The value of SPECT in the detection of stress injury to the pars interarticularis in patients with low back pain

The medical cost associated with back pain in the United States is considerable and growing. Although the differential diagnosis of back pain is broad, epidemiological studies suggest a correlation between adult and adolescent complaints. Injury of the pars interarticularis is one of the most common identifiable causes of ongoing low back pain in adolescent athletes. It constitutes a spectrum of disease ranging from bone stress to spondylolysis and spondylolisthesis. Bone stress may be the earliest sign of disease. Repetitive bone stress causes bone remodeling and may result in spondylolysis, a non-displaced fracture of the pars interarticularis. A fracture of the pars interarticularis may ultimately become unstable leading to spondylolisthesis. Results in the literature support the use of bone scintigraphy to diagnose bone stress in patients with suspected spondylolysis. Single photon emission computed tomography (SPECT) provides more contrast than planar bone scintigraphy, increases the sensitivity and improves anatomic localization of skeletal lesions without exposing the patient to additional radiation. It also provides an opportunity for better correlation with other imaging modalities, when necessary. As such, the addition of SPECT to standard planar bone scintigraphy can result in a more accurate diagnosis and a better chance for efficient patient care. It is our expectation that by improving our ability to correctly diagnose bone stress in patients with suspected injury of the posterior elements, the long-term cost of managing this condition will be lowered

Targeted Radionuclide Therapy: Practical Applications and Future Prospects

In recent years, there has been a proliferation in the development of targeted radionuclide cancer therapy. It is now possible to use baseline clinical and imaging assessments to determine the most effective therapy and to tailor this therapy during the course of treatment based on radiation dosimetry and tumor response. Although this personalized approach to medicine has the advantage of maximizing therapeutic effect while limiting toxicity, it can be challenging to implement and expensive. Further, in order to use targeted radionuclide therapy effectively, there is a need for multidisciplinary awareness, education, and collaboration across the scientific, industrial, and medical communities. Even more important, there is a growing understanding that combining radiopharmaceuticals with conventional treatment such as chemotherapy and external beam radiotherapy may limit patient morbidity while improving survival. Developments in radiopharmaceuticals as biomarkers capable of predicting therapeutic response and targeting disease are playing a central role in medical research. Adoption of a practical approach to manufacturing and delivering radiopharmaceuticals, assessing patient eligibility, optimizing post-therapy follow-up, and addressing reimbursement issues will be essential for their success

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

COPYRIGHT © 2021 by the Society of Nuclear Medicine and Molecular Imaging.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 algo- rithms 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

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

Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.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 demen- tia (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 soft- ware (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 interpreta- tion 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 partici- pants, 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.This is a multisite project of the Toronto Dementia Research Alli- ance (www.tdra.utoronto.ca) partly funded by Brain Canada, The Edward Foundation, the Canadian Institutes of Health Research (FDN 159910), the LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, and the Dr Sandra Black Cen- tre for Brain Resilience and Recovery. M.F. received support from the Saul A. Silverman Family Foundation as a Canada Interna- tional Scientific Exchange Program and the Morris Kerzner Memo- rial Fund. We gratefully acknowledge GE Healthcare and the CAMH Brain Health Imaging Centre for manufacturing and sup- plying the ligand. We are also grateful to GE Healthcare for provid- ing the software to calculate the brain region of interest SUV ratios. The study protocol, Brain Eye Amyloid Memory study (BEAM), is registered at https://clinicaltrials.gov/ct2/show/NCT02524405? term=beam+sandra+black&rank=1

The use of random forests to classify amyloid brain PET

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.Purpose: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification. Methods: The data set included 57 baseline 18F-florbetapir (Amyvid; Lilly, Indianapolis, IN) brain PET scans in participants with severe white matter disease, presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a multicenter prospective observational trial. Scans were processed using the MINC toolkit to generate SUV ratios, normalized to cerebellar gray matter, and clinically read by 2 nuclear medicine physicians with interpretation based on consensus (35 negative, 22 positive). SUV ratio data and clinical reads were used for super- vised training of an RF classifier programmed in MATLAB. Results: A 10,000-tree RF, each tree using 15 randomly selected cases and 20 randomly selected features (SUV ratio per region of interest), with 37 cases for training and 20 cases for testing, had sensitivity = 86% (95% confidence in- terval [CI], 42%–100%), specificity = 92% (CI, 64%–100%), and classification accuracy = 90% (CI, 68%–99%). The most common features at the root node (key regions for stratification) were (1) left posterior cingulate (1039 trees), (2) left middle frontal gyrus (1038 trees), (3) left precuneus (857 trees), (4) right an- terior cingulate gyrus (655 trees), and (5) right posterior cingulate (588 trees). Conclusions: Random forests can classify brain PET as positive or negative for amyloid deposition and suggest key clinically relevant, regional features for classification.CIHR MITNEC C6 || Linda C Campbell Foundation || Lilly-Avid Radiopharmaceuticals

