BamToCov: an efficient toolkit for sequence coverage calculations

Many genomics applications requires the calculation of nucleotide coverage of a reference or counting how many reads maps in a reference region. Here we present BamToCov, a suite of tools for rapid and flexible coverage calculations relying on a memory efficient algorithm and designed for flexible integration in bespoke pipelines. The tools of the suite will process sorted BAM or CRAM files, allowing to extract coverage information using different filtering approaches. BamToCov tools, unlike existing tools already available, have been developed to require a minimum amount of memory, to be easily integrated in workflows, and to allow for strand-specific coverage analyses. The unique coverage calculation algorithm makes it the ideal choice for the analysis of long reads alignments. The programs and their documentation are freely available at https://github.com/telatin/bamtocov.

Dynamic 18F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?

PurposeDynamic amino acid positron emission tomography (PET) has become essential in neuro-oncology, most notably for its prognostic value in the noninvasive prediction of isocitrate dehydrogenase (IDH) mutations in newly diagnosed gliomas. The 6-[18F]fluoro-l-DOPA (18F-FDOPA) kinetic model has an underlying complexity, while previous studies have predominantly used a semiquantitative dynamic analysis. Our study addresses whether a semiquantitative analysis can capture all the relevant information contained in time–activity curves for predicting the presence of IDH mutations compared to the more sophisticated graphical and compartmental models.MethodsThirty-seven tumour time–activity curves from 18F-FDOPA PET dynamic acquisitions of newly diagnosed gliomas (median age = 58.3 years, range = 20.3–79.9 years, 16 women, 16 IDH-wild type) were analyzed with a semiquantitative model based on classical parameters, with (SQ) or without (Ref SQ) a reference region, or on parameters of a fit function (SQ Fit), a graphical Logan model with input function (Logan) or reference region (Ref Logan), and a two-tissue compartmental model previously reported for 18F-FDOPA PET imaging of gliomas (2TCM). The overall predictive performance of each model was assessed with an area under the curve (AUC) comparison using multivariate analysis of all the parameters included in the model. Moreover, each extracted parameter was assessed in a univariate analysis by a receiver operating characteristic curve analysis.ResultsThe SQ model with an AUC of 0.733 for predicting IDH mutations showed comparable performance to the other models with AUCs of 0.752, 0.814, 0.693, 0.786, and 0.863, respectively corresponding to SQ Fit, Ref SQ, Logan, Ref Logan, and 2TCM (p ≥ 0.10 for the pairwise comparisons with other models). In the univariate analysis, the SQ time-to-peak parameter had the best diagnostic performance (75.7% accuracy) compared to all other individual parameters considered.ConclusionsThe SQ model circumvents the complexities of the 18F-FDOPA kinetic model and yields similar performance in predicting IDH mutations when compared to the other models, most notably the compartmental model. Our study provides supportive evidence for the routine clinical application of the SQ model for the dynamic analysis of 18F-FDOPA PET images in newly diagnosed gliomas.

Dynamic 18F-FDopa PET Imaging for Newly-diagnosed Gliomas: Is a Semi-quantitative Model Sufficient?

Purpose: Even though the semi quantitative dynamic analysis of 18F-FDOPA PET effectively and non-invasively predicts isocitrate dehydrogenase (IDH) mutations in newly-diagnosed gliomas, the underlying kinetic model of 18F-FDOPA is complex. Our current study addresses whether a semi quantitative analysis indeed captures all the clinically relevant predictive features of the more sophisticated graphical and compartmental models.Methods: Thirty-seven tumour time-activity curves from 18F-FDOPA PET dynamic acquisitions of newly-diagnosed gliomas were analysed using a semi quantitative model with (Ref SQ) or without reference region (SQ), a graphical Logan model with input function (Logan) or reference region (Ref Logan), and a two-tissue compartmental model validated for 18F-FDOPA PET imaging in gliomas (2TCM). The overall predictive performance of each model for predicting IDH mutations was assessed, by an area under the curve (AUC) comparison of multivariate analyses of all parameters included in the model.Results: SQ model with an AUC of 0.733 showed comparable performances to other models with AUCs of 0.814, 0.693, 0.786, 0.863, respectively corresponding to Ref SQ, Logan, Ref Logan and 2 TCM (p≥0.11 for the pairwise comparisons with other models). SQ time-to-peak parameter had the best diagnostic performance relative to all individual parameters with an accuracy of 75.7%.Conclusions: The SQ model circumvents the complexities of the 18F-FDOPA kinetic model and yields similar performances compared to other models most notably the compartmental model for predicting IDH mutations. This validates the application of the SQ model for the dynamic analysis of 18F-FDOPA PET images in routine clinical practice for newly-diagnosed gliomas.

