Prevalencia y concordancia entre diagnóstico clínico y anatomopatológico de demencia en una clínica psicogeriátrica

Leiros, Beatriz Grandal; Méndez, Lina I. Pérez; Zelaya, María Victoria; Eguinoa, Laura Moreno; García‐Bragado, F.; Álvarez, T. Tuñón; Larreta, J.J. Roldán

doi:10.1016/j.nrl.2016.04.011

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Although this may have influenced our results, it is practically difficult to perform DAT SPECT and pathological examination at the same time. When autopsy is used as a reference standard, diagnostic discrepancies between DAT SPECT and autopsy findings can be noted, as the diagnosis can change with disease progression [33]. Third, we did not assess the effect of reconstruction parameters and scanner characterization.…”

Section: Discussionmentioning

confidence: 99%

Impact of a combination of quantitative indices representing uptake intensity, shape, and asymmetry in DAT SPECT using machine learning: comparison of different volume of interest settings

et al. 2019

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BackgroundWe sought to assess the machine learning-based combined diagnostic accuracy of three types of quantitative indices obtained using dopamine transporter single-photon emission computed tomography (DAT SPECT)—specific binding ratio (SBR), putamen-to-caudate ratio (PCR)/fractal dimension (FD), and asymmetry index (AI)—for parkinsonian syndrome (PS). We also aimed to compare the effect of two different types of volume of interest (VOI) settings from commercially available software packages DaTQUANT (Q) and DaTView (V) on diagnostic accuracy.MethodsSeventy-one patients with PS and 40 without PS (NPS) were enrolled. Using SPECT images obtained from these patients, three quantitative indices were calculated at two different VOI settings each. SBR-Q, PCR-Q, and AI-Q were derived using the VOI settings from DaTQUANT, whereas SBR-V, FD-V, and AI-V were derived using those from DaTView. We compared the diagnostic value of these six indices for PS. We incorporated a support vector machine (SVM) classifier for assessing the combined accuracy of the three indices (SVM-Q: combination of SBR-Q, PCR-Q, and AI-Q; SVM-V: combination of SBR-V, FD-V, and AI-V). A Mann-Whitney U test and receiver-operating characteristics (ROC) analysis were used for statistical analyses.ResultsROC analyses demonstrated that the areas under the curve (AUC) for SBR-Q, PCR-Q, AI-Q, SBR-V, FD-V, and AI-V were 0.978, 0.837, 0.802, 0.906, 0.972, and 0.829, respectively. On comparing the corresponding quantitative indices between the two types of VOI settings, SBR-Q performed better than SBR-V (p = 0.006), whereas FD-V performed better than PCR-Q (p = 0.0003). No significant difference was observed between AI-Q and AI-V (p = 0.56). The AUCs for SVM-Q and SVM-V were 0.988 and 0.994, respectively; the two different VOI settings displayed no significant differences in terms of diagnostic accuracy (p = 0.48).ConclusionThe combination of the three indices obtained using the SVM classifier improved the diagnostic performance for PS; this performance did not differ based on the VOI settings and software used.

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Section: Discussionmentioning

confidence: 99%

Impact of a combination of quantitative indices representing uptake intensity, shape, and asymmetry in DAT SPECT using machine learning: comparison of different volume of interest settings

et al. 2019

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“…First, the diagnoses of PS and NPS were based on clinical diagnoses; thus, we did not use a pathological diagnosis. Although this may have influenced our results, it is difficult to perform DAT SPECT and pathological examination at the same time, and the pathological diagnosis can change with disease progression [18]. It should be also noted that DAT SPECT results are included as indicative biomarkers in the diagnostic criteria of DLB.…”

Section: Discussionmentioning

confidence: 99%

Impact of the cerebrospinal fluid-mask algorithm on the diagnostic performance of 123I-Ioflupane SPECT: an investigation of parkinsonian syndromes

et al. 2019

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Background A cerebrospinal fluid (CSF)-mask algorithm has been developed to reduce the adverse influence of CSF-low-counts on the diagnostic utility of the specific binding ratio (SBR) index calculated with Southampton method. We assessed the effect of the CSF-mask algorithm on the diagnostic performance of the SBR index for parkinsonian syndromes (PS), including Parkinson’s disease, and the influence of cerebral ventricle dilatation on the CSF-mask algorithm. Methods We enrolled 163 and 158 patients with and without PS, respectively. Both the conventional SBR (non-CSF-mask) and SBR corrected with the CSF-mask algorithm (CSF-mask) were calculated from 123 I-Ioflupane single-photon emission computed tomography (SPECT) images of these patients. We compared the diagnostic performance of the corresponding indices and evaluated whether the effect of the CSF-mask algorithm varied according to the extent of ventricle dilatation, as assessed with the Evans index (EI). A receiver-operating characteristics (ROC) analysis was used for statistical analyses. Results ROC analyses demonstrated that the CSF-mask algorithm performed better than the non-CSF-mask (no correction, area under the curve [AUC] = 0.917 [95% confidence interval (CI) 0.887–0.947] vs. 0.895 [95% CI 0.861–0.929], p < 0.001; attenuation correction, AUC = 0.930 [95% CI 0.902–0.957] vs. 0.903 [95% CI 0.870–0.936], p < 0.001). When not corrected for attenuation, no significant difference in the AUC was observed in the low EI group between the non-CSF-mask and CSF-mask algorithms (0.927 [95% CI 0.877–0.978] vs. 0.942 [95% CI 0.898–0.986], p = 0.11); in the middle and high EI groups, the CSF-mask algorithm performed better than the non-CSF-mask algorithm (middle EI group, AUC = 0.894 [95% CI 0.825–0.963] vs. 0.872 [95% CI 0.798–0.947], p < 0.05; high EI group, AUC = 0.931 [95% CI 0.883–0.978] vs. 0.900 [95% CI 0.840–0.961], p < 0.01). When corrected for attenuation, significant differences in the AUC were observed in all three EI groups (low EI group, AUC = 0.961 [95% CI 0.924–0.998] vs. 0.942 [95% CI 0.895–0.988], p < 0.05; middle EI group, AUC = 0.905 [95% CI 0.843–0.968] vs. 0.872 [95% CI 0.800–0.944], p < 0.005; high EI group, AUC = 0.954 [95% CI 0.917–0.991] vs. 0.917 [95% CI 0.862–0.973], p < 0.005). Conclusion The CSF-mask algorithm improved the performance of the SBR index in informing the diagnosis of PS, especially in cases with ventricle dilatation.

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Use of Modern Classification Systems for the Complex Diagnostics of Alzheimer’s Disease

Zorkina,

Morozova,

Abramova

et al. 2024

Neurosci Behav Physi

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Prevalencia y concordancia entre diagnóstico clínico y anatomopatológico de demencia en una clínica psicogeriátrica

Abstract: Almost a third of our patients were not correctly diagnosed in vivo. The most common mistake was the underdiagnosis of cerebrovascular pathology.

Cited by 8 publications

References 12 publications

Impact of a combination of quantitative indices representing uptake intensity, shape, and asymmetry in DAT SPECT using machine learning: comparison of different volume of interest settings

Impact of a combination of quantitative indices representing uptake intensity, shape, and asymmetry in DAT SPECT using machine learning: comparison of different volume of interest settings

Impact of the cerebrospinal fluid-mask algorithm on the diagnostic performance of 123I-Ioflupane SPECT: an investigation of parkinsonian syndromes

Use of Modern Classification Systems for the Complex Diagnostics of Alzheimer’s Disease

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