Impact of region-of-interest delineation methods, reconstruction algorithms, and intra- and inter-operator variability on internal dosimetry estimates using PET

Purpose Human dosimetry studies play a central role in radioligand development for positron emission tomography (PET). Drawing regions of interest (ROIs) on the PET images is used to measure the dose in each organ. In the study aspects related to ROI delineation methods were evaluated for two radioligands of different biodistribution (intestinal vs urinary). Procedures PET images were simulated from a human voxel-based phantom. Several ROI delineation methods were tested: antero-posterior projections (AP), 3D sub-samples of the organs (S), and a 3D volume covering the whole-organ (W). Inter- and intra-operator variability ROI drawing was evaluated by using human data. Results The effective dose estimates using S and W methods were comparable to the true values. AP methods overestimated (49 %) the dose for the radioligand with intestinal biodistribution. Moreover, the AP method showed the highest inter-operator variability: 11 ± 1 %. Conclusions The sub-sampled organ method showed the best balance between quantitative accuracy and inter- and intra-operator variability.Postprint (author's final draft

Impact of training method on the robustness of the visual assessment of 18F-florbetaben PET scans: results from a Phase 3 trial

Training for accurate image interpretation is essential for the clinical use of β-amyloid PET imaging, but the role of interpreter training and the accuracy of the algorithm for routine visual assessment of florbetaben PET scans are unclear. The aim of this study was to test the robustness of the visual assessment method for florbetaben scans, comparing efficacy readouts across different interpreters and training methods and against a histopathology standard of truth (SoT). Methods: Analysis was based on data from an international open-label, nonrandomized, multicenter phase-3 study in patients with or without dementia (ClinicalTrials.gov: NCT01020838). Florbetaben scans were assessed visually and quantitatively, and results were compared with amyloid plaque scores. For visual assessment, either in-person training (n = 3 expert interpreters) or an electronic training method (n = 5 naïve interpreters) was used. Brain samples from participants who died during the study were used to determine the histopathologic SoT using Bielschowsky silver staining (BSS) and immunohistochemistry for β-amyloid plaques. Results: Data were available from 82 patients who died and underwent postmortem histopathology. When visual assessment results were compared with BSS + immunohistochemistry as SoT, median sensitivity was 98.2% for the in-person–trained interpreters and 96.4% for the e-trained interpreters, and median specificity was 92.3% and 88.5%, respectively. Median accuracy was 95.1% and 91.5%, respectively. On the basis of BSS only as the SoT, median sensitivity was 98.1% and 96.2%, respectively; median specificity was 80.0% and 76.7%, respectively; and median accuracy was 91.5% and 86.6%, respectively. Interinterpreter agreement (Fleiss κ) was excellent (0.89) for in-person–trained interpreters and very good (0.71) for e-trained interpreters. Median intrainterpreter agreement was 0.9 for both in-person–trained and e-trained interpreters. Visual and quantitative assessments were concordant in 88.9% of scans for in-person–trained interpreters and in 87.7% of scans for e-trained interpreters. Conclusion: Visual assessment of florbetaben images was robust in challenging scans from elderly end-of-life individuals. Sensitivity, specificity, and interinterpreter agreement were high, independent of expertise and training method. Visual assessment was accurate and reliable for detection of plaques using BSS and immunohistochemistry and well correlated with quantitative assessments

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data.

Funder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health); doi: https://doi.org/10.13039/100011272; Grant(s): 305444, 305444Funder: Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness); doi: https://doi.org/10.13039/501100003329Funder: Generalitat de Catalunya (Government of Catalonia); doi: https://doi.org/10.13039/501100002809Funder: EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj); doi: https://doi.org/10.13039/501100008530Funder: Instituto Nacional de Bioinformática ELIXIR Implementation Studies Centro de Excelencia Severo OchoaFunder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health)Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock

Impact of type 2 diabetes-associated mood disorders on the electrical properties of brain serotoninergic neurons

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