Background/Aims: Disease-modifying therapy for Alzheimer’s disease (AD) has led to a need for biomarkers to identify prodromal AD and very early stage of AD dementia. We aimed to identify the cutoff values of cerebrospinal fluid (CSF) biomarkers for detecting prodromal AD. Methods: We assessed 56 patients with amnestic mild cognitive impairment (aMCI) who underwent lumbar puncture. Additionally, 87 healthy elderly individuals and 34 patients with AD dementia served as controls. Positron emission tomography was performed using florbetaben as a probe. We analyzed the concentration of Aβ_1–42, total tau protein (t-Tau), and tau protein phosphorylated at threonine 181 (p-Tau¹⁸¹) in CSF with INNOTEST enzyme-linked immunosorbent assay. Results: For the detection of prodromal AD in patients with aMCI, the cutoff values of CSF Aβ_1–42, t-Tau, and p-Tau¹⁸¹ were 749.5 pg/mL, 225.6 pg/mL, and 43.5 pg/mL, respectively. To discriminate prodromal AD in patients with aMCI, the t-Tau/Aβ_1–42 and ­p-Tau¹⁸¹/Aβ_1–42 ratios defined cutoff values at 0.298 and 0.059, respectively. Conclusions: CSF biomarkers are very useful tools for the differential diagnosis of prodromal AD in aMCI patients. The concentration of CSF biomarkers is well correlated with the stages of the AD spectrum.

Purpose: This study assesses the clinical performance of BTXBrain-Amyloid, an artificial intelligence-powered software for quantifying amyloid uptake in brain PET images. Methods: 150 amyloid brain PET images were visually assessed by experts and categorized as negative and positive. Standardized uptake value ratio (SUVR) was calculated with cerebellum grey matter as the reference region, and receiver operating characteristic (ROC) and precision-recall (PR) analysis for BTXBrain-Amyloid were conducted. For comparison, same image processing and analysis was performed using Statistical Parametric Mapping (SPM) program. In addition, to evaluate the spatial normalization (SN) performance, mutual information (MI) between MRI template and spatially normalized PET images was calculated and SPM group analysis was conducted. Results: Both BTXBrain and SPM methods discriminated between negative and positive groups. However, BTXBrain exhibited lower SUVR standard deviation (0.06 and 0.21 for negative and positive, respectively) than SPM method (0.11 and 0.25). In ROC analysis, BTXBrain had an AUC of 0.979, compared to 0.959 for SPM, while PR curves showed an AUC of 0.983 for BTXBrain and 0.949 for SPM. At the optimal cut-off, the sensitivity and specificity were 0.983 and 0.921 for BTXBrain and 0.917 and 0.921 for SPM12, respectively. MI evaluation also favored BTXBrain (0.848 vs. 0.823), indicating improved SN. In SPM group analysis, BTXBrain exhibited higher sensitivity in detecting basal ganglia differences between negative and positive groups. Conclusion: BTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences. These results suggest the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation.

A 59-year-old man underwent abdominal CT for evaluation of weight loss and generalized weakness. A large mass protruding from the lesser curvature of the stomach suggestive of gastrointestinal stromal tumor was demonstrated. F-FDG PET/CT showed hypermetabolic activity in the gastric mass. Mass excision and wedge resection were performed, and the diagnosis of inflammatory myofibroblastic tumor was confirmed.