One-Year Longitudinal Changes in Tau Accumulation on [18F]PI-2620 PET in the Alzheimer Spectrum.

We investigated the longitudinal changes in cortical tau accumulation and their association with cognitive decline in patients in the Alzheimer disease (AD) continuum using 2-(2-([18F]fluoro)pyridin-4-yl)-9H-pyrrolo[2,3-b:4,5c']dipyridine ([18F]PI-2620) PET. Methods: We prospectively enrolled 52 participants (age, 69.7 ± 8.4 y; 18 men and 34 women): 7 with normal cognition, 28 with mild cognitive impairment, and 17 with AD. They all completed the [18F]PI-2620 and [18F]florbetaben PET, MRI, and neuropsychologic tests at baseline and, excepting the [18F]florbetaben PET, at the 1-y follow-up. Amyloid-ß (Aß) PET images were visually scored as positive (+) or negative (-). Patients on the AD continuum, including Aß+ mild cognitive impairment and AD, were classified into early-onset (EO+) (<65 y old) or late-onset (LO+) (≥65 y old) groups. [18F]PI-2620 PET SUV ratios (SUVRs) were determined by calculating the cerebral-to-inferior cerebellar ratio. Cortical volumes were calculated using 3-dimensional T1-weighted MRI. The correlation between tau accumulation progression and cognitive decline was also investigated. Results: The global [18F]PI-2620 PET SUVRs were 1.04 ± 0.07 in 15 Aß- patients, 1.18 ± 0.21 in 20 LO+ patients (age, 76.7 ± 3.8 y), and 1.54 ± 0.38 in 17 EO+ patients (age, 63.4 ± 5.4 y; P < 0.001) at baseline. The global SUVR increased over 1 y by 0.05 ± 0.07 (3.90%) and 0.13 ± 0.22 (8.41%) in the LO+ and EO+ groups, respectively, whereas in the Aß- groups, it remained unchanged. The EO+ group showed higher global and regional tau deposition than did the Aß- and LO+ groups (P < 0.05 for each) and rapid accumulation in Braak stage V (0.15 ± 0.25; 9.10% ± 12.27%; P = 0.016 and 0.008), Braak stage VI (0.08 ± 0.12; 7.16% ± 10.06%; P < 0.006 and 0.005), and global SUVR (P = 0.013) compared with the Aß- group. In the EO+ group, the changes in SUVR in Braak stages II-VI were strongly correlated with the baseline and changes in verbal memory (P < 0.03). The LO+ group showed higher tau accumulation in Braak stage I-IV areas than did the Aß- group (P < 0.001 for each). In the LO+ group, the change in SUVR in Braak stages III and IV moderately correlated with the change in attention (P < 0.05), and the change in SUVR in Braak stages V and VI moderately correlated with the change in visuospatial function (P < 0.005). Conclusion: These findings suggest that [18F]PI-2620 PET can be a biomarker to provide regional and chronologic information about tau pathology in the AD continuum.

[18F]fluorodeoxyglucose positron emission tomography/computed tomography characteristics of primary mediastinal germ cell tumors.

Primary mediastinal germ cell tumor (MGCT) is an uncommon tumor. Although it has histology similar to that of gonadal germ cell tumor (GCT), the prognosis for MGCT is generally worse than that for gonadal GCT. We performed visual assessment and quantitative analysis of [18F]fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) for MGCTs. A total of 35 MGCT patients (age = 33.1 ± 16.8 years, F:M = 16:19) who underwent preoperative PET/CT were retrospectively reviewed. The pathologic diagnosis of MGCTs identified 24 mature teratomas, 4 seminomas, 5 yolk sac tumors, and 2 mixed germ cell tumors. Visual assessment was performed by categorizing the uptake intensity, distribution, and contour of primary MGCTs. Quantitative parameters including the maximum standardized uptake value (SUVmax), tumor-to-background ratio (TBR), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and maximum diameter were compared between benign and malignant MGCTs. On visual assessment, the uptake intensity was the only significant parameter for differentiating between benign and malignant MGCTs (p = 0.040). In quantitative analysis, the SUVmax (p < 0.001), TBR (p < 0.001), MTV (p = 0.033), and TLG (p < 0.001) showed significantly higher values for malignant MGCTs compared with benign MGCTs. In receiver operating characteristic (ROC) curve analysis of these quantitative parameters, the SUVmax had the highest area under the curve (AUC) (AUC = 0.947, p < 0.001). Furthermore, the SUVmax could differentiate between seminomas and nonseminomatous germ cell tumors (p = 0.042) and reflect serum alpha fetoprotein (AFP) levels (p = 0.012). The visual uptake intensity and SUVmax on [18F]FDG PET/CT showed discriminative ability for benign and malignant MGCTs. Moreover, the SUVmax may associate with AFP levels.

