereas the lowest values were achieved for small organs, such as the adrenal glands. The trained models showed robust performance on dynamic noisy images as well.
Conclusions : Deep learning models allow high-performance multiorgan segmentation for 2 popular PET tracers without the use of CT information. These models may tackle the limitations of using CT segmentation in PET/CT image quantification, kinetic modeling, radiomics analysis, dosimetry, or any other tasks that require organ segmentation masks.</p
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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