Fluorodeoxyglucose positron emission tomography of mild cognitive impairment with clinical follow-up at 3 years

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

INTRODUCTION

Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.

METHODS

We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.

RESULTS

A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.

DISCUSSION

The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.

HIGHLIGHTS

There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Brain 18 F-FDG-PET and an optimized cingulate island ratio to differentiate Lewy body dementia and Alzheimer's disease

BACKGROUND AND PURPOSE

The diagnosis of Dementia with Lewy Bodies (DLB) is challenging due to various clinical presentations and clinical and neuropathological features that overlap with Alzheimer's disease (AD). The use of ¹⁸ F-Fluorodeoxyglucose-PET (¹⁸ F-FDG-PET) can be limited due to similar patterns in DLB and AD. However, metabolism in the posterior cingulate cortex is known to be relatively preserved in DLB and visual assessment of the "cingulate island sign" became a helpful tool in the analysis of 18F-FDG-PET. The aim of this study was the evaluation of visual and semiquantitative 18F-FDG-PET analyses in the diagnosis of DLB and the differentiation to AD as well as its relation to other dementia biomarkers.

METHODS

This retrospective study comprises 81 patients with a clinical diagnosis of DLB or AD that underwent ¹⁸ F-FDG-PET/CT. PET scans were analyzed visually and semiquantitatively and results were compared to clinical data, cerebrospinal fluid results, dopamine transporter scintigraphy, and 18F-Florbetaben-PET. Furthermore, different cingulate island ratios were calculated to analyze their diagnostic accuracy.

RESULTS

Visual assessment of 18F-FDG-PET showed an accuracy of 62%-77% in differentiating between DLB and AD. Standard uptake values were significantly lower in the primary visual cortex and the lateral occipital cortex of DLB patients compared to AD patients. The cingulate island ratio was significantly higher in the DLB group compared to the AD group and the ratio posterior cingulate cortex to visual cortex plus lateral occipital cortex showed the highest diagnostic accuracy to discriminate between DLB and AD at 81%.

CONCLUSIONS

Semiquantitative 18F-FDG-PET imaging and especially the use of an optimized cingulate island ratio are valuable tools to differentiate between DLB and AD.

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients' cognitive function and early intervention is helpful to improve patient's quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer's disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM. Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM). Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index. Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers. Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.

F-18 ML-104 tau PET imaging in mild cognitive impairment

OBJECTIVE

This study was undertaken to evaluate the tau distribution patterns in patients with amnestic mild cognitive impairment (aMCI) using PET radiotracer F-18 ML-104.

MATERIALS AND METHODS

Thirty patients, clinically diagnosed as aMCI [mini mental state evaluation ≥24] in the neurology or geriatric memory clinics, were included in the study. Each aMCI patient underwent F-18 fluorodeoxyglucose and F-18 ML-104 tau PET. Standardized uptake value ratios for cortical gray matter regions were evaluated for F-18 ML-104 tau PET and compared with normal controls and with early Alzheimer's disease (AD) patients (used from a previous study).

RESULTS

aMCI revealed significantly higher standardized uptake value ratios in both medial temporal cortices, precuneus and posterior cingulate cortices in comparison to normal controls and a significantly lesser binding in bilateral medial and lateral temporal, precuneus and posterior cingulate cortices in comparison to early AD. A negative correlation was noted between F-18 fluorodeoxyglucose uptake and F-18 ML-104 retention in the precuneus and posterior cingulate cortices in aMCI, while F-18 ML-104 retention and mini mental state evaluation scores revealed a moderate negative correlation in the posterior cingulate cortices.

CONCLUSION

We could demonstrate a significant increase in cortical tau deposition in aMCI patients in comparison to normal controls, thus providing in vivo evidence of the underlying pathological process in this subgroup of patients with high probability of conversion to AD.

A preliminary study of resting brain metabolism in treatment-resistant depression before and after treatment with olanzapine-fluoxetine combination

