| 1. |
- Altomare, Daniele, et al.
(author)
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Prognostic value of Alzheimer’s biomarkers in mild cognitive impairment : the effect of age at onset
- 2019
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In: Journal of Neurology. - : Springer Science and Business Media LLC. - 0340-5354 .- 1432-1459. ; 266:10, s. 2535-2545
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Journal article (peer-reviewed)abstract
- Objective: The aim of this study is to assess the impact of age at onset on the prognostic value of Alzheimer’s biomarkers in a large sample of patients with mild cognitive impairment (MCI). Methods: We measured Aβ42, t-tau, hippocampal volume on magnetic resonance imaging (MRI) and cortical metabolism on fluorodeoxyglucose–positron emission tomography (FDG-PET) in 188 MCI patients followed for at least 1 year. We categorised patients into earlier and later onset (EO/LO). Receiver operating characteristic curves and corresponding areas under the curve (AUCs) were performed to assess and compar the biomarker prognostic performances in EO and LO groups. Linear Model was adopted for estimating the time-to-progression in relation with earlier/later onset MCI groups and biomarkers. Results: In earlier onset patients, all the assessed biomarkers were able to predict cognitive decline (p < 0.05), with FDG-PET showing the best performance. In later onset patients, all biomarkers but t-tau predicted cognitive decline (p < 0.05). Moreover, FDG-PET alone in earlier onset patients showed a higher prognostic value than the one resulting from the combination of all the biomarkers in later onset patients (earlier onset AUC 0.935 vs later onset AUC 0.753, p < 0.001). Finally, FDG-PET showed a different prognostic value between earlier and later onset patients (p = 0.040) in time-to-progression allowing an estimate of the time free from disease. Discussion: FDG-PET may represent the most universal tool for the establishment of a prognosis in MCI patients and may be used for obtaining an onset-related estimate of the time free from disease.
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| 3. |
- Caroli, Anna, et al.
(author)
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Mild cognitive impairment with suspected nonamyloid pathology (SNAP) Prediction of progression
- 2015
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In: Neurology. - 0028-3878 .- 1526-632X. ; 84:5, s. 508-515
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Journal article (peer-reviewed)abstract
- Objectives:The aim of this study was to investigate predictors of progressive cognitive deterioration in patients with suspected non-Alzheimer disease pathology (SNAP) and mild cognitive impairment (MCI).Methods:We measured markers of amyloid pathology (CSF -amyloid 42) and neurodegeneration (hippocampal volume on MRI and cortical metabolism on [F-18]-fluorodeoxyglucose-PET) in 201 patients with MCI clinically followed for up to 6 years to detect progressive cognitive deterioration. We categorized patients with MCI as A+/A- and N+/N- based on presence/absence of amyloid pathology and neurodegeneration. SNAPs were A-N+ cases.Results:The proportion of progressors was 11% (8/41), 34% (14/41), 56% (19/34), and 71% (60/85) in A-N-, A+N-, SNAP, and A+N+, respectively; the proportion of APOE epsilon 4 carriers was 29%, 70%, 31%, and 71%, respectively, with the SNAP group featuring a significantly different proportion than both A+N- and A+N+ groups (p 0.005). Hypometabolism in SNAP patients was comparable to A+N+ patients (p = 0.154), while hippocampal atrophy was more severe in SNAP patients (p = 0.002). Compared with A-N-, SNAP and A+N+ patients had significant risk of progressive cognitive deterioration (hazard ratio = 2.7 and 3.8, p = 0.016 and p < 0.001), while A+N- patients did not (hazard ratio = 1.13, p = 0.771). In A+N- and A+N+ groups, none of the biomarkers predicted time to progression. In the SNAP group, lower time to progression was correlated with greater hypometabolism (r = 0.42, p = 0.073).Conclusions:Our findings support the notion that patients with SNAP MCI feature a specific risk progression profile.
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| 4. |
- de Boer, Anneloes, et al.
