Characteristics of the Sexual Networks of Men Who Have Sex With Men in Montréal, Toronto, and Vancouver: Insights from Canada's 2022 Mpox Outbreak.

BACKGROUND: The 2022-2023 global mpox outbreak disproportionately affected gay, bisexual, and other men who have sex with men (GBM). We investigated differences in GBM's sexual partner distributions across Canada's 3 largest cities and over time, and how they shaped transmission. METHODS: The Engage Cohort Study (2017-2023) recruited GBM via respondent-driven sampling in Montréal, Toronto, and Vancouver (n = 2449). We compared reported sexual partner distributions across cities and periods: before COVID-19 (2017-2019), pandemic (2020-2021), and after lifting of restrictions (2021-2023). We used Bayesian regression and poststratification to model partner distributions. We estimated mpox's basic reproduction number (R0) using a risk-stratified compartmental model. RESULTS: Pre-COVID-19 pandemic distributions were comparable: fitted average partners (past 6 months) were 10.4 (95% credible interval: 9.4-11.5) in Montréal, 13.1 (11.3-15.1) in Toronto, and 10.7 (9.5-12.1) in Vancouver. Sexual activity decreased during the pandemic and increased after lifting of restrictions, but remained below prepandemic levels. Based on reported cases, we estimated R0 of 2.4 to 2.7 and similar cumulative incidences (0.7%-0.9%) across cities. CONCLUSIONS: Similar sexual partner distributions may explain comparable R0 and cumulative incidence across cities. With potential for further recovery in sexual activity, mpox vaccination and surveillance strategies should be maintained.

Examining the Influence of Imbalanced Social Contact Matrices in Epidemic Models.

Transmissible infections such as those caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread according to who contacts whom. Therefore, many epidemic models incorporate contact patterns through contact matrices. Contact matrices can be generated from social contact survey data. However, the resulting matrices are often imbalanced, such that the total number of contacts reported by group A with group B do not match those reported by group B with group A. We examined the theoretical influence of imbalanced contact matrices on the estimated basic reproduction number (R0). We then explored how imbalanced matrices may bias model-based epidemic projections using an illustrative simulation model of SARS-CoV-2 with 2 age groups (<15 and ≥15 years). Models with imbalanced matrices underestimated the initial spread of SARS-CoV-2, had later time to peak incidence, and had smaller peak incidence. Imbalanced matrices also influenced cumulative infections observed per age group, as well as the estimated impact of an age-specific vaccination strategy. Stratified transmission models that do not consider contact balancing may generate biased projections of epidemic trajectory and the impact of targeted public health interventions. Therefore, modeling studies should implement and report methods used to balance contact matrices for stratified transmission models.

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response.

PURPOSE OF REVIEW: Big Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response. RECENT FINDINGS: Social, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.

Testing Whether Higher Contact Among the Vaccinated Can Be a Mechanism for Observed Negative Vaccine Effectiveness.

Evidence from early observational studies suggested negative vaccine effectiveness (${V}_{Eff}$) for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant. Since true ${V}_{Eff}$ is unlikely to be negative, we explored how differences in contact among vaccinated persons (e.g., potentially from the implementation of vaccine mandates) could lead to observed negative ${V}_{Eff}$. Using a susceptible-exposed-infectious-recovered (SEIR) transmission model, we examined how vaccinated-contact heterogeneity, defined as an increase in the contact rate only between vaccinated individuals, interacted with 2 mechanisms of vaccine efficacy: vaccine efficacy against susceptibility ($V{E}_S$) and vaccine efficacy against infectiousness ($V{E}_I$), to produce underestimated and in some cases, negative measurements of ${V}_{Eff}$. We found that vaccinated-contact heterogeneity led to negative estimates when $V{E}_I$, and especially $V{E}_S$, were low. Moreover, we determined that when contact heterogeneity was very high, ${V}_{Eff}$ could still be underestimated given relatively high vaccine efficacies (0.7), although its effect on ${V}_{Eff}$ was strongly reduced. We also found that this contact heterogeneity mechanism generated a signature temporal pattern: The largest underestimates and negative measurements of ${V}_{Eff}$ occurred during epidemic growth. Overall, our research illustrates how vaccinated-contact heterogeneity could have feasibly produced negative measurements during the Omicron period and highlights its general ability to bias observational studies of ${V}_{Eff}$.

