Tau aggregates represent a key pathologic feature of Alzheimer’s disease and other neurodegenerative diseases. Recently, PET probes have been developed for in vivo detection of tau accumulation; however, they are limited because of off-target binding and a reduced ability to detect tau in non-Alzheimer’s disease tauopathies. The novel tau PET tracer, [18F]PI-2620, has a high binding affinity and specificity for aggregated tau; therefore, it was hypothesized to have desirable properties for the visualization of tau accumulation in Alzheimer’s disease and non-Alzheimer’s disease tauopathies. To assess the ability of [18F]PI-2620 to detect regional tau burden in non-Alzheimer’s disease tauopathies compared with Alzheimer’s disease, patients with progressive supranuclear palsy (n = 3), corticobasal syndrome (n = 2), corticobasal degeneration (n = 1), or Alzheimer’s disease (n = 8), and healthy controls (n = 7) were recruited. All participants underwent MRI, amyloid β assessment, and [18F]PI-2620 PET (Image acquisition at 60–90 minutes post-injection). Cortical and subcortical tau accumulations were assessed by calculating standardized uptake value ratios using [18F]PI-2620 PET. For pathologic validation, tau pathology was assessed using tau immunohistochemistry and compared with [18F]PI-2620 retention in an autopsied case of corticobasal degeneration. In Alzheimer’s disease, focal retention of [18F]PI-2620 was evident in the temporal and parietal lobes, precuneus, and cingulate cortex. Standardized uptake value ratio analyses revealed that patients with non-Alzheimer’s disease tauopathies had elevated [18F]PI-2620 uptake only in the globus pallidus, as compared to patients with Alzheimer’s disease, but not healthy controls. A head-to-head comparison of [18F]PI-2620 and [18F]PM-PBB3, another tau PET probe for possibly visualizing the 4-repeat tau pathogenesis in non-Alzheimer’s disease, revealed different retention patterns in one subject with progressive supranuclear palsy. Imaging-pathology correlation analysis of the autopsied patient with corticobasal degeneration revealed no significant correlation between [18F]PI-2620 retention in vivo. High [18F]PI-2620 uptake at 60–90 minutes post-injection in the globus pallidus may be a sign of neurodegeneration in four-repeat tauopathy, but not necessarily practical for diagnosis of non- Alzheimer’s disease tauopathies. Collectively, this tracer is a promising tool to detect Alzheimer’s disease-tau aggregation. However, late acquisition PET images of [18F]PI-2620 may have limited utility for reliable detection of four-repeat tauopathy because of lack of correlation between postmortem tau pathology and different retention pattern than the non-Alzheimer’s disease-detectable tau radiotracer, [18F]PM-PBB3. A recent study reported that [18F]PI-2620 tracer kinetics curves in four-repeat tauopathies peak earlier (within 30 minutes) than Alzheimer’s disease; therefore, further studies are needed to determine appropriate PET acquisition times that depend on the respective interest regions and diseases.
Background The Response Evaluation Criteria in Solid Tumors (RECIST) for computed tomography (CT) is preoperatively used to evaluate therapeutic effects. However, it does not reflect the pathological treatment response (PTR) of pancreatic ductal adenocarcinoma (PDAC). The Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) for positron emission tomography (PET)/CT is effective in other cancers. This study aimed to confirm the usefulness of PERCIST and the prognostic utility of PET/CT for PDAC. Methods Forty‐two consecutive patients with PDAC who underwent neoadjuvant therapy (NAT) and pancreatectomy at our institution between 2014 and 2018 were retrospectively analyzed. We evaluated the treatment response and prognostic significance of PET/CT parameters and other clinicopathological factors. Results Twenty‐two patients who underwent PET/CT both before and after NAT with the same protocol were included. RECIST revealed stable disease and partial response in 20 and 2 cases, respectively. PERCIST revealed stable metabolic disease, partial metabolic response, and complete metabolic response in 8, 9, and 5 cases, respectively. The PTR was G3, G2, and G1 in 8, 12, and 2 cases, respectively. For comparing the concordance rates between PTR and each parameter, PERCIST (72.7% [16/22]) was significantly superior to RECIST (36.4% [8/22]) (P = .017). The area under the curve survival values of PET/CT parameters were 0.777 for metabolic tumor volume (MTV), 0.500 for maximum standardized uptake value, 0.554 for peak standardized uptake value corrected for lean body mass, and 0.634 for total lesion glycolysis. A 50% cut‐off value for the MTV reduction rate yielded the largest difference in survival between responders and nonresponders. On multivariate analysis, MTV reduction rates < 50% were independent predictors for relapse‐free survival (hazard ratio [HR], 3.92; P = .044) and overall survival (HR, 14.08; P = .023). Conclusions PERCIST was more accurate in determining NAT’s therapeutic effects for PDAC than RECIST. MTV reduction rates were independent prognostic factors for PDAC.
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.
Purpose We aimed to evaluate the diagnostic performances of quantitative indices obtained from dopamine transporter (DAT) single-photon emission computed tomography (SPECT) and 123I-metaiodobenzylguanidine (MIBG) scintigraphy for Parkinsonian syndromes (PS) using the classification and regression tree (CART) analysis. Methods We retrospectively enrolled 216 patients with or without PS, including 80 without PS (NPS) and 136 with PS [90 Parkinson’s disease (PD), 21 dementia with Lewy bodies (DLB), 16 progressive supranuclear palsy (PSP), and 9 multiple system atrophy (MSA). The striatal binding ratio (SBR), putamen-to-caudate ratio (PCR), and asymmetry index (AI) were calculated using DAT SPECT. The heart-to-mediastinum uptake ratio (H/M) based on the early (H/M [Early]) and delayed (H/M [Delay]) images and cardiac washout rate (WR) were calculated from MIBG scintigraphy. The CART analysis was used to establish a diagnostic decision tree model for differentiating PS based on these quantitative indices. Results The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 87.5, 96.3, 93.3, 92.9, and 93.1 for NPS; 91.1, 78.6, 75.2, 92.5, and 83.8 for PD; 57.1, 95.9, 60.0, 95.4, and 92.1 for DLB; and 50.0, 98.0, 66.7, 96.1, and 94.4 for PSP, respectively. The PCR, WR, H/M (Delay), and SBR indices played important roles in the optimal decision tree model, and their feature importance was 0.61, 0.22, 0.11, and 0.05, respectively. Conclusion The quantitative indices showed high diagnostic performances in differentiating NPS, PD, DLB, and PSP, but not MSA. Our findings provide useful guidance on how to apply these quantitative indices in clinical practice.
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