Performance of deep learning models for response evaluation on whole-body bone scans in prostate cancer.

OBJECTIVE: We aimed to develop deep learning classifiers for assessing therapeutic response on bone scans of patients with prostate cancer. METHODS: A set of 3791 consecutive bone scans coupled with their last previous scan (1528 patients) was evaluated. Bone scans were labeled as "progression" or "nonprogression" on the basis of clinical reports and image review. A 2D-convolutional neural network architecture was trained with three different preprocessing methods: 1) no preprocessing (Raw), 2) spatial normalization (SN), and 3) spatial and count normalization (SCN). Data were allocated into training, validation, and test sets in the ratio of 72:8:20, with the 20% independent test set rotating all scans over a five-fold testing procedure. A Grad-CAM algorithm was employed to generate class activation maps to visualize the lesions contributing to the decision. Diagnostic performance was compared using area under the receiver operating characteristics curves (AUCs). RESULTS: The data consisted of 791 scans labeled as "progression" and 3000 scans labeled as "nonprogression." The AUCs of the classifiers were 0.632-0.710 on the Raw dataset, were significantly higher with the use of SN at 0.784-0.854 (p < 0.001 for Raw versus SN), and higher still with SCN at 0.954-0.979 (p < 0.001 for SN versus SCN). Class activation maps of the SCN model visualized lesions contributing to the model's decision of progression. CONCLUSION: With preprocessing of spatial and count normalization, our deep learning model achieved excellent performance in classifying the therapeutic response of bone scans in patients with prostate cancer.

Scaling problem in Parkinson's disease patients with pain.

INTRODUCTION: Although pain is common in Parkinson's disease (PD), the underlying mechanism remains unknown. Scaling function and dopaminergic hypofunction may contribute to pain development because increased pain sensitivity is observed in PD and is normalized after levodopa administration. We aimed to determine whether spatial discrimination (SD) and striatal dopaminergic activity (DA) differed between PD patients with and without pain. METHODS: We divided 90 patients with drug-naïve PD into two groups based on the presence or absence of pain and compared the SD threshold (SDT). We evaluated the correlation of the SDT with pain severity in PD with pain. We also compared the DA of 48 patients and analyzed the correlation with pain severity in PD patients with pain. RESULTS: The SDTs did not differ between the two groups, but unmeasurable SDT was more frequent in PD with pain. There was a positive correlation of pain severity with the SDT of the more affected hand but no correlation with the SDT of the less affected hand. The DA did not differ between the groups. There was a negative trend of pain severity with the DA of the ventral striatum (VS) but no correlation with the other striatal subregions. CONCLUSIONS: Pain in PD may be associated with scaling dysfunction in the sensory system. The abnormal scaling function would render the PD patient hypersensitive to even mild pain. The dopamine in the VS appears to be associated with pain severity; however, the relationship of striatal dopaminergic deficits with pain occurrence requires further investigation.

Surface Functionalization and Reversible Disassembly of DNA-Assembly Nanoparticles for Sensitive and Multiplexed Detection of DNA Targets Without Enzymatic and Catalytic Amplification.

Recently, DNA-assembly nanoparticles based on DNA-metal ion interactions are emerging as new building blocks for drug delivery and metal nanostructure synthesis. However, the surface modification of DNA-assembly nanoparticles using functional biomolecules that can identify specific targets has rarely been explored. In this study, we developed a new immobilization chemical strategy to efficiently functionalize the barcode DNA-assembly nanoparticles (bcDNA NPs) with thiolated probe DNA (pDNA) for synthesizing pDNA-functionalized bcDNA NPs (pDNA-bcDNA NPs). We used them as nanoprobes to successfully demonstrate the sensitive and selective detection of multiple DNA targets. Importantly, Au ions played an essential role as anchoring sites via their conjugation with both thiolated pDNA and bcDNA NPs. In addition, we could reversibly and rapidly disassemble the pDNA-bcDNA NPs into the initial bcDNA strands with a recovery rate of 91%; this process significantly amplified the signal by releasing a million bcDNA strands, which enabled DNA quantification from a single pDNA-bcDNA NP. The Au3+ concentration, pH, and surface passivation conditions were carefully investigated to maximize the pDNA loading to 8500 strands/bcDNA NP. The limit of detection was determined to be 221 fM, which is the most sensitive among the absorbance-based methods without polymerase chain reaction, hybridization chain reactions, catalytic hairpin assembly, and other reactions involving enzymes and catalysts. The reversible disassembly of DNA strands and Au ion-mediated conjugation chemistry could be extended for the detection of other types of targets, such as proteins, metal ions, and small molecules, using other organic functionalities that are or can be thiolated, including polypeptides, aptamers, and antibodies.