Treatment-resistant depression (TRD) occurs in many patients and causes high morbidity and mortality. Because TRD subjects are particularly difficult to study especially longitudinally, biological data remain very limited. In a preliminary study to judge feasibility and power, 25 TRD patients were referred from specialty psychiatric practices. All were severely and chronically depressed and mostly had comorbid psychiatric disorders as is typical in TRD. Nine patients were able to complete all required components of the protocol that included diagnostic interview; rating scales; clinical magnetic resonance imaging; medication washout; treatment with maximally tolerated olanzapine-fluoxetine combination for 8 weeks; and pre- and post-treatment fluorodeoxyglucose positron emission tomography. This drug combination is an accepted standard of treatment for TRD. Dropouts arose from worsening depression, insomnia, and anxiety. One patient remitted; three responded. A priori regions of interest included the amygdala and subgenual cingulate cortex (sgACC; Brodmann area BA25). Responders showed decreased metabolism with treatment in the right amygdala that correlated with clinical response; no significant changes in BA25; better response to treatment the higher the baseline BA25 metabolism; and decreased right ventromedial prefrontal metabolism (VMPFC; broader than BA25) with treatment which did not correlate with depression scores. The baseline metabolism of all individuals showed heterogeneous patterns when compared to a normative metabolic database. Although preliminary given the sample size, this study highlights several issues important for future work: marked dropout rate in this study design; need for large sample size for adequate power; baseline metabolic heterogeneity of TRD requiring careful subject characterization for future studies of interventions; relationship of amygdala activity decreases with response; and the relationship between baseline sgACC and VMPFC activity with response. Successful treatment of TRD with olanzapine-fluoxetine combination shows changes in cerebral metabolism like those seen in treatment-responsive major depression.

18F-FDG PET for Prediction of Conversion to Alzheimer's Disease Dementia in People with Mild Cognitive Impairment: An Updated Systematic Review of Test Accuracy

Background:

A previous Cochrane systematic review concluded there is insufficient evidence to support the routine use of ¹⁸F-FDG PET in clinical practice in people with mild cognitive impairment (MCI).

Objectives:

To update the evidence and reassess the accuracy of ¹⁸F-FDG-PET for detecting people with MCI at baseline who would clinically convert to Alzheimer’s disease (AD) dementia at follow-up.

Methods:

A systematic review including comprehensive search of electronic databases from January 2013 to July 2017, to update original searches (1999 to 2013). All key review steps, including quality assessment using QUADAS 2, were performed independently and blindly by two review authors. Meta-analysis could not be conducted due to heterogeneity across studies.

Results:

When all included studies were examined across all semi-quantitative and quantitative metrics, exploratory analysis for conversion of MCI to AD dementia (n = 24) showed highly variable accuracy; half the studies failed to meet four or more of the seven sets of QUADAS 2 criteria. Variable accuracy for all metrics was also found across eleven newly included studies published in the last 5 years (range: sensitivity 56–100%, specificity 24–100%). The most consistently high sensitivity and specificity values (approximately ≥80%) were reported for the sc-SPM (single case statistical parametric mapping) metric in 6 out of 8 studies.

Conclusion:

Systematic and comprehensive assessment of studies of ¹⁸FDG-PET for prediction of conversion from MCI to AD dementia reveals many studies have methodological limitations according to Cochrane diagnostic test accuracy gold standards, and shows accuracy remains highly variable, including in the most recent studies. There is some evidence, however, of higher and more consistent accuracy in studies using computer aided metrics, such as sc-SPM, in specialized clinical settings. Robust, methodologically sound prospective longitudinal cohort studies with long (≥5 years) follow-up, larger consecutive samples, and defined baseline threshold(s) are needed to test these promising results. Further evidence of the clinical validity and utility of ¹⁸F-FDG PET in people with MCI is needed.

Imaging in prodromal dementia with Lewy bodies: Where do we stand?

OBJECTIVES

The aim of this review was to provide an overview of the literature on imaging in prodromal dementia with Lewy bodies (DLB).

DESIGN

Systematic PubMed search and literature review.

RESULTS

Diagnostic classification of the prodromal DLB stage remains to be established but is likely to require imaging biomarkers to improve diagnostic accuracy. In subjects with mild cognitive impairment with Lewy body disease (MCI-LB) (here synonymous with prodromal DLB) and REM sleep behaviour disorder, a high risk condition for future conversion to a synucleinopathy, imaging modalities have assessed early structural brain changes, striatal dopaminergic integrity, metabolic brain, and cerebral perfusion alterations. It remains uncertain whether structural brain imaging can differentiate MCI-LB from mild cognitive impairment with Alzheimer disease (MCI-AD), but early right anterior insula thinning has been reported to occur in MCI-LB compared with MCI-AD. Dopaminergic deficits have been observed in a substantial proportion of MCI-LB subjects and have a high specificity for Lewy body disease at the pre-dementia stage. Cardiac sympathetic denervation, occipital hypometabolism, or hypoperfusion is less studied as this pre-dementia stage and it remains to be determined whether any imaging abnormalities antedate DLB.

CONCLUSION

Imaging studies in prodromal DLB are still in their infancy but offer great potential to study early in vivo structural and functional biological alterations. Future work should focus on longitudinal multimodal imaging studies with postmortem validation of diagnosis in order to develop and then validate criteria for prodromal DLB.