The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated With MoCA Score

Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.Purpose: The aim of this study was to evaluate random forests (RFs) to identify ROIs on 18F-florbetapir and 18F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score. Materials and Methods: Fifty-seven subjects with significant white matter disease presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a mul- ticenter prospective observational trial, had MoCA and 18F-florbetapir PET; 55 had 18F-FDG PET. Scans were processed using the MINC toolkit to gen- erate SUV ratios, normalized to cerebellar gray matter (18F-florbetapir PET), or pons (18F-FDG PET). SUV ratio data and MoCA score were used for su- pervised training of RFs programmed in MATLAB. Results: 18F-Florbetapir PETs were randomly divided into 40 training and 17 testing scans; 100 RFs of 1000 trees, constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: right posterior cingulate gyrus, right anterior cingulate gyrus, left precuneus, left posterior cingulate gyrus, and right precuneus. Amyloid in- creased with decreasing MoCA score. 18F-FDG PETs were randomly di- vided into 40 training and 15 testing scans; 100 RFs of 1000 trees, each tree constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: left fusiform gyrus, left precuneus, left posterior cingulate gyrus, right precuneus, and left middle orbitofrontal gyrus. 18F-FDG decreased with decreasing MoCA score. Conclusions: Random forests help pinpoint clinically relevant ROIs associ- ated with MoCA score; amyloid increased and 18F-FDG decreased with de- creasing MoCA score, most significantly in the posterior cingulate gyrus.CIHR MITNEC C6 || Linda C Campbell Foundation || Lilly-Avid Radiopharmaceuticals

Imatinib for Melanomas Harboring Mutationally Activated or Amplified KIT Arising on Mucosal, Acral, and Chronically Sun-Damaged Skin

Amplifications and mutations in the KIT proto-oncogene in subsets of melanomas provide therapeutic opportunities

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Associations of Amyloid Deposition and FDG Uptake in Aging and Cognitively Impaired Elders With and Without Moderate to Severe Periventricular White Matter Hyperintensities Using Simple Machine Learning Techniques

Abstract Background: Cerebral small vessel disease (SVD) often coexists with Alzheimer’s Disease (AD), but subjects with significant SVD were usually considered mixed disease and excluded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and other international cohorts. We use 2 machine learning algorithms (random forests (RFs) and K-means clustering) and 2 radiopharmaceuticals (18F-Florbetabir and 18F-FDG) to study associations of periventricular white matter hyperintensity (pvWMH), amyloid deposition and 18F-FDG uptake in aging and cognitively impaired elders. Methods: A multi-center, observational, prospective cohort study was designed to acquire data in subjects with significant pvWMH. Baseline data from the first 57 subjects recruited through tertiary memory clinics or stroke prevention clinics formed the basis for analysis. All subjects had a 3T MRI, 18F-Florbetabir PET and neurological evaluation; 18F-FDG PET was available in 55. A matched cohort of 57 subjects with minimal pvWMH was derived from ADNI. PETs were interpreted by 2 dual certified radiologists/ nuclear medicine physicians with MIM software (MIM Software Inc, Cleveland, Ohio) and processed to obtain quantitative data in regions of interest (ROIs). Algorithms programmed in MATLAB were used to classify 18F-Florbetabir PET as positive or negative for amyloid deposition, suggest ROIs for classification, and study associations with pvWMH. Discussion: This work complements current data on associations of amyloid, 18F-FDG and pvWMH. RFs (supervised) and K-means clustering (unsupervised) had similar classification accuracy for 18F-Florbetabir PET with clinical interpretation as the gold standard. ROIs for classification were, most commonly: the left posterior cingulate, left precuneus and left middle fontal gyrus; key nodes of the default node network, where amyloid deposition preferentially begins in the brain of patients with AD. Subjects with significant pvWMH had slightly more amyloid deposition across a spectrum of ROIs compared with their matched ADNI cohort. 18F-FDG uptake was slightly higher in several cortical ROIs with lower uptake in the basal ganglia. Possibly these findings can be explained by poor amyloid clearance related to venous collagenosis in subjects with significant pvWMH coupled with metabolic compensation. Future research will include evaluation of 2-year follow-up, correlation of PET with MRI, neurological assessment and ApoE4 status in the full cohort of subjects.Ph.D