Retinal Thickness and Its Interocular Asymmetry Between Parkinson's Disease and Drug-Induced Parkinsonism.

BACKGROUND: Drug-induced parkinsonism (DIP) is common, but diagnosis is challenging. Although dopamine transporter imaging is useful, the cost and inconvenience are problematic, and an easily accessible screening technique is needed. We aimed to determine whether optical coherence tomography (OCT) findings could differentiate DIP from Parkinson's disease (PD). METHODS: We investigated 97 de novo PD patients and 27 DIP patients using OCT and [18F] N-(3-fluoropropyl)-2b-carbon ethoxy-3b-(4-iodophenyl) nortropane (FP-CIT) positron emission tomography. We compared peripapillary retinal nerve fiber layer thickness (pRNFLT) and macular retinal thickness (mRT) between PD and DIP patients as well as interocular differences in the pRNFLT and the mRT. Asymmetric index (%) for retinal thickness (AIRT) was calculated to measure the interocular differences between pRNFLT and mRT. The correlation between AIRT and total striatal specific/non-specific binding ratio asymmetry index (SNBRAI) was investigated in PD and DIP patients. RESULTS: No significant differences in pRNFLT and mRT values were observed between PD and DIP patients (all P values > 0.090). The mean SNBRAI was significantly higher in PD than in DIP (P = 0.008) patients; however, AIRT did not differ between PD and DIP patients in pRNFLT and mRT (all P values > 0.100). SNBRAI did not correlate with AIRT of pRNFL or mRT in PD and DIP patients (all P values > 0.060). CONCLUSION: Our study showed no benefit of retinal thickness and interocular asymmetry measurements using OCT for distinguishing PD from DIP in the early stages. Additional investigations are needed for confirmation.

MR Template-Based Individual Brain PET Volumes-of-Interest Generation Neither Using MR nor Using Spatial Normalization.

For more anatomically precise quantitation of mouse brain PET, spatial normalization (SN) of PET onto MR template and subsequent template volumes-of-interest (VOIs)-based analysis are commonly used. Although this leads to dependency on the corresponding MR and the process of SN, routine preclinical/clinical PET images cannot always afford corresponding MR and relevant VOIs. To resolve this issue, we propose a deep learning (DL)-based individual-brain-specific VOIs (i.e., cortex, hippocampus, striatum, thalamus, and cerebellum) directly generated from PET images using the inverse-spatial-normalization (iSN)-based VOI labels and deep convolutional neural network model (deep CNN). Our technique was applied to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer's disease. Eighteen mice underwent T2-weighted MRI and 18F FDG PET scans before and after the administration of human immunoglobin or antibody-based treatments. To train the CNN, PET images were used as inputs and MR iSN-based target VOIs as labels. Our devised methods achieved decent performance in terms of not only VOI agreements (i.e., Dice similarity coefficient) but also the correlation of mean counts and SUVR, and CNN-based VOIs was highly accordant with ground-truth (the corresponding MR and MR template-based VOIs). Moreover, the performance metrics were comparable to that of VOI generated by MR-based deep CNN. In conclusion, we established a novel quantitative analysis method both MR-less and SN-less fashion to generate individual brain space VOIs using MR template-based VOIs for PET image quantification. Supplementary Information: The online version contains supplementary material available at 10.1007/s13139-022-00772-4.

Fully Automatic Quantitative Measurement of 18F-FDG PET/CT in Thymic Epithelial Tumors Using a Convolutional Neural Network.

