Predicting long-term clinical stability in amyloid-positive subjects by FDG-PET.
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ABSTRACT:
SUBMITTER: Iaccarino L
PROVIDER: S-EPMC6562030 | biostudies-literature | 2019 Jun
REPOSITORIES: biostudies-literature
Publications
Predicting long-term clinical stability in amyloid-positive subjects by FDG-PET.
Iaccarino Leonardo L Sala Arianna A Perani Daniela D
Annals of clinical and translational neurology 20190524 6
Imaging biomarkers can be used to screen participants for Alzheimer's disease clinical trials. To test the predictive values in clinical progression of neuropathology change (amyloid-PET) or brain metabolism as neurodegeneration biomarker ([18F]FDG-PET), we evaluated data from <i>N</i> = 268 healthy controls and <i>N</i> = 519 mild cognitive impairment subjects. Despite being a significant risk factor, amyloid positivity was not associated with clinical progression in the majority (≥60%) of subj ...[more]
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Project description:BackgroundCognitive impairment is a common symptom of Parkinson's disease (PD) that increases in risk and severity as the disease progresses. An accurate prediction of the risk of progression from the mild cognitive impairment (MCI) stage to the dementia (PDD) stage is an unmet clinical need.MethodsWe investigated the use of a supervised learning algorithm called the support vector machine (SVM) to retrospectively stratify patients on the basis of brain fluorodeoxyglucose-PET (FDG-PET) scans. Of 43 patients with PD-MCI according to the baseline scan, 23 progressed to PDD within a 5-year period, whereas 20 maintained stable MCI. The baseline scans were used to train a model, which separated patients identified as PDD converters versus those with stable MCI with 95% sensitivity and 91% specificity.ResultsIn an independent validation data set of 19 patients, the AUC was 0.73, with 67% sensitivity and 80% specificity. The SVM model was topographically characterized by hypometabolism in the temporal and parietal lobes and hypermetabolism in the anterior cingulum and putamen and the insular, mesiotemporal, and postcentral gyri. The performance of the SVM model was further tested on 2 additional data sets, which confirmed that the model was also sensitive to later-stage PDD (17 of 19 patients; 89% sensitivity) and dementia with Lewy bodies (DLB) (16 of 17 patients; 94% sensitivity), but not to normal cognition PD (2 of 17 patients). Finally, anti-PD medication status did not change the SVM classification of the other set of 10 patients with PD who were scanned twice, ON and OFF medication.ConclusionsThese results potentially indicate that the proposed FDG-PET-based SVM classifier has utility for providing an accurate prognosis of dementia development in patients with PD-MCI.
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Project description:PurposeWe aimed to investigate the role of dual-phase FDG PET/CT in predicting the prognosis of patients with operable breast cancer.MethodsWe retrospectively reviewed the data of 998 patients who underwent radical treatment for breast cancer. Before treatment, PET/CT scans were performed 1 and 2 h after FDG administration. The maximum standardized uptake value (SUVmax) at both time points (SUVmax1 and SUVmax2) in the primary tumor and the retention index (RI) were calculated. PET recurrence risk (PET-RR) was determined based on the SUVmax1 and RI, and disease-free survival (DFS) and overall survival (OS) were evaluated according to the metabolic parameters. Propensity score matching was performed to adjust for biological characteristics.ResultsThe cut-off values for SUVmax1 and RI were 3 and 5%, respectively. The 5-year DFS was 94.9% and 86.1% (P < 0.001), and the 5-year OS was 97.6% and 92.7% (P < 0.001) in the low and high PET-RR groups, respectively. In multivariate analysis, high T status, nodal metastasis, the triple-negative subtype, and high PET-RR were independent factors of poor DFS. Propensity score matching revealed similar findings (5-year DFS 91.8% vs. 88.6%, P = 0.041 and 5-year OS 97.1% vs. 94.2%, P = 0.240, respectively).ConclusionThe combined parameters of SUVmax1 and RI on dual-phase FDG PET/CT were useful for predicting prognosis of patients with breast cancer. Patients with a high SUVmax1 and a negative time course of FDG uptake had a favorable prognosis.
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