Maximizing the impact of limited vaccine supply under different early epidemic conditions: a 2-city modelling analysis of monkeypox virus transmission among men who have sex with men.

BACKGROUND: The current global monkeypox virus (MPXV) outbreak has disproportionately affected gay, bisexual and other men who have sex with men (GBMSM). Given that many jurisdictions have been faced with limited supplies of MPXV vaccine, we aimed to explore optimal vaccine allocation between 2 linked GBMSM transmission networks over a short-term time horizon, across several epidemic conditions. METHODS: We constructed a deterministic compartmental MPXV transmission model. We parameterized the model to reflect 2 representative, partially connected GBMSM sexual networks ( cities), using 2022 data from Ontario. We simulated a roll-out of 5000 vaccine doses over 30 days that started 45 days after epidemic seeding with 10 imported cases. Within this model, we varied the relative city (network) sizes, epidemic potentials (R 0), between-city mixing and distribution of seed cases between cities. For each combination of varied factors, we identified the allocation of doses between cities that maximized infections averted by day 90. RESULTS: Under our modelling assumptions, we found that a limited MPXV vaccine supply could generally avert more early infections when prioritized to networks that were larger, had more initial infections or had greater R 0. Greater between-city mixing decreased the influence of initial seed cases and increased the influence of city R 0 on optimal allocation. Under mixed conditions (e.g., fewer seed cases but greater R 0), optimal allocation required doses shared between cities. INTERPRETATION: In the context of the current global MPXV outbreak, we showed that prioritization of a limited supply of vaccines based on network-level factors can help maximize infections averted during an emerging epidemic. Such prioritization should be grounded in an understanding of context-specific risk drivers and should acknowledge potential connectedness of multiple transmission networks.

Population-Level Sexual Mixing According to HIV Status and Preexposure Prophylaxis Use Among Men Who Have Sex With Men in Montreal, Canada: Implications for HIV Prevention.

Using cross-sectional survey data (Engage, 2017-2018) from 1,137 men who have sex with men, ≥16 years old, in Montreal, we compared observed human immunodeficiency virus (HIV) seroconcordance in previous-6-months' sexual partnerships with what would have been observed by chance if zero individuals serosorted. Of 5 recent partnerships where both individuals were HIV-negative, we compared observed concordance in preexposure prophylaxis (PrEP) use with the counterfactual if zero individuals selected partners based on PrEP use. We estimated the concordance by chance using a balancing-partnerships approach assuming proportionate mixing. HIV-positive respondents had a higher proportion of HIV-positive partners (66.4%, 95% confidence interval (CI): 64.0, 68.6) than by chance (23.9%, 95% CI: 23.1, 24.7). HIV-negative respondents (both on and not on PrEP) had higher proportions of HIV-negative partners (82.9% (95% CI: 81.1, 84.7) and 90.7% (95% CI: 89.6, 91.7), respectively) compared with by chance (76.1%, 95% CI: 75.3, 76.9); however, those on PrEP had a higher proportion of HIV-positive partners than those not on PrEP (17.1% (95% CI: 15.3, 18.9) vs. 9.3% (95% CI: 8.3, 10.4). Those on PrEP also had a higher proportion of partners on PrEP among their HIV-negative partners (50.6%, 95% CI: 42.5, 58.8) than by chance (28.5%, 95% CI: 27.5, 29.4). The relationship between PrEP and sexual-mixing patterns demonstrated by less population-level serosorting among those on PrEP and PrEP-matching warrants consideration during PrEP roll-out.

Enhancing your practice: debriefing in interventional radiology.