OBJECTIVES: The aim of this study was to develop a deep learning (DL)-based segmentation algorithm for automatic measurement of metabolic parameters of 18F-FDG PET/CT in thymic epithelial tumors (TETs), comparable performance to manual volumes of interest. PATIENTS AND METHODS: A total of 186 consecutive patients with resectable TETs and preoperative 18F-FDG PET/CT were retrospectively enrolled (145 thymomas, 41 thymic carcinomas). A quasi-3D U-net architecture was trained to resemble ground-truth volumes of interest. Segmentation performance was assessed using the Dice similarity coefficient. Agreements between manual and DL-based automated extraction of SUVmax, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and 63 radiomics features were evaluated via concordance correlation coefficients (CCCs) and linear regression slopes. Diagnostic and prognostic values were compared in terms of area under the receiver operating characteristics curve (AUC) for thymic carcinoma and hazards ratios (HRs) for freedom from recurrence. RESULTS: The mean Dice similarity coefficient was 0.83 ± 0.34. Automatically measured SUVmax (slope, 0.97; CCC, 0.92), MTV (slope, 0.94; CCC, 0.96), and TLG (slope, 0.96; CCC, 0.96) were in good agreement with manual measurements. The mean CCC and slopes were 0.88 ± 0.06 and 0.89 ± 0.05, respectively, for the radiomics parameters. Automatically measured SUVmax, MTV, and TLG showed good diagnostic accuracy for thymic carcinoma (AUCs: SUVmax, 0.95; MTV, 0.85; TLG, 0.87) and significant prognostic value (HRs: SUVmax, 1.31 [95% confidence interval, 1.16-1.48]; MTV, 2.11 [1.09-4.06]; TLG, 1.90 [1.12-3.23]). No significant differences in the AUCs or HRs were found between automatic and manual measurements for any of the metabolic parameters. CONCLUSIONS: Our DL-based model provides comparable segmentation performance and metabolic parameter values to manual measurements in TETs.

Unified Deep Learning-Based Mouse Brain MR Segmentation: Template-Based Individual Brain Positron Emission Tomography Volumes-of-Interest Generation Without Spatial Normalization in Mouse Alzheimer Model.

Although skull-stripping and brain region segmentation are essential for precise quantitative analysis of positron emission tomography (PET) of mouse brains, deep learning (DL)-based unified solutions, particularly for spatial normalization (SN), have posed a challenging problem in DL-based image processing. In this study, we propose an approach based on DL to resolve these issues. We generated both skull-stripping masks and individual brain-specific volumes-of-interest (VOIs-cortex, hippocampus, striatum, thalamus, and cerebellum) based on inverse spatial normalization (iSN) and deep convolutional neural network (deep CNN) models. We applied the proposed methods to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer's disease. Eighteen mice underwent T2-weighted MRI and 18F FDG PET scans two times, before and after the administration of human immunoglobulin or antibody-based treatments. For training the CNN, manually traced brain masks and iSN-based target VOIs were used as the label. We compared our CNN-based VOIs with conventional (template-based) VOIs in terms of the correlation of standardized uptake value ratio (SUVR) by both methods and two-sample t-tests of SUVR % changes in target VOIs before and after treatment. Our deep CNN-based method successfully generated brain parenchyma mask and target VOIs, which shows no significant difference from conventional VOI methods in SUVR correlation analysis, thus establishing methods of template-based VOI without SN.

Diagnostic and prognostic values of 2-[18F]FDG PET/CT in resectable thymic epithelial tumour.

OBJECTIVES: We aimed to evaluate the diagnostic ability for the prediction of histologic grades and prognostic values on recurrence and death of pretreatment 2-[18F]FDG PET/CT in patients with resectable thymic epithelial tumours (TETs). METHODS: One hundred and fourteen patients with TETs who underwent pretreatment 2-[18F]FDG PET/CT between 2012 and 2018 were retrospectively evaluated. TETs were classified into three histologic subtypes: low-risk thymoma (LRT, WHO classification A/AB/B1), high-risk thymoma (HRT, B2/B3), and thymic carcinoma (TC). Area under the receiver operating characteristics curve (AUC) was used to assess the diagnostic performance of PET/CT variables (maximum standardised uptake value [SUVmax], metabolic tumour volume [MTV], total lesion glycolysis [TLG], maximum diameter). Cox proportional hazards models were built using PET/CT and clinical variables. RESULTS: The tumours included 52 LRT, 33 HRT, and 29 TC. SUVmax showed good diagnostic ability for differentiating HRT/TC from LRT (AUC 0.84, 95% confidence interval [CI] 0.76 - 0.92) and excellent ability for differentiating TC from LRT/HRT (AUC 0.94, 95% CI 0.90 - 0.98), with significantly higher values than MTV, TLG, and maximum diameter. With an optimal cut-off value of 6.4, the sensitivity, specificity, and accuracy for differentiating TC from LRT/HRT were 69%, 96%, and 89%, respectively. In the multivariable Cox proportional hazards analyses for freedom-from-recurrence, SUVmax was an independent prognostic factor (p < 0.001), whereas MTV and TLG were not. SUVmax was a significant predictor for overall survival in conjunction with clinical stage and resection margin. CONCLUSION: SUVmax showed excellent diagnostic performance for prediction of TC and significant prognostic value in terms of recurrence and survival. KEY POINTS: • Maximum standardised uptake value (SUVmax) shows excellent performance in the differentiation of thymic carcinoma from low- and high-risk thymoma. • SUVmax is an independent prognostic factor for freedom-from-recurrence in the multivariable Cox proportional hazard model and a significant predictor for overall survival. • 2-[18F]FDG PET/CT can provide a useful diagnostic and prognostic imaging biomarker in conjunction with histologic classification and stage and help choose appropriate management for thymic epithelial tumours.

