Usefulness of position emission tomography/computed tomography in a case of sarcoidosis with multiorgan involvement

Sarcoidosis, a systemic inflammatory disease of unknown etiology, can affect any site in the body. A bone lesion was unexpectedly detected by fluorodeoxyglucose position emission tomography/computed tomography (FDG PET/CT) in a patient with multiorgan sarcoidosis. FDG PET/CT should be considered for the detection of clinically silent lesions of sarcoidosis.

DeepImageTranslator V2: analysis of multimodal medical images using semantic segmentation maps generated through deep learning

Introduction: Analysis of multimodal medical images often requires the selection of one or many anatomical regions of interest (ROIs) for extraction of useful statistics. This task can prove laborious when a manual approach is used. We have previously developed a user-friendly software tool for image-to-image translation using deep learning. Therefore, we present herein an update to the DeepImageTranslator software with the addiction of a tool for multimodal medical image segmentation analysis (hereby referred to as the MMMISA). Methods: The MMMISA was implemented using the Tkinter library. Backend computations were implemented using the Pydicom, Numpy, and OpenCV libraries. We tested our software using 4188 whole-body axial 2-deoxy-2-[18F]-fluoroglucose-position emission tomography/computed tomography ([18F]-FDG-PET/CT) slices of 10 patients from the ACRIN-HNSCC (American College of Radiology Imaging Network-Head and Neck Squamous Cell Carcinoma) database. Using the deep learning software DeepImageTranslator, a model was trained with 36 randomly selected CT slices and manually labelled semantic segmentation maps. Utilizing the trained model, all the CT scans of the 10 HNSCC patients were segmented with high accuracy. Segmentation maps generated using the deep convolutional network were then used to measure organ specific [18F]-FDG uptake. We also compared measurements performed using the MMMISA and those made with manually selected ROIs. Results: The MMMISA is a tool that allows user to select ROIs based on deep learning-generated segmentation maps and to compute accurate statistics for these ROIs based on coregistered multimodal images. We found that organ-specific [18F]-FDG uptake measured using multiple manually selected ROIs is concordant with whole-tissue measurements made with segmentation maps using the MMMISA tool.

Quantitative evaluation of range and metabolic activity of hepatic alveolar echinococcosis lesion microenvironment using PET/CT and multi-site sampling method

Background Alveolar echinococcosis (AE) lesion microenvironment (LME) is crucial site where parasite-host interactions happen and of great significance during surgery and obtaining liver samples for basic research. However, little is known about quantification of LME range and its’ metabolic activity regarding different lesion characteristics. Methods A prospective and retrospective analysis of LME from surgical AE patients was performed. Patients (n = 75) received abdominal computed tomography (CT) and position emission tomography/computed tomography using 18F-fluodeoxyglucose (18F-FDG-PET/CT) within 1 week prior to surgery. Semiquantitatively, calcification was clustered with 0%, < 50% and ≥ 50% degrees at lesion periphery; liquefaction was clustered with 0%, < 50%, 50 ~ 75%, ≥75% degrees at lesion center using volumetric ratio. Tumor to background ratio (TBR) of 18F-FDG standard uptake value (SUV, n = 75) was calculated, and range of 18F-FDG uptake area was measured; Multi-site sampling method (MSS, n = 35) was introduced to obtain histological slides to evaluate immune cell infiltrative ranges. Results Altogether six major lesion groups have been identified (A: 0% calcified, 0% liquefied; B: ≥50% calcified, 0% liquefied; C: < 50% calcified, < 50% liquefied; D: ≥50% calcified, < 50% liquefied; E: < 50% calcified, 50 ~ 75% liquefied; F: ≥50% calcified, ≥75% liquefied). Statistically, TBR values respectively were 5.1 ± 1.9, 2.7 ± 1.2, 4.2 ± 1.2, 2.7 ± 0.7, 4.6 ± 1.2, 2.9 ± 1.1 in groups A ~ F, and comparisons showed A > B, A > D, A > F, E > B, E > D, E > F, C > B, C > D, C > F (P < 0.05); LME ranges indicated by PET/CT respectively were 14.9 ± 3.9, 10.6 ± 1.5, 12.3 ± 1.1, 7.8 ± 1.6, 11.1 ± 2.3, 7.0 ± 0.4 mm in groups A ~ F, and comparisons showed A > B, A > D, A > F, A > E, C > B, C > D, C > F, E > D, E > F, B > D, B > F (P < 0.05); LME ranges indicated by MSS respectively were 17.9 ± 4.9, 13.0 ± 2.7, 11.9 ± 2.6, 6.0 ± 2.2, 11.0 ± 4.1, 6.0 ± 2.2 mm in groups A ~ F, and comparisons showed A > C, A > D, A > F, B > D, B > F, C > D, C > F (P < 0.05). Generally, less calcifications indicated higher TBR values and wider LME ranges; and, severer liquefactions indicated smaller LME ranges. Additionally, patients with previous medication history had lower TBR values. Conclusions PET/CT and MSS method showed distinct TBRs and LME ranges for different calcifications and liquefactions. This study would be able to provide references for both surgical resections of lesions and more accurate sample acquisitions for basic research targeted to immunology.