Parametric Estimation of Reference Signal Intensity for Semi-Quantification of Tau Deposition: A Flortaucipir and [18F]-APN-1607 Study

BackgroundTau positron emission tomography (PET) imaging can reveal the pathophysiology and neurodegeneration that occurs in Alzheimer’s disease (AD) in vivo. The standardized uptake value ratio (SUVR) is widely used for semi-quantification of tau deposition but is susceptible to disturbance from the reference region and the partial volume effect (PVE). To overcome this problem, we applied the parametric estimation of reference signal intensity (PERSI) method—which was previously evaluated for flortaucipir imaging—to two tau tracers, flortaucipir and [18F]-APN-1607.MethodsTwo cohorts underwent tau PET scanning. Flortaucipir PET imaging data for cohort I (65 healthy controls [HCs], 60 patients with mild cognitive impairment [MCI], and 12 AD patients) were from the AD Neuroimaging Initiative database. [18F]-APN-1607 ([18F]-PM-PBB3) PET imaging data were for Cohort II, which included 21 patients with a clinical diagnosis of amyloid PET-positive AD and 15 HCs recruited at Huashan Hospital. We used white matter (WM) postprocessed by PERSI (PERSI-WM) as the reference region and compared this with the traditional semi-quantification method that uses the whole cerebellum as the reference. SUVRs were calculated for regions of interest including the frontal, parietal, temporal, and occipital lobes; anterior and posterior cingulate; precuneus; and Braak I/II (entorhinal cortex and hippocampus). Receiver operating characteristic (ROC) curve analysis and effect sizes were used to compare the two methods in terms of ability to discriminate between different clinical groups.ResultsIn both cohorts, regional SUVR determined using the PERSI-WM method was superior to using the cerebellum as reference region for measuring tau retention in AD patients (e.g., SUVR of the temporal lobe: flortaucipir, 1.08 ± 0.17 and [18F]-APN-1607, 1.57 ± 0.34); and estimates of the effect size and areas under the ROC curve (AUC) indicated that it also increased between-group differences (e.g., AUC of the temporal lobe for HC vs AD: flortaucipir, 0.893 and [18F]-APN-1607: 0.949).ConclusionThe PERSI-WM method significantly improves diagnostic discrimination compared to conventional approach of using the cerebellum as a reference region and can mitigate the PVE; it can thus enhance the efficacy of semi-quantification of multiple tau tracers in PET scanning, making it suitable for large-scale clinical application.

Multi-Atlas MRI-Based Striatum Segmentation for 123I-FP-CIT SPECT (DAT-SPECT) Compared With the Bolt Method and SPECT-Atlas-Based Segmentation Method Toward the Accurate Diagnosis of Parkinson's Disease/Syndrome

Aims: This study aimed to analyze the performance of multi-atlas MRI-based parcellation for 123I-FP-CIT SPECT (DAT-SPECT) in healthy volunteers. The proposed method was compared with the SPECT-atlas-based and Bolt methods. 18F-FE-PE2I-PET (DAT-PET) was used as a reference.Methods: Thirty healthy subjects underwent DAT-SPECT, DAT-PET, and 3D-T1WI-MRI. We calculated the striatum uptake ratio (SUR/SBR), caudate uptake ratio (CUR), and putamen uptake ratio (PUR) for DAT-SPECT using the multi-atlas MRI-based method, SPECT-atlas-based method, and Bolt method. In the multi-atlas MRI-based method, the cerebellum, occipital cortex, and whole-brain were used as reference regions. The correlation of age with DAT-SPECT activity and the correlations of SUR/SBR, CUR, and PUR between DAT-SPECT and DAT-PET were calculated by each of the three methods.Results: The correlation between age and SUR/SBR for DAT-SPECT based on the multi-atlas MRI-based method was comparable to that based on the SPECT-atlas-based method (r = −0.441 to −0.496 vs. −0.488). The highest correlation between DAT-SPECT and DAT-PET was observed using the multi-atlas MRI-based method with the occipital lobe defined as the reference region compared with the SPECT-atlas-based and Bolt methods (SUR, CUR, and PUR: 0.687, 0.723, and 0.676 vs. 0.698, 0.660, and 0.616 vs. 0.655).Conclusion: Multi-atlas MRI-based parcellation with the occipital lobe defined as the reference region was at least comparable to the clinical methods.

