18F-FMISO-PET Hypoxia Monitoring for Head-and-Neck Cancer Patients: Radiomics Analyses Predict the Outcome of Chemo-Radiotherapy.

Tumor hypoxia is associated with radiation resistance and can be longitudinally monitored by 18F-fluoromisonidazole (18F-FMISO)-PET/CT. Our study aimed at evaluating radiomics dynamics of 18F-FMISO-hypoxia imaging during chemo-radiotherapy (CRT) as predictors for treatment outcome in head-and-neck squamous cell carcinoma (HNSCC) patients. We prospectively recruited 35 HNSCC patients undergoing definitive CRT and longitudinal 18F-FMISO-PET/CT scans at weeks 0, 2 and 5 (W0/W2/W5). Patients were classified based on peritherapeutic variations of the hypoxic sub-volume (HSV) size (increasing/stable/decreasing) and location (geographically-static/geographically-dynamic) by a new objective classification parameter (CP) accounting for spatial overlap. Additionally, 130 radiomic features (RF) were extracted from HSV at W0, and their variations during CRT were quantified by relative deviations (∆RF). Prediction of treatment outcome was considered statistically relevant after being corrected for multiple testing and confirmed for the two 18F-FMISO-PET/CT time-points and for a validation cohort. HSV decreased in 64% of patients at W2 and in 80% at W5. CP distinguished earlier disease progression (geographically-dynamic) from later disease progression (geographically-static) in both time-points and cohorts. The texture feature low grey-level zone emphasis predicted local recurrence with AUCW2 = 0.82 and AUCW5 = 0.81 in initial cohort (N = 25) and AUCW2 = 0.79 and AUCW5 = 0.80 in validation cohort. Radiomics analysis of 18F-FMISO-derived hypoxia dynamics was able to predict outcome of HNSCC patients after CRT.

Validation of the Alzheimer Disease Dementia Conversion-Related Pattern as an ATN Biomarker of Neurodegeneration.

OBJECTIVE: To determine whether the Alzheimer disease (AD) dementia conversion-related pattern (ADCRP) on [18F]FDG PET can serve as a valid predictor for the development of AD dementia, the individual expression of the ADCRP (subject score) and its prognostic value were examined in patients with mild cognitive impairment (MCI) and biologically defined AD. METHODS: A total of 269 patients with available [18F]FDG PET, [18F]AV-45 PET, phosphorylated and total tau in CSF, and neurofilament light chain in plasma were included. Following the AT(N) classification scheme, where AD is defined biologically by in vivo biomarkers of ß-amyloid (Aß) deposition ("A") and pathologic tau ("T"), patients were categorized to the A-T-, A+T-, A+T+ (AD), and A-T+ groups. RESULTS: The mean subject score of the ADCRP was significantly higher in the A+T+ group compared to each of the other group (all p < 0.05) but was similar among the latter (all p > 0.1). Within the A+T+ group, the subject score of ADCRP was a significant predictor of conversion to dementia (hazard ratio, 2.02 per z score increase; p < 0.001), with higher predictive value than of alternative biomarkers of neurodegeneration (total tau and neurofilament light chain). Stratification of A+T+ patients by the subject score of ADCRP yielded well-separated groups of high, medium, and low conversion risks. CONCLUSIONS: The ADCRP is a valuable biomarker of neurodegeneration in patients with MCI and biologically defined AD. It shows great potential for stratifying the risk and estimating the time to conversion to dementia in patients with MCI and underlying AD (A+T+). CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that [18F]FDG PET predicts the development of AD dementia in individuals with MCI and underlying AD as defined by the AT(N) framework.

Principal-Component Analysis-Based Measures of PET Data Closely Reflect Neuropathologic Staging Schemes.

Voxel-based principal-component analysis allows for an identification of patterns of glucose metabolism and amyloid deposition related to the conversion from mild cognitive impairment (MCI) to Alzheimer disease (AD). The present study aimed to validate these AD conversion-related patterns (ADCRPs) against neuropathologic findings. Methods: We included patients from the Alzheimer's Disease Neuroimaging Initiative who underwent autopsy and for whom 18F-FDG PET (30 AD, 6 MCI, 2 cognitively normal) and amyloid-ß (Aß) PET (17 AD, 3 MCI, 2 cognitively normal) were available. Pattern expression scores (PESs) of the 18F-FDG- and Aß-ADCRP were compared with Braak tangle stage and Thal amyloid phase, respectively. Mean 18F-FDG uptake and mean 18F-AV-45 SUV ratio (SUVr) in regions of hypometabolism and elevated amyloid load typical of AD, respectively, were used as volume-of-interest-based PET measures. The diagnostic performance for identifying none-to-low vs. intermediate-to-high AD neuropathologic change (ADNC) was assessed for all biomarkers. Results: We observed significant associations between PES of 18F-FDG-ADCRP and Braak stage (ρ > 0.48, P < 0.005) and between PES of Aß-ADCRP and Thal phase (ρ > 0.66, P < 0.001). PES of 18F-FDG-ADCRP, PES of Aß-ADCRP, and their combination identified intermediate-to-high ADNC with an area under the receiver-operating-characteristic curve (AUC) of 0.80, 0.95, and 0.98 (n = 22), respectively. Mean 18F-FDG uptake and mean 18F-AV-45 SUVr in AD-typical regions were also significantly associated with Braak stage (|ρ| > 0.45, P < 0.01) and Thal phase (ρ > 0.55, P < 0.01), respectively. Volume-of-interest-based PET measures discriminated between ADNC stages with an AUC of 0.79, 0.88, and 0.90 for mean 18F-FDG uptake, mean 18F-AV-45 SUVr, and their combination (n = 22), respectively. Contemplating all subjects with available 18F-FDG PET and neuropathology information (n = 38), PES of 18F-FDG-ADCRP was a significant predictor of intermediate-to-high ADNC (AUC = 0.72), whereas mean 18F-FDG uptake was not (AUC = 0.66), although the difference between methods was not significant. Conclusion: PES of 18F-FDG-ADCRP, a measure of neurodegeneration, shows close correspondence with the extent of tau pathology, as assessed by Braak tangle stage. PES of Aß-ADCRP is a valid biomarker of underlying amyloid pathology, as demonstrated by its strong correlation with Thal phase. The combination of ADCRPs performed better than 18F-FDG-ADCRP alone, although there was only negligible improvement compared with Aß-ADCRP.

