Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease

Carli, Fabrizio De; Nobili, Flavio; Pagani, Marco; Bauckneht, Matteo; Massa, Federico; Grazzini, Matteo; Jonsson, Cathrine; Peira, Enrico; Morbelli, Silvia; Arnaldi, Dario

doi:10.1007/s00259-018-4197-7

Cited by 21 publications

(28 citation statements)

References 43 publications

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“…For VROI-1 and VROI-2, we obtained cut-off values of 1.175 and 1.165, respectively. As for the VROI-SVM model, it was derived in these groups of pAD patients and controls, which is an obvious limitation, but a crossvalidation has been applied through the leave-one-out approach to mitigate overfitting and, more important, the VROI-SVM has now been validated in an independent and larger population, showing similar accuracy between AD patients and controls [60]. The next step requires a software implementation of the model with relevant thresholds for the application in research or clinical setting.…”

Section: Discussionmentioning

confidence: 99%

“…A detailed analysis of these factors was not included in this study considering the relatively limited size of sub-samples. As discussed in [60], accurate standardization of data acquisition and preprocessing is a prerequisite for the reliable application of diagnostic tools; however, a certain variability among recording centers may remain due to different equipment and specific characteristics of local population. For this reason, the training of multivariate models on extensive multicentric datasets is essential for the extraction of the main common features characterizing the pathological process favoring generalization of results and then supporting clinical diagnosis.…”

Section: Discussionmentioning

confidence: 99%

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Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer’s Disease1

Brugnolo

Carli

Pagani

et al. 2019

JAD

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Background: Several automatic tools have been implemented for semi-quantitative assessment of brain [ 18 ]F-FDG-PET. Objective: We aimed to head-to-head compare the diagnostic performance among three statistical parametric mapping (SPM)-based approaches, another voxel-based tool (i.e., PALZ), and a volumetric region of interest (VROI-SVM)-based approach, in distinguishing patients with prodromal Alzheimer's disease (pAD) from controls. Methods: Sixty-two pAD patients (MMSE score = 27.0 ± 1.6) and one hundred-nine healthy subjects (CTR) (MMSE score = 29.2 ± 1.2) were enrolled in five centers of the European Alzheimer's Disease Consortium. The three SPM-based methods, based on different rationales, included 1) a cluster identified through the correlation analysis between [ 18 ]F-FDG-PET and a verbal memory test (VROI-1), 2) a VROI derived from the comparison between pAD and CTR (VROI-2), and 3) visual analysis of individual maps obtained by the comparison between each subject and CTR (SPM-Maps). The VROI-SVM approach was based on 6 VROI plus 6 VROI asymmetry values derived from the pAD versus CTR comparison thanks to support vector machine (SVM). Results: The areas under the ROC curves between pAD and CTR were 0.84 for VROI-1, 0.83 for VROI-2, 0.79 for SPM maps, 0.87 for PALZ, and 0.95 for VROI-SVM. Pairwise comparisons of Youden index did not show statistically significant differences in diagnostic performance between VROI-1, VROI-2, SPM-Maps, and PALZ score whereas VROI-SVM performed significantly (p < 0.005) better than any of the other methods. Conclusion: The study confirms the good accuracy of [ 18 ]F-FDG-PET in discriminating healthy subjects from pAD and highlights that a non-linear, automatic VROI classifier based on SVM performs better than the voxel-based methods.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer’s Disease1

Brugnolo

Carli

Pagani

et al. 2019

JAD

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“…However, the sensitivity of FDG-PET index in our analysis only reaches about 72.82% in patients with pMCI, which is a rather low figure as compared to more sound volume of interest tools. More sensitive alternative choices tracking FDG-PET hypometabolism such as the Support Vector Machine model-based analysis need to be considered in the future studies [14, 15]. Similarly, reduced FDG-PET accounted for a relatively large proportion of AD dementia (34.31% in N− group and 50.98% in N+ group).…”

Section: Discussionmentioning

confidence: 99%

FDG-PET as an independent biomarker for Alzheimer’s biological diagnosis: a longitudinal study

et al. 2019

Alz Res Therapy

108

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Background Reduced 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) brain metabolism was recognized as a biomarker of neurodegeneration in the recently proposed ATN framework for Alzheimer’s disease (AD) biological definition. However, accumulating evidence suggested it is an independent biomarker, which is denoted as “F” in the very study. Methods A total of 551 A+T+ individuals from the Alzheimer’s Disease Neuroimaging Initiative database were recruited and then further divided into four groups based on the biomarker positivity as 132 A+T+N−F−, 102 A+T+N−F+, 113 A+T+N+F−, and 204 A+T+N+F+. Frequency distributions of the groups were compared, as well as the clinical progression [measured by the longitudinal changes in cognition and brain structure, and mild cognitive impairment (MCI) to AD dementia conversion] between every pair of F+ and F− groups. Results The prevalence of A+T+N+F+ profile was 66.24% in clinically diagnosed AD dementia patients; similarly, the majority of individuals with reduced FDG-PET were AD dementia subjects. Among the 551 individuals that included, 537 had at least one follow-up (varied from 1 to 8 years). Individuals in F+ groups performed worse and dropped faster in Mini-Mental State Examination scale and had faster shrinking middle temporal lobe than those in F− groups (all p < 0.05). Moreover, in MCI patients, reduced FDG-PET exerted 2.47 to 4.08-fold risk of AD dementia progression compared with those without significantly impaired FDG-PET (both p < 0.001). Conclusions Based on the analyses, separating FDG-PET from “N” biomarker to build the ATN(F) system is necessary and well-founded. The analysis from this study could be a complement to the original ATN framework for AD’s biological definition. Electronic supplementary material The online version of this article (10.1186/s13195-019-0512-1) contains supplementary material, which is available to authorized users.

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“…The MRI dataset of the ADNI consisted of 260 patient cases (130 AD cases and 130 CN cases). Prior to this processing of this dataset, the approach of Carli et al [79] was used to determine AD. The characteristics of AD, including voxel cluster and voxel volume, were retrieved and utilised in this study.…”

Section: A Adnimentioning

confidence: 99%

Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review

2020

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The accurate diagnosis of Alzheimer's disease (AD) plays an important role in patient treatment, especially at the disease's early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. Although many recent studies have used computers to diagnose AD, most machine detection methods are limited by congenital observations. AD can be diagnosed-but not predicted-at its early stages, as prediction is only applicable before the disease manifests itself. Deep Learning (DL) has become a common technique for the early diagnosis of AD. Here, we briefly review some of the important literature on AD and explore how DL can help researchers diagnose the disease at its early stages. INDEX TERMS Alzheimer's disease, deep learning, early stage detection and diagnosis.

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Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease

Cited by 21 publications

References 43 publications

Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer’s Disease1

Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer’s Disease1

FDG-PET as an independent biomarker for Alzheimer’s biological diagnosis: a longitudinal study

Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review

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