Changes in Urologic Operative Practice at the Beginning of the COVID-19 Pandemic in a Large, National Cohort

ObjectiveTo analyze population-level changes in operative practice since the onset of the COVID-19 pandemic to contextualize observations made by individual practices and optimize future responses.Materials and MethodsThis US retrospective analysis used the Premier Perspectives Database. We investigated changes in operative volume through March 2020. Baseline operative volume for urologic surgery was calculated using data from the preceding 12 months and compared on a total and by procedure basis. Multivariable linear regression was used to identify hospital-level predictors of change in response to the pandemic.ResultsAt baseline, we captured 23,788 urologic procedural encounters per month as compared with 19,071 during March 2020– a 19.9% decrease. Urologic oncology-related cases were relatively preserved as compared to others (average change in March 2020: +1.1% versus -32.2%). Northeastern (β = -5.66, 95% confidence interval [CI]: -10.2 to -1.18, p = 0.013) and Midwestern hospitals (β = -4.17, 95% CI: -7.89 to -0.45, p = 0.027; both with South as reference region), and those with an increasing percentage of patients insured by Medicaid (β= -0.17 per percentage point, 95% CI: -0.33 to -0.01, p = 0.04) experienced a significantly larger decrease in volume.ConclusionsThere was a 20% decline in urologic operative volume in March 2020, compared with baseline, that preferentially affected hospitals serving Medicaid patients, and those in Northeast and Midwest. In the face of varying mandates on elective surgery, widespread declines in operative volume may also represent hesitancy on behalf of patients to interface with healthcare during the pandemic.

A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [18F]flutemetamol amyloid PET images

Background [18F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation as either negative (equating to none or sparse amyloid plaques) or amyloid positive (equating to moderate or frequent plaques). Quantitation is however fundamental to the practice of nuclear medicine and hence can be used to supplement amyloid reading methodology especially in unclear cases. Methods A total of 2770 [18F]flutemetamol images were collected from 3 clinical studies and 6 research cohorts with available visual reading of [18F]flutemetamol and quantitative analysis of images. These were assessed further to examine both the discordance and concordance between visual and quantitative imaging primarily using thresholds robustly established using pathology as the standard of truth. Scans covered a wide range of cases (i.e. from cognitively unimpaired subjects to patients attending the memory clinics). Methods of quantifying amyloid ranged from using CE/510K cleared marked software (e.g. CortexID, Brass), to other research-based methods (e.g. PMOD, CapAIBL). Additionally, the clinical follow-up of two types of discordance between visual and quantitation (V+Q- and V-Q+) was examined with competing risk regression analysis to assess possible differences in prediction for progression to Alzheimer’s disease (AD) and other diagnoses (OD). Results Weighted mean concordance between visual and quantitation using the autopsy-derived threshold was 94% using pons as the reference region. Concordance from a sensitivity analysis which assessed the maximum agreement for each cohort using a range of cut-off values was also estimated at approximately 96% (weighted mean). Agreement was generally higher in clinical cases compared to research cases. V-Q+ discordant cases were 11% more likely to progress to AD than V+Q- for the SUVr with pons as reference region. Conclusions Quantitation of amyloid PET shows a high agreement vs binary visual reading and also allows for a continuous measure that, in conjunction with possible discordant analysis, could be used in the future to identify possible earlier pathological deposition as well as monitor disease progression and treatment effectiveness.

Supervised clustering for TSPO PET imaging

Purpose This technical note seeks to act as a practical guide for implementing a supervised clustering algorithm (SVCA) reference region approach and to explain the main strengths and limitations of the technique in the context of 18-kilodalton translocator protein (TSPO) positron emission tomography (PET) studies in experimental medicine. Background TSPO PET is the most widely used imaging technique for studying neuroinflammation in vivo in humans. Quantifying neuroinflammation with PET can be a challenging and invasive procedure, especially in frail patients, because it often requires blood sampling from an arterial catheter. A widely used alternative to arterial sampling is SVCA, which identifies the voxels with minimal specific binding in the PET images, thus extracting a pseudo-reference region for non-invasive quantification. Unlike other reference region approaches, SVCA does not require specification of an anatomical reference region a priori, which alleviates the limitation of TSPO contamination in anatomically-defined reference regions in individuals with underlying inflammatory processes. Furthermore, SVCA can be applied to any TSPO PET tracer across different neurological and neuropsychiatric conditions, providing noninvasivequantification of TSPO expression. Methods We provide an overview of the development of SVCA as well as step-by-step instructions for implementing SVCA with suggestions for specific settings. We review the literature on SVCAapplications using first- and second- generation TSPO PET tracers and discuss potential clinically relevant limitations and applications. Conclusions The correct implementation of SVCA can provide robust and reproducible estimates of brain TSPO expression. This review encourages the standardisation of SVCA methodology in TSPO PET analysis, ultimately aiming to improve replicability and comparability across study sites.