Early differential diagnosis between Alzheimer's disease and dementia with Lewy bodies: Comparison between (18)F-FDG PET and (123)I-IMP SPECT

Both (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and (123)I-iodoamphetamine (IMP) single-photon emission computed tomography (SPECT) have been used for the differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). Less information is available, however, regarding the differential diagnosis of mild cognitive impairment (MCI) due to AD and MCI due to DLB. We examined nine AD patients (AD group), nine DLB patients (DLB group), eight MCI due to AD patients (MCI-AD group), and nine MCI due to DLB patients (MCI-DLB group) with FDG PET and IMP SPECT using a well-characterized normal database and a stereotactic extraction estimation method. In the AD and DLB groups, receiver operating characteristic (ROC) analysis in the occipital regions showed significant accuracy of both FDG PET and IMP SPECT for the differential diagnosis. In the MCI-AD and MCI-DLB groups, ROC analysis showed significant accuracy of only FDG PET for the differential diagnosis. Both FDG PET and IMP SPECT would be useful for the differential diagnosis between AD and DLB. For the differential diagnosis of MCI-AD versus MCI-DLB, FDG PET would be more useful than IMP SPECT.

¹⁸F-FDG PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

¹⁸F-FDFG uptake by brain tissue as measured by positron emission tomography (PET) is a well-established method for assessment of brain function in people with dementia. Certain findings on brain PET scans can potentially predict the decline of mild cognitive Impairment (MCI) to Alzheimer's disease dementia or other dementias.

OBJECTIVES

To determine the diagnostic accuracy of the ¹⁸F-FDG PET index test for detecting people with MCI at baseline who would clinically convert to Alzheimer's disease dementia or other forms of dementia at follow-up.

SEARCH METHODS

We searched the Cochrane Register of Diagnostic Test Accuracy Studies, MEDLINE, EMBASE, Science Citation Index, PsycINFO, BIOSIS previews, LILACS, MEDION, (Meta-analyses van Diagnostisch Onderzoek), DARE (Database of Abstracts of Reviews of Effects), HTA (Health Technology Assessment Database), ARIF (Aggressive Research Intelligence Facility) and C-EBLM (International Federation of Clinical Chemistry and Laboratory Medicine Committee for Evidence-based Laboratory Medicine) databases to January 2013. We checked the reference lists of any relevant studies and systematic reviews for additional studies.

SELECTION CRITERIA

We included studies that evaluated the diagnostic accuracy of ¹⁸F-FDG PET to determine the conversion from MCI to Alzheimer's disease dementia or to other forms of dementia, i.e. any or all of vascular dementia, dementia with Lewy bodies, and fronto-temporal dementia. These studies necessarily employ delayed verification of conversion to dementia and are sometimes labelled as 'delayed verification cross-sectional studies'.

DATA COLLECTION AND ANALYSIS

Two blinded review authors independently extracted data, resolving disagreement by discussion, with the option to involve a third review author as arbiter if necessary. We extracted and summarised graphically the data for two-by-two tables. We conducted exploratory analyses by plotting estimates of sensitivity and specificity from each study on forest plots and in receiver operating characteristic (ROC) space. When studies had mixed thresholds, we derived estimates of sensitivity and likelihood ratios at fixed values (lower quartile, median and upper quartile) of specificity from the hierarchical summary ROC (HSROC) models.

MAIN RESULTS

We included 14 studies (421 participants) in the analysis. The sensitivities for conversion from MCI to Alzheimer's disease dementia were between 25% and 100% while the specificities were between 15% and 100%. From the summary ROC curve we fitted we estimated that the sensitivity was 76% (95% confidence interval (CI): 53.8 to 89.7) at the included study median specificity of 82%. This equates to a positive likelihood ratio of 4.03 (95% CI: 2.97 to 5.47), and a negative likelihood ratio of 0.34 (95% CI: 0.15 to 0.75). Three studies recruited participants from the same Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort but only the largest ADNI study (Herholz 2011) is included in the meta-analysis. In order to demonstrate whether the choice of ADNI study or discriminating brain region (Chételat 2003) or reader assessment (Pardo 2010) make a difference to the pooled estimate, we performed five additional analyses. At the median specificity of 82%, the estimated sensitivity was between 74% and 76%. There was no impact on our findings. In addition to evaluating Alzheimer's disease dementia, five studies evaluated the accuracy of ¹⁸F-FDG PET for all types of dementia. The sensitivities were between 46% and 95% while the specificities were between 29% and 100%; however, we did not conduct a meta-analysis because of too few studies, and those studies which we had found recruited small numbers of participants. Our findings are based on studies with poor reporting, and the majority of included studies had an unclear risk of bias, mainly for the reference standard and participant selection domains. According to the assessment of Index test domain, more than 50% of studies were of poor methodological quality.