Deep learning-based amyloid PET positivity classification model in the Alzheimer’s disease continuum by using 2-[18F]FDG PET

Background Considering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[18F]FDG). Methods We used 2-[18F]FDG PET datasets from the Alzheimer's Disease Neuroimaging Initiative and Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer’s disease for model development. Moreover, we used an independent dataset from another hospital. A 2.5-D deep learning architecture was constructed using 291 submodules and three axes images as the input. We conducted the voxel-wise analysis to assess the regions with substantial differences in glucose metabolism between the amyloid PET-positive and PET-negative participants. This facilitated an understanding of the deep model classification. In addition, we compared these regions with the classification probability from the submodules. Results There were 686 out of 1433 (47.9%) and 50 out of 100 (50%) amyloid PET-positive participants in the training and internal validation datasets and the external validation datasets, respectively. With 50 times iterations of model training and validation, the model achieved an AUC of 0.811 (95% confidence interval (CI) of 0.803–0.819) and 0.798 (95% CI, 0.789–0.807) on the internal and external validation datasets, respectively. The area under the curve (AUC) was 0.860 when tested with the model with the highest value (0.864) on the external validation dataset. Moreover, it had 75.0% accuracy, 76.0% sensitivity, 74.0% specificity, and 75.0% F1-score. We found an overlap between the regions within the default mode network, thus generating high classification values. Conclusion The proposed model based on the 2-[18F]FDG PET imaging data and a DL framework might successfully classify amyloid PET positivity in clinical practice, without performing amyloid PET, which have limited accessibility.

Cerebrospinal fluid p-tau231 as an early indicator of emerging pathology in Alzheimer’s disease

Biomarkers for early phosphorylation of tau constitute an unmet need for disease modifying intervention in early stages of Alzheimer’s disease (AD). Recent advances in targeted mass spectrometry and immunoassays have revealed phosphorylation sites, in the cerebrospinal fluid (CSF), with potentially greater utility as preclinical and diagnostic biomarkers as compared to the well validated biomarker – phosphorylated tau at threonine 181 (p-tau181). Phosphorylated tau (p-tau) epitopes in cerebrospinal fluid (CSF) are highly accurate biomarkers for Alzheimer’s disease (AD) neuropathology and are already increased before cognitive symptoms have manifested. However, it is unknown if these preclinical increases transpire earlier, prior to amyloid-beta (Aβ) positivity threshold, and if an ordinal sequence of p-tau epitopes occurs at this incipient phase. In this study, we measured cerebrospinal (CSF) p-tau181, p-tau217 and p-tau231 in 171 participants across the AD continuum compared to AD neuropathology as indexed by Ab ([18F]AZD4694) and tau ([18F]MK6240) position emission tomography. CSF P-tau217 and p-tau231 predicted Aβ and tau at the preclinical and dementia stages to a similar degree but p-tau231 attained abnormal levels first. P-tau231 was more sensitive to the earliest changes in Aβ in the medial orbitofrontal, precuneus and posterior cingulate cortices before global Aβ PET positivity had been achieved. Our findings demonstrate that CSF p-tau231 increases early in development of AD pathology and is a principal candidate for detecting incipient Aβ pathology for therapeutic trial application.