Comparison of diagnostic sensitivity of [18F]fluoroestradiol and [18F]fluorodeoxyglucose positron emission tomography/computed tomography for breast cancer recurrence in patients with a history of estrogen receptor-positive primary breast cancer.

BACKGROUND: To compare the diagnostic sensitivity of [18F]fluoroestradiol ([18F]FES) and [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) for breast cancer recurrence in patients with estrogen receptor (ER)-positive primary breast cancer. METHODS: Our database of consecutive patients enrolled in a previous prospective cohort study to assess [18F]FES PET/CT was reviewed to identify eligible patients who had ER-positive primary breast cancer with suspected first recurrence at presentation and who underwent [18F]FDG PET/CT. The sensitivity of qualitative [18F]FES and [18F]FDG PET/CT interpretations was assessed, comparing them with histological diagnoses. RESULTS: Of the 46 enrolled patients, 45 were confirmed as having recurrent breast cancer, while one was diagnosed with chronic granulomatous inflammation. Forty (89%) patients were ER-positive, four (9%) were ER-negative, and one (2%) patient did not undergo an ER assay. The sensitivity of [18F]FES PET/CT was 71.1% (32/45, 95% CI, 55.7-83.6), while that of [18F]FDG PET/CT was 80.0% (36/45, 95% CI, 65.4-90.4) with a threshold of positive interpretation, and 93.3% (42/45, 95% CI, 81.7-98.6) when a threshold of equivocal was used. There was no significant difference in sensitivity between [18F]FES and [18F]FDG PET/CT (P = 0.48) with a threshold of positive [18F]FDG uptake, but the sensitivity of [18F]FDG was significantly higher than [18F]FES (P = 0.013) with a threshold of equivocal [18F]FDG uptake. One patient with a benign lesion showed negative [18F]FES but positive [18F]FDG uptake. CONCLUSIONS: The restaging of patients who had ER-positive primary breast cancer and present with recurrent disease may include [18F]FES PET/CT as an initial test when standard imaging studies are equivocal or suspicious.

Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture.

The aim of this study is to design GoogLeNet deep neural network architecture by expanding the kernel size of the inception layer and combining the convolution layers to classify the electrocardiogram (ECG) beats into a normal sinus rhythm, premature ventricular contraction, atrial premature contraction, and right/left bundle branch block arrhythmia. Based on testing MIT-BIH arrhythmia benchmark databases, the scope of training/test ECG data was configured by covering at least three and seven R-peak features, and the proposed extended-GoogLeNet architecture can classify five distinct heartbeats; normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block (RBBB), and left bundle brunch block(LBBB), with an accuracy of 95.94%, an error rate of 4.06%, a maximum sensitivity of 96.9%, and a maximum positive predictive value of 95.7% for judging a normal or an abnormal beat with considering three ECG segments; an accuracy of 98.31%, a sensitivity of 88.75%, a specificity of 99.4%, and a positive predictive value of 94.4% for classifying APC from NSR, PVC, APC beats, whereas the error rate for misclassifying APC beat was relative low at 6.32%, compared with previous research efforts.

Effect of Nutrition Counseling Program on Weight Control in Obese University Students.

This study was conducted to investigate the effect of nutrition counseling program and related factors on weight control for obese university students. Subjects were 24 students with a body mass index (BMI) of 25 or above. The program was conducted from September 16th to November 18th, 2015. Change of body composition, blood index and nutrient intake were observed in subjects before and after the program. The average age of the subjects was 23.2 years old and the percentage of male and female was 66.7% and 33.0%, respectively. There were tendencies of decrease in weight, amount of body fat, BMI, and body fat percentage. The blood test showed that values of all biochemical parameters were in the normal range before and after the program. When the change of the nutrient intake was examined and compared with the Dietary Reference Intakes for Koreans (KDRIs), there was a tendency of decreased intake in most of the nutrients including protein. However, the nutrient quality index showed increasing tendency, which implies that the intake of micronutrients was getting balances simultaneously with the decrease of calorie intake.