Vascular burden and cognition: Mediating roles of neurodegeneration and amyloid PET

It remains unclear to what extent cerebrovascular burden relates to amyloid beta (Aβ) deposition, neurodegeneration, and cognitive dysfunction in mixed disease populations with small vessel disease and Alzheimer's disease (AD) pathology. In 120 subjects, we investigated the association of vascular burden (white matter hyperintensity [WMH] volumes) with cognition. Using mediation analyses, we tested the indirect effects of WMH on cognition via Aβ deposition (¹⁸ F-AV45 positron emission tomography [PET]) and neurodegeneration (cortical thickness or ¹⁸ F fluorodeoxyglucose PET) in AD signature regions. We observed that increased total WMH volume was associated with poorer performance in all tested cognitive domains, with the strongest effects observed for semantic fluency. These relationships were mediated mainly via cortical thinning, particularly of the temporal lobe, and to a lesser extent serially mediated via Aβ and cortical thinning of AD signature regions. WMH volumes differentially impacted cognition depending on lobar location and Aβ status. In summary, our study suggests mainly an amyloid-independent pathway in which vascular burden affects cognitive function via localized neurodegeneration. HIGHLIGHTS: Alzheimer's disease often co-exists with vascular pathology. We studied a unique cohort enriched for high white matter hyperintensities (WMH). High WMH related to cognitive impairment of semantic fluency and executive function. This relationship was mediated via temporo-parietal atrophy rather than metabolism. This relationship was, to lesser extent, serially mediated via amyloid beta and atrophy.

Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies

We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward personalization of therapies. We try to motivate the nuclear medicine community to expand and align efforts into making routine and reliable personalization of RPTs a reality.

Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative

RATIONALE

We evaluated K-means clustering to classify amyloid brain PETs as positive or negative.

PATIENTS AND METHODS

Sixty-six participants (31 men, 35 women; age range, 52-81 years) were recruited through a multicenter observational study: 19 cognitively normal, 25 mild cognitive impairment, and 22 dementia (11 Alzheimer disease, 3 subcortical vascular cognitive impairment, and 8 Parkinson-Lewy Body spectrum disorder). As part of the neurocognitive and imaging evaluation, each participant had an 18F-flutemetamol (Vizamyl, GE Healthcare) brain PET. All studies were processed using Cortex ID software (General Electric Company, Boston, MA) to calculate SUV ratios in 19 regions of interest and clinically interpreted by 2 dual-certified radiologists/nuclear medicine physicians, using MIM software (MIM Software Inc, Cleveland, OH), blinded to the quantitative analysis, with final interpretation based on consensus. K-means clustering was retrospectively used to classify the studies from the quantitative data.

RESULTS

Based on clinical interpretation, 46 brain PETs were negative and 20 were positive for amyloid deposition. Of 19 cognitively normal participants, 1 (5%) had a positive 18F-flutemetamol brain PET. Of 25 participants with mild cognitive impairment, 9 (36%) had a positive 18F-flutemetamol brain PET. Of 22 participants with dementia, 10 (45%) had a positive 18F-flutemetamol brain PET; 7 of 11 participants with Alzheimer disease (64%), 1 of 3 participants with vascular cognitive impairment (33%), and 2 of 8 participants with Parkinson-Lewy Body spectrum disorder (25%) had a positive 18F-flutemetamol brain PET. Using clinical interpretation as the criterion standard, K-means clustering (K = 2) gave sensitivity of 95%, specificity of 98%, and accuracy of 97%.

CONCLUSIONS

K-means clustering may be a powerful algorithm for classifying amyloid brain PET.

Machine Learning in Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects

This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural networks work and a discussion of potential applications. Recognizing that there is a spectrum of applications, we focus on recent publications in the areas of image reconstruction, low-dose PET, disease detection, and models used for diagnosis and outcome prediction. Finally, since the way machine learning algorithms are reported in the literature is extremely variable, we conclude with a call to arms regarding the need for standardized reporting of design and outcome metrics and we propose a basic checklist our community might follow going forward.