Amyloid biomarkers as predictors of conversion from mild cognitive impairment to Alzheimer's dementia: a comparison of methods.

BACKGROUND: Amyloid-ß (Aß) PET is an established predictor of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD). We compared three PET (including an approach based on voxel-wise Cox regression) and one cerebrospinal fluid (CSF) outcome measures in their predictive power. METHODS: Datasets were retrieved from the ADNI database. In a training dataset (N = 159), voxel-wise Cox regression and principal component analyses were used to identify conversion-related regions (Cox-VOI and AD conversion-related pattern (ADCRP), respectively). In a test dataset (N = 129), the predictive value of mean normalized 18F-florbetapir uptake (SUVR) in AD-typical brain regions (composite SUVR) or the Cox-VOI and the pattern expression score (PES) of ADCRP and CSF Aß42/Aß40 as predictors were compared by Cox models (corrected for age and sex). RESULTS: All four Aß measures were significant predictors (p < 0.001). Prediction accuracies (Harrell's c) showed step-wise significant increases from Cox-SUVR (c = 0.71; HR = 1.84 per Z-score increase), composite SUVR (c = 0.73; HR = 2.18), CSF Aß42/Aß40 (c = 0.75; HR = 3.89) to PES (c = 0.77; HR = 2.71). CONCLUSION: The PES of ADCRP is the most predictive Aß PET outcome measure, comparable to CSF Aß42/Aß40, with a slight but statistically significant advantage.

Deep Brain Stimulation of the Medial Forebrain Bundle in a Rodent Model of Depression: Exploring Dopaminergic Mechanisms with Raclopride and Micro-PET.

BACKGROUND: Deep brain stimulation (DBS) of the medial forebrain bundle (MFB) can reverse depressive-like symptoms clinically and in experimental models of depression, but the mechanisms of action are unknown. OBJECTIVES: This study investigated the role of dopaminergic mechanisms in MFB stimulation-mediated behavior changes, in conjunction with raclopride administration and micropositron emission tomography (micro-PET). METHODS: Flinders Sensitive Line (FSL) rats were allocated into 4 groups: FSL (no treatment), FSL+ (DBS), FSL.R (FSL with raclopride), and FSL.R+ (FSL with raclopride and DBS). Animals were implanted with bilateral electrodes targeting the MFB and given 11 days access to raclopride in the drinking water with or without concurrent continuous bilateral DBS over the last 10 days. Behavioral testing was conducted after stimulation. A PET scan using [18F]desmethoxyfallypride was performed to determine D2 receptor availability before and after raclopride treatment. Changes in gene expression in the nucleus accumbens and the hippocampus were assessed using quantitative polymerase chain reaction. RESULTS: Micro-PET imaging showed that raclopride administration blocked 36% of the D2 receptor in the striatum, but the relative level of blockade was reduced/modulated by stimulation. Raclopride treatment enhanced depressive-like symptoms in several tasks, and the MFB DBS partially reversed the depressive-like phenotype. The raclopride-treated MFB DBS animals had increased levels of mRNA coding for dopamine receptor D1 and D2 suggestive of a stimulation-mediated increase in dopamine receptors. CONCLUSION: Data suggest that chronic and continuous MFB DBS could act via the modulation of the midbrain dopaminergic transmission, including impacting on the postsynaptic dopamine receptor profile.

Predictive Value of 18F-Florbetapir and 18F-FDG PET for Conversion from Mild Cognitive Impairment to Alzheimer Dementia.

