Concordance in detecting amyloid positivity between 18F-florbetaben and 18F-flutemetamol amyloid PET using quantitative and qualitative assessments

Cho, Soo Hyun; Choe, Yeong Sim; Kim, Young Ju; Lee, Byungju; Kim, Hee Jin; Kim, Jun Pyo; Jung, Young Hee; Kim, Soo-Jong; Kim, Byeong C.; Farrar, Gill; Na, Duk L.; Moon, Seung Hwan; Seo, Sang Won

doi:10.1038/s41598-020-76102-5

Cited by 26 publications

(22 citation statements)

References 28 publications

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“…High agreement rates were found between FBB and FMM in visual assessment (94.4%) and SUVR cut-off categorization (98.1%). In addition, both FBB and FMM showed high agreement rates between visual assessment and SUVR cut-off categorization (93.5% in FBB and 91.6% in FMM) 40 . Fifth, selecting grey matter regions as ROI might have made the comparison between DTI features and others unfair.…”

Section: Discussionmentioning

confidence: 78%

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Kim

et al. 2021

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Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71–0.74, AUC for validation = 0.68–0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.

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Section: Discussionmentioning

confidence: 78%

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Kim

et al. 2021

Sci Rep

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“…Once all outliers are removed from the dataset, 2.5% is added to the SUVR of the highest remaining case, which results in a cutoff value [ 36 ]. As a result, the cortical SUVR cutoff values were 1.1 for FBB and 1.03 for FMM, respectively, when the whole cerebellum was used as a reference region [ 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

Performance of the plasma Aβ42/Aβ40 ratio, measured with a novel HPLC-MS/MS method, as a biomarker of amyloid PET status in a DPUK-KOREAN cohort

et al. 2021

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Background We assessed the feasibility of plasma Aβ42/Aβ40 determined using a novel liquid chromatography-mass spectrometry method (LC-MS) as a useful biomarker of PET status in a Korean cohort from the DPUK Study. Methods A total of 580 participants belonging to six groups, Alzheimer’s disease dementia (ADD, n = 134), amnestic mild cognitive impairment (aMCI, n = 212), old controls (OC, n = 149), young controls (YC, n = 15), subcortical vascular cognitive impairment (SVCI, n = 58), and cerebral amyloid angiopathy (CAA, n = 12), were included in this study. Plasma Aβ40 and Aβ42 were quantitated using a new antibody-free, LC-MS, which drastically reduced the sample preparation time and cost. We performed receiver operating characteristic (ROC) analysis to develop the cutoff of Aβ42/Aβ40 and investigated its performance predicting centiloid-based PET positivity (PET+). Results Plasma Aβ42/Aβ40 were lower for PET+ individuals in ADD, aMCI, OC, and SVCI (p < 0.001), but not in CAA (p = 0.133). In the group of YC, OC, aMCI, and ADD groups, plasma Aβ42/Aβ40 predicted PET+ with an area under the ROC curve (AUC) of 0.814 at a cutoff of 0.2576. When adding age, APOE4, and diagnosis, the AUC significantly improved to 0.912. Conclusion Plasma Aβ42/Aβ40, as measured by this novel LC-MS method, showed good discriminating performance based on PET positivity.

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“…However, this might be mitigated to some extent in that visual assessment by experienced doctors has shown high agreement with SUVR cut-off optimization method. 32 Regardless of the abovementioned limitations, the current study is noteworthy as this is the first study to demonstrate that increased 18 F-THK5351 uptake can predict cognitive decline among Aβ– aMCI patients.…”

Section: Discussionmentioning

confidence: 77%

¹⁸F-THK5351 PET Positivity and Longitudinal Changes in Cognitive Function in β-Amyloid-Negative Amnestic Mild Cognitive Impairment

et al. 2022

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Purpose Neuroinflammation is considered an important pathway associated with several diseases that result in cognitive decline. 18 F-THK5351 positron emission tomography (PET) signals might indicate the presence of neuroinflammation, as well as Alzheimer’s disease-type tau aggregates. β-amyloid (Aβ)-negative (Aβ–) amnestic mild cognitive impairment (aMCI) may be associated with non-Alzheimer’s disease pathophysiology. Accordingly, we investigated associations between 18 F-THK5351 PET positivity and cognitive decline among Aβ– aMCI patients. Materials and Methods The present study included 25 amyloid PET negative aMCI patients who underwent a minimum of two follow-up neuropsychological evaluations, including clinical dementia rating-sum of boxes (CDR-SOB). The patients were classified into two groups: 18 F-THK5351-positive and -negative groups. The present study used a linear mixed effects model to estimate the effects of 18 F-THK5351 PET positivity on cognitive prognosis among Aβ– aMCI patients. Results Among the 25 Aβ– aMCI patients, 10 (40.0%) were 18 F-THK5351 positive. The patients in the 18 F-THK5351-positive group were older than those in the 18 F-THK5351-negative group (77.4±2.2 years vs. 70.0±5.5 years; p <0.001). There was no difference between the two groups with regard to the proportion of apolipoprotein E ε4 carriers. Interestingly, however, the CDR-SOB scores of the 18 F-THK5351-positive group deteriorated at a faster rate than those of the 18 F-THK5351-negative group (B=0.003, p =0.033). Conclusion The results of the present study suggest that increased 18 F-THK5351 uptake might be a useful predictor of poor prognosis among Aβ– aMCI patients, which might be associated with increased neuroinflammation (ClinicalTrials.gov NCT02656498 ).

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Concordance in detecting amyloid positivity between 18F-florbetaben and 18F-flutemetamol amyloid PET using quantitative and qualitative assessments

Cited by 26 publications

References 28 publications

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Performance of the plasma Aβ42/Aβ40 ratio, measured with a novel HPLC-MS/MS method, as a biomarker of amyloid PET status in a DPUK-KOREAN cohort

¹⁸F-THK5351 PET Positivity and Longitudinal Changes in Cognitive Function in β-Amyloid-Negative Amnestic Mild Cognitive Impairment

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Concordance in detecting amyloid positivity between 18F-florbetaben and 18F-flutemetamol amyloid PET using quantitative and qualitative assessments

Cited by 26 publications

References 28 publications

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Performance of the plasma Aβ42/Aβ40 ratio, measured with a novel HPLC-MS/MS method, as a biomarker of amyloid PET status in a DPUK-KOREAN cohort

18F-THK5351 PET Positivity and Longitudinal Changes in Cognitive Function in β-Amyloid-Negative Amnestic Mild Cognitive Impairment

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¹⁸F-THK5351 PET Positivity and Longitudinal Changes in Cognitive Function in β-Amyloid-Negative Amnestic Mild Cognitive Impairment