(author)
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Consensus-Based Technical Recommendations for Clinical Translation of Renal Phase Contrast MRI
- 2022
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In: Journal of Magnetic Resonance Imaging. - : John Wiley & Sons. - 1053-1807 .- 1522-2586. ; 55:2, s. 323-335
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Journal article (peer-reviewed)abstract
- BACKGROUND: Phase-contrast (PC) MRI is a feasible and valid noninvasive technique to measure renal artery blood flow, showing potential to support diagnosis and monitoring of renal diseases. However, the variability in measured renal blood flow values across studies is large, most likely due to differences in PC-MRI acquisition and processing. Standardized acquisition and processing protocols are therefore needed to minimize this variability and maximize the potential of renal PC-MRI as a clinically useful tool.PURPOSE: To build technical recommendations for the acquisition, processing, and analysis of renal 2D PC-MRI data in human subjects to promote standardization of renal blood flow measurements and facilitate the comparability of results across scanners and in multicenter clinical studies.STUDY TYPE: Systematic consensus process using a modified Delphi method.POPULATION: Not applicable.SEQUENCE FIELD/STRENGTH: Renal fast gradient echo-based 2D PC-MRI.ASSESSMENT: An international panel of 27 experts from Europe, the USA, Australia, and Japan with 6 (interquartile range 4-10) years of experience in 2D PC-MRI formulated consensus statements on renal 2D PC-MRI in two rounds of surveys. Starting from a recently published systematic review article, literature-based and data-driven statements regarding patient preparation, hardware, acquisition protocol, analysis steps, and data reporting were formulated.STATISTICAL TESTS: Consensus was defined as ≥75% unanimity in response, and a clear preference was defined as 60-74% agreement among the experts.RESULTS: Among 60 statements, 57 (95%) achieved consensus after the second-round survey, while the remaining three showed a clear preference. Consensus statements resulted in specific recommendations for subject preparation, 2D renal PC-MRI data acquisition, processing, and reporting.DATA CONCLUSION: These recommendations might promote a widespread adoption of renal PC-MRI, and may help foster the set-up of multicenter studies aimed at defining reference values and building larger and more definitive evidence, and will facilitate clinical translation of PC-MRI.LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.
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| 5. |
- DeSouza, Nandita M., et al.
(author)
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Standardised lesion segmentation for imaging biomarker quantitation : a consensus recommendation from ESR and EORTC
- 2022
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In: Insights into Imaging. - : Springer. - 1869-4101. ; 13:1
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Journal article (peer-reviewed)abstract
- Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with <= 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified.
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| 7. |
- Fournier, Laure, et al.
(author)
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Incorporating radiomics into clinical trials : expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers
- 2021
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In: European Radiology. - : SPRINGER. - 0938-7994 .- 1432-1084. ; 31:8, s. 6001-6012
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Journal article (peer-reviewed)abstract
- Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.
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| 8. |
- Prestia, Annapaola, et al.
(author)
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Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics
- 2015
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In: Alzheimer's & Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 11:10, s. 1191-1201
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Journal article (peer-reviewed)abstract
- Introduction: Proposed diagnostic criteria (international working group and National Institute on Aging and Alzheimer's Association) for Alzheimer's disease (AD) include markers of amyloidosis (abnormal cerebrospinal fluid [CSF] amyloid beta [A beta]42) and neurodegeneration (hippocampal atrophy, temporo-parietal hypometabolism on [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET), and abnormal CSF tau). We aim to compare the accuracy of these biomarkers, individually and in combination, in predicting AD among mild cognitive impairment (MCI) patients. Methods: In 73 MCI patients, followed to ascertain AD progression, markers were measured. Sensitivity and specificity, positive (LR+) and negative (LR-) likelihood ratios, and crude and adjusted hazard ratios were computed. Results: Twenty-nine MCI patients progressed and 44 remained stable. Positivity to any marker achieved the lowest LR- (0.0), whereas the combination A beta 42 plus FDG-PET achieved the highest LR+ (6.45). In a survival analysis, positivity to any marker was associated with 100% conversion rate, whereas negativity to all markers was associated with 100% stability. Discussion: The best criteria combined amyloidosis and neurodegeneration biomarkers, whereas the individual biomarker with the best performance was FDG-PET.