AUTHORS' CONCLUSIONS

It is difficult to determine to what extent the findings from the meta-analysis can be applied to clinical practice. Given the considerable variability of specificity values and lack of defined thresholds for determination of test positivity in the included studies, the current evidence does not support the routine use of ¹⁸F-FDG PET scans in clinical practice in people with MCI. The ¹⁸F-FDG PET scan is a high-cost investigation, and it is therefore important to clearly demonstrate its accuracy and to standardise the process of ¹⁸F-FDG PET diagnostic modality prior to its being widely used. Future studies with more uniform approaches to thresholds, analysis and study conduct may provide a more homogeneous estimate than the one available from the included studies we have identified.

Prodromal dementia with Lewy bodies

BACKGROUND

The clinical condition of dementia is now recognized as a diagnosis that can only be applied too late in the disease process to be useful for therapeutic approaches centring on disease modification. As a result, in recent years increasing attention has been given to mild cognitive impairment (MCI) and the diagnosis of prodromal dementia. This paper reviews the evidence for the clinical presentation of prodromal dementia with Lewy bodies (DLB).

METHOD

A Medline search was carried out to identify articles with original data on the prodromal presentation of DLB.

RESULTS

In MCI cohorts that progress to dementia, the proportion diagnosed with DLB is similar to that reported in dementia cohorts. Prodromal DLB may present as any MCI subtype, although visuospatial and executive domains may be most commonly affected. Rapid eye movement (REM) sleep behaviour disorder (RBD), autonomic symptoms, hyposmia, hallucinations and motor symptoms seem to be more common in prodromal DLB than in prodromal Alzheimer's disease (AD). Some of these symptoms can precede the diagnosis of DLB by several years. There has been little research into the use of biomarkers in prodromal DLB, although in RBD cohorts, clinical and imaging biomarkers have been associated with the development of DLB.

CONCLUSIONS

The evidence available suggests that prodromal DLB may be differentiated from other dementia prodromes in most cases. Further research is needed to confirm this, and to assess the utility of biomarkers such as 123I-FP-CIT and 123I-MIBG imaging.

The clinical characteristics of dementia with Lewy bodies and a consideration of prodromal diagnosis

Dementia with Lewy bodies (DLB) is the second most common type of degenerative dementia following Alzheimer’s disease (AD). DLB is clinically and pathologically related to Parkinson's disease (PD) and PD dementia, and the three disorders can be viewed as existing on a spectrum of Lewy body disease. In recent years there has been a concerted effort to establish the phenotypes of AD and PD in the prodromal phase (before the respective syndromes of cognitive and motor impairment are expressed). Evidence for the prodromal presentation of DLB is also emerging. This paper briefly reviews what is known about the clinical presentation of prodromal DLB before discussing the pathology of Lewy body disease and how this relates to potential biomarkers of prodromal DLB. The presenting features of DLB can be broadly placed in three categories: cognitive impairment (particularly nonamnestic cognitive impairments), behavioural/psychiatric phenomena (for example, hallucinations, rapid eye movement sleep behaviour disorder (RBD)) and physical symptoms (for example, parkinsonism, decreased sense of smell, autonomic dysfunction). Some noncognitive symptoms such as constipation, RBD, hyposmia and postural dizziness can predate the onset of memory impairment by several years in DLB. Pathological studies of Lewy body disease have found that the earliest sites of involvement are the olfactory bulb, the dorsal motor nucleus of the vagal nerve, the peripheral autonomic nervous system, including the enteric nervous system, and the brainstem. Some of the most promising early markers for DLB include the presence of RBD, autonomic dysfunction or hyposmia, ¹²³I-metaiodobenzylguanidine cardiac scintigraphy, measures of substantia nigra pathology and skin biopsy for α-synuclein in peripheral autonomic nerves. In the absence of disease-modifying therapies, the diagnosis of prodromal DLB is of limited use in the clinic. That said, knowledge of the prodromal development of DLB could help clinicians identify cases of DLB where the diagnosis is uncertain. Prodromal diagnosis is of great importance in research, where identifying Lewy body disease at an earlier stage may allow researchers to investigate the initial phases of dementia pathophysiology, develop treatments designed to interrupt the development of the dementia syndrome and accurately identify the patients most likely to benefit from these treatments.