A comparative analysis: international variation in PET-CT service provision in oncology—an International Cancer Benchmarking Partnership study

Objective To explore differences in position emission tomography-computed tomography (PET-CT) service provision internationally to further understand the impact variation may have upon cancer services. To identify areas of further exploration for researchers and policymakers to optimize PET-CT services and improve the quality of cancer services. Design Comparative analysis using data based on pre-defined PET-CT service metrics from PET-CT stakeholders across seven countries. This was further informed via document analysis of clinical indication guidance and expert consensus through round-table discussions of relevant PET-CT stakeholders. Descriptive comparative analyses were produced on use, capacity and indication guidance for PET-CT services between jurisdictions. Setting PET-CT services across 21 jurisdictions in seven countries (Australia, Denmark, Canada, Ireland, New Zealand, Norway and the UK). Participants None. Intervention(s) None. Main Outcome Measure(s) None. Results PET-CT service provision has grown over the period 2006–2017, but scale of increase in capacity and demand is variable. Clinical indication guidance varied across countries, particularly for small-cell lung cancer staging and the specific acknowledgement of gastric cancer within oesophagogastric cancers. There is limited and inconsistent data capture, coding, accessibility and availability of PET-CT activity across countries studied. Conclusions Variation in PET-CT scanner quantity, acquisition over time and guidance upon use exists internationally. There is a lack of routinely captured and accessible PET-CT data across the International Cancer Benchmarking Partnership countries due to inconsistent data definitions, data linkage issues, uncertain coverage of data and lack of specific coding. This is a barrier in improving the quality of PET-CT services globally. There needs to be greater, richer data capture of diagnostic and staging tools to facilitate learning of best practice and optimize cancer services.

Age, sex and APOE-ε4 modify the balance between soluble and fibrillar β-amyloid in cognitively intact individuals: topographical patterns and replication across two independent cohorts

ABSTRACTCerebral beta-amyloid (Aβ) accumulation is the earliest detectable pathophysiological event along the Alzheimer’s disease (AD) continuum, therefore an accurate quantification of incipient Aβ abnormality is of great importance to identify preclinical AD. Both cerebrospinal fluid (CSF) Aβ concentrations and Position Emission Tomography (PET) with specific tracers provide established biomarkers of Aβ pathology. Yet, they identify two different biological processes reflecting the clearance rate of soluble Aβ as opposed to the cerebral aggregation of insoluble Aβ fibrils. Studies have demonstrated high agreement between CSF and PET-based Aβ measurements on diagnostic and prognostic levels. However, an open question is whether risk factors known to increase AD prevalence may promote an imbalance between these biomarkers, leading to a higher cumulative Aβ cerebral aggregation for a given level of cleared Aβ in the CSF. Unveiling such interactions in cognitively unimpaired (CU) individuals shall provide novel insights into the biological pathways underlying Aβ aggregation in the brain and ultimately improve our knowledge on disease modelling. With this in mind, we assessed the impact of three major unmodifiable AD risk factors (age, APOE-ε4 and sex) on the association between soluble and deposited Aβ in a sample of 293 middle-aged CU individuals who underwent both lumbar puncture and PET imaging using the [18F]flutemetamol tracer. We looked for interactions between CSF Aβ42/40 concentrations and each of the assessed risk factors, in promoting Aβ PET uptake both in candidate regions of interest and in the whole brain. We found that, for any given level of CSF Aβ42/40, older age and female sex induced higher fibrillary plaque deposition in neocortical areas including the anterior, middle and posterior cingulate cortex. By contrast, the modulatory role of APOE-ε4 was uniquely prominent in areas known for being vulnerable to early tau deposition, such as the entorhinal cortex and the hippocampus bilaterally. Post hoc three-way interactions additionally proved evidence for a synergistic effect among the risk factors on the spatial topology of Aβ deposition as a function of CSF Aβ4/40 levels. Importantly, findings were replicated in an independent sample of CU individuals derived from the ADNI cohort. Our data clarify the mechanisms underlying the higher AD prevalence associated to those risk factors and suggest that APOE-ε4 in particular paves the way for subsequent tau spreading in the medial temporal lobe, thus favoring a spatial co-localization between Aβ and tau and increasing their synergistic interaction along the disease continuum.