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| 9. |
- Prestia, Annapaola, et al.
(author)
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Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease
- 2013
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In: Neurology. - 0028-3878 .- 1526-632X. ; 80:11, s. 1048-1056
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Journal article (peer-reviewed)abstract
- Objectives: The current model of Alzheimer disease (AD) stipulates that brain amyloidosis biomarkers turn abnormal earliest, followed by cortical hypometabolism, and finally brain atrophy ones. The aim of this study is to provide clinical evidence of the model in patients with mild cognitive impairment (MCI). Methods: A total of 73 patients with MCI from 3 European memory clinics were included. Brain amyloidosis was assessed by CSF A beta 42 concentration, cortical metabolism by an index of temporoparietal hypometabolism on FDG-PET, and brain atrophy by automated hippocampal volume. Patients were divided into groups based on biomarker positivity: 1) A beta 422- FDG-PET- Hippo-, 2) A beta 42+ FDG-PET- Hippo-, 3) A beta 42+ FDG-PET + Hippo-, 4) A beta 42+ FDG-PET+ Hippo+, and 5) any other combination not in line with the model. Measures of validity were prevalence of group 5, increasing incidence of progression to dementia with increasing biological severity, and decreasing conversion time. Results: When patients with MCI underwent clinical follow-up, 29 progressed to dementia, while 44 remained stable. A total of 26% of patients were in group 5. Incident dementia was increasing with greater biological severity in groups 1 to 5 from 4% to 27%, 64%, and 100% (p for trend, 0.0001), and occurred increasingly earlier (p for trend = 0.024). Conclusions: The core biomarker pattern is in line with the current pathophysiologic model of AD. Fully normal and fully abnormal pattern is associated with exceptional and universal development of dementia. Cases not in line might be due to atypical neurobiology or inaccurate thresholds for biomarker (ab) normality.
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| 10. |
- Pruijm, Menno, et al.
(author)
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Renal blood oxygenation level-dependent magnetic resonance imaging to measure renal tissue oxygenation : a statement paper and systematic review
- 2018
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In: Nephrology, Dialysis and Transplantation. - : Oxford University Press. - 0931-0509 .- 1460-2385. ; 33, s. II22-II28
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Research review (peer-reviewed)abstract
- Tissue hypoxia plays a key role in the development and progression of many kidney diseases. Blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI) is the most promising imaging technique to monitor renal tissue oxygenation in humans. BOLD-MRI measures renal tissue deoxyhaemoglobin levels voxel by voxel. Increases in its outcome measure R2* (transverse relaxation rate expressed as per second) correspond to higher deoxyhaemoglobin concentrations and suggest lower oxygenation, whereas decreases in R2* indicate higher oxygenation. BOLD-MRI has been validated against micropuncture techniques in animals. Its reproducibility has been demonstrated in humans, provided that physiological and technical conditions are standardized. BOLD-MRI has shown that patients suffering from chronic kidney disease (CKD) or kidneys with severe renal artery stenosis have lower tissue oxygenation than controls. Additionally, CKD patients with the lowest cortical oxygenation have the worst renal outcome. Finally, BOLD-MRI has been used to assess the influence of drugs on renal tissue oxygenation, and may offer the possibility to identify drugs with nephroprotective or nephrotoxic effects at an early stage. Unfortunately, different methods are used to prepare patients, acquire MRI data and analyse the BOLD images. International efforts such as the European Cooperation in Science and Technology (COST) action 'Magnetic Resonance Imaging Biomarkers for Chronic Kidney Disease' (PARENCHIMA) are aiming to harmonize this process, to facilitate the introduction of this technique in clinical practice in the near future. This article represents an extensive overview of the studies performed in this field, summarizes the strengths and weaknesses of the technique, provides recommendations about patient preparation, image acquisition and analysis, and suggests clinical applications and future developments.
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