The present study examined the predictive values of amyloid PET, 18F-FDG PET, and nonimaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: The study included 319 patients with MCI from the Alzheimer Disease Neuroimaging Initiative database. In a derivation dataset (n = 159), the following Cox proportional-hazards models were constructed, each adjusted for age and sex: amyloid PET using 18F-florbetapir (pattern expression score of an amyloid-ß AD conversion-related pattern, constructed by principle-components analysis); 18F-FDG PET (pattern expression score of a previously defined 18F-FDG-based AD conversion-related pattern, constructed by principle-components analysis); nonimaging (functional activities questionnaire, apolipoprotein E, and mini-mental state examination score); 18F-FDG PET + amyloid PET; amyloid PET + nonimaging; 18F-FDG PET + nonimaging; and amyloid PET + 18F-FDG PET + nonimaging. In a second step, the results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects according to the predicted conversion risk. Results: On the basis of the independent validation dataset, the 18F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the nonimaging model and were significantly improved by the addition of nonimaging variables. The best prediction accuracy was reached by combining 18F-FDG PET, amyloid PET, and nonimaging variables. The combined model yielded 5-y free-of-conversion rates of 100%, 64%, and 24% for the low-, medium- and high-risk groups, respectively. Conclusion:18F-FDG PET, amyloid PET, and nonimaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks, which is of great interest for patient care and clinical trials.

Principal Components Analysis of Brain Metabolism Predicts Development of Alzheimer Dementia.

The value of 18F-FDG PET for predicting conversion from mild cognitive impairment (MCI) to Alzheimer dementia (AD) is currently under debate. We used a principal components analysis (PCA) to identify a metabolic AD conversion-related pattern (ADCRP) and investigated the prognostic value of the resulting pattern expression score (PES). Methods:18F-FDG PET scans of 544 MCI patients were obtained from the Alzheimer Disease Neuroimaging Initiative database and analyzed. We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose cerebral metabolic patterns associated with conversion from MCI to AD. By Cox proportional hazards regression, we examined the prognostic value of candidate predictors. Also, we constructed prognostic models with clinical, imaging, and clinical and imaging variables in combination. Results: PCA revealed an ADCRP that involved regions with relative decreases in metabolism (temporoparietal, frontal, posterior cingulate, and precuneus cortices) and relative increases in metabolism (sensorimotor and occipital cortices, cerebellum, and left putamen). Among the predictor variables age, sex, Functional Activities Questionnaire, Mini-Mental State Examination, apolipoprotein E, PES, and normalized 18F-FDG uptake (regions with significant hypo- and hypermetabolism in patients with conversion vs. those without conversion), PES was the best independent predictor of conversion (hazard ratio, 1.77, per z score increase; 95% CI, 1.24-2.52; P < 0.001). Moreover, adding PES to the model including the clinical variables significantly increased its prognostic value. Conclusion: The ADCRP expression score was a valid predictor of conversion. A combination of clinical variables and PES yielded a higher accuracy than each single tool in predicting conversion from MCI to AD, underlining the incremental utility of 18F-FDG PET.

Prognosis of conversion of mild cognitive impairment to Alzheimer's dementia by voxel-wise Cox regression based on FDG PET data.

AIM: The value of 18F-fluorodeoxyglucose (FDG) PET for the prognosis of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD) is controversial. In the present work, the identification of cerebral metabolic patterns with significant prognostic value for conversion of MCI patients to AD is investigated with voxel-based Cox regression, which in contrast to common categorical comparisons also utilizes time information. METHODS: FDG PET data of 544 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly split into two equally-sized datasets (training and test). Within a median follow-up duration of 47 months (95% CI: 46-48 months) 181 patients developed AD. In the training dataset, voxel-wise Cox regressions were used to identify regions associated with conversion of MCI to AD. These were compared to regions identified by a classical group comparison (analysis of covariance (ANCOVA) with statistical parametric mapping (SPM) 8) between converters and non-converters (both adjusted for apolipoprotein E (APOE) genotype, mini-mental state examination (MMSE) score, age, sex and education). In the test dataset, normalized FDG uptake within significant brain regions from voxel-wise Cox- and ANCOVA analyses (Cox- and ANCOVA- regions of interest (ROI), respectively) and clinical variables APOE status, MMSE score and education were tested in different Cox models (adjusted for age, sex) including: (1) only clinical variables, (2) only normalized FDG uptake in ANCOVA-ROI, (3) only normalized FDG uptake from Cox-ROI, (4) clinical variables plus FDG uptake in ANCOVA-ROI, (5) clinical variables plus FDG uptake from Cox-ROI. RESULTS: Conversion-related regions with relative hypometabolism comprised parts of the temporo-parietal and posterior cingulate cortex/precuneus for voxel-wise ANCOVA, plus frontal regions for voxel-wise Cox regression (both p < .01, false discovery rate (FDR) corrected). The clinical-only model (1) and the models based on normalized FDG uptake from Cox-ROI only (2) and ANCOVA-ROI only (3) all significantly predicted conversion to AD (Wald Test (WT): p < .001). The clinical model (1) was significantly improved by adding imaging information in model (4) (Akaike information criterion (AIC) relative likelihood (RL) (1) vs (4): RL < 0.018). There were no significant differences between models (2) and (3), as well as (4) and (5). CONCLUSIONS: Voxel-wise Cox regression identifies conversion-related patterns of cerebral glucose metabolism, but is not superior to classical group contrasts in this regard. With imaging information from both FDG PET patterns, the prediction of conversion to AD was improved.