LEARNING OBJECTIVES: Review the history of debriefing and provide an Interventional Radiologist (IR) specific framework for leading an effective debrief. BACKGROUND: A debrief is often regarded as a meeting with persons who were involved in a stressful, traumatic and/or emotionally challenging situation to review processes, communicate concerns or gather feedback. The goals of these sessions can be for learning/quality improvement (QI) or psychological/emotional support, or a mix of both. Debriefing after tough situations has become a standard tool of many medical specialties, such as surgery, critical care and emergency medicine, with specialty specific literature available. However, there is a paucity of Interventional Radiology specific literature available for debriefing techniques. CLINICAL FINDINGS/PROCEDURE DETAILS: We will review the history and types of debriefing and why a debrief could be considered. We will provide a framework for leading a successful debrief in Interventional Radiology. CONCLUSION: Debriefing can be a useful tool for learning and QI as well as psychological or emotional support after a challenging or tough situation. Debriefing can address multiple variables and can stylistically be tailored to suit specific needs. IRs have an opportunity to take a leadership role in debriefing, providing comfort and quality improvement through communication and support.

Quantifying the impact of cascade inequalities: a modelling study on the prevention impacts of antiretroviral therapy scale-up in Eswatini.

Background: Inequalities in the antiretroviral therapy (ART) cascade across subpopulations remain an ongoing challenge in the global HIV response. Eswatini achieved the UNAIDS 95-95-95 targets by 2020, with differentiated programs to minimize inequalities across subpopulations, including for female sex workers (FSW) and their clients. We sought to estimate additional HIV infections expected in Eswatini if cascade scale-up had not been equal, and under which epidemic conditions these inequalities could have the largest influence. Methods: Drawing on population-level and FSW-specific surveys in Eswatini, we developed a compartmental model of heterosexual HIV transmission which included eight subpopulations and four sexual partnership types. We calibrated the model to stratified HIV prevalence, incidence, and ART cascade data. Taking observed cascade scale-up in Eswatini as the base-case-reaching 95-95-95 in the overall population by 2020-we defined four counterfactual scenarios in which the population overall reached 80-80-90 by 2020, but where FSW, clients, both, or neither were disproportionately left behind, reaching only 60-40-80. We quantified relative additional cumulative HIV infections by 2030 in counterfactual vs base-case scenarios. We further estimated linear effects of viral suppression gap among FSW and clients on additional infections by 2030, plus effect modification by FSW/client population sizes, rates of turnover, and HIV prevalence ratios. Results: Compared with the base-case scenario, leaving behind neither FSW nor their clients led to the fewest additional infections by 2030: median (95% credible interval) 14.9 (10.4, 18.4)% vs 26.3 (19.7, 33.0)% if both were left behind-a 73 (40, 149)% increase. The effect of lower cascade on additional infections was larger for clients vs FSW, and both effects increased with population size and relative HIV incidence. Conclusions: Inequalities in the ART cascade across subpopulations can undermine the anticipated prevention impacts of cascade scale-up. As Eswatini has shown, addressing inequalities in the ART cascade, particularly those that intersect with high transmission risk, could maximize incidence reductions from cascade scale-up.

Adjusting for hidden biases in sexual behaviour data: a mechanistic approach.

Background: Two required inputs to mathematical models of sexually transmitted infections are the average duration in epidemiological risk states (e.g., selling sex) and the average rates of sexual partnership change. These variables are often only available as aggregate estimates from published cross-sectional studies, and may be subject to distributional, sampling, censoring, and measurement biases. Methods: We explore adjustments for these biases using aggregate estimates of duration in sex work and numbers of reported sexual partners from a published 2011 survey of female sex worker in Eswatini. We develop adjustments from first principles, and construct Bayesian hierarchical models to reflect our mechanistic assumptions about the bias-generating processes. Results: We show that different mechanisms of bias for duration in sex work may "cancel out" by acting in opposite directions, but that failure to consider some mechanisms could over- or underestimate duration in sex work by factors approaching 2. We also show that conventional interpretations of sexual partner numbers are biased due to implicit assumptions about partnership duration, but that unbiased estimators of partnership change rate can be defined that explicitly incorporate a given partnership duration. We highlight how the unbiased estimator is most important when the survey recall period and partnership duration are similar in length. Conclusions: While we explore these bias adjustments using a particular dataset, and in the context of deriving inputs for mathematical modelling, we expect that our approach and insights would be applicable to other datasets and motivations for quantifying sexual behaviour data.

Corrigendum to "Estimating effective reproduction number using generation time versus serial interval, with application to covid-19 in the Greater Toronto Area, Canada" [Infectious Disease Modelling 5 (2020) 889-896].

[This corrects the article DOI: 10.1016/j.idm.2020.10.009.].

Risk heterogeneity in compartmental HIV transmission models of ART as prevention in Sub-Saharan Africa: A scoping review.

BACKGROUND: Transmission models provide complementary evidence to clinical trials about the potential population-level incidence reduction attributable to ART (ART prevention impact). Different modelling assumptions about risk heterogeneity may influence projected ART prevention impacts. We sought to review representations of risk heterogeneity in compartmental HIV transmission models applied to project ART prevention impacts in Sub-Saharan Africa. METHODS: We systematically reviewed studies published before January 2020 that used non-linear compartmental models of sexual HIV transmission to simulate ART prevention impacts in Sub-Saharan Africa. We summarized data on model structure/assumptions (factors) related to risk and intervention heterogeneity, and explored multivariate ecological associations of ART prevention impacts with modelled factors. RESULTS: Of 1384 search hits, 94 studies were included. 64 studies considered sexual activity stratification and 39 modelled at least one key population. 21 studies modelled faster/slower ART cascade transitions (HIV diagnosis, ART initiation, or cessation) by risk group, including 8 with faster and 4 with slower cascade transitions among key populations versus the wider population. In ecological analysis of 125 scenarios from 40 studies (subset without combination intervention), scenarios with risk heterogeneity that included turnover of higher risk groups were associated with smaller ART prevention benefits. Modelled differences in ART cascade across risk groups also influenced the projected ART benefits, including: ART prioritized to key populations was associated with larger ART prevention benefits. Of note, zero of these 125 scenarios considered lower ART coverage among key populations. CONCLUSION: Among compartmental transmission models applied to project ART prevention impacts in Sub-Saharan Africa, representations of risk heterogeneity and projected impacts varied considerably. Inclusion/exclusion of risk heterogeneity with turnover, and intervention heterogeneity across risk groups could influence the projected impacts of ART scale-up. These findings highlight a need to capture risk heterogeneity with turnover and cascade heterogeneity when projecting ART prevention impacts.

Adaptive data-driven age and patch mixing in contact networks with recurrent mobility.

Infectious disease transmission models often stratify populations by age and geographic patches. Contact patterns between age groups and patches are key parameters in such models. Arenas et al. (2020) develop an approach to simulate contact patterns associated with recurrent mobility between patches, such as due to work, school, and other regular travel. Using their approach, mixing between patches is greater than mobility data alone would suggest, because individuals from patches A and B can form contacts if they meet in patch C. We build upon their approach to address three potential gaps that remain, outlined in the bullets below. We describe the steps required to implement our approach in detail, and present step-wise results of an example application to generate contact matrices for SARS-CoV-2 transmission modelling in Ontario, Canada. We also provide methods for deriving the mobility matrix based on GPS mobility data (appendix).•Our approach includes a distribution of contacts by age that is responsive to the underlying age distributions of the mixing populations.•Our approach maintains different age mixing patterns by contact type, such that changes to the numbers of different types of contacts are appropriately reflected in changes to overall age mixing patterns.•Our approach distinguishes between two mixing pools associated with each patch, with possible implications for the overall connectivity of the population: the home pool, in which contacts can only be formed with other individuals residing in the same patch, and the travel pool, in which contacts can be formed with some residents of, and any other visitors to the patch.

Estimating the epidemic consequences of HIV prevention gaps among key populations.

INTRODUCTION: HIV epidemic appraisals are used to characterize heterogeneity and inequities in the context of the HIV pandemic and the response. However, classic measures used in appraisals have been shown to underestimate disproportionate risks of onward transmission, particularly among key populations. In response, a growing number of modelling studies have quantified the consequences of unmet prevention and treatment needs (prevention gaps) among key populations as a transmission population attributable fraction over time (tPAFt ). To aid its interpretation and use by programme implementers and policy makers, we outline and discuss a conceptual framework for understanding and estimating the tPAFt via transmission modelling as a measure of onward transmission risk from HIV prevention gaps; and discuss properties of the tPAFt . DISCUSSION: The distribution of onward transmission risks may be defined by who is at disproportionate risk of onward transmission, and under which conditions. The latter reflects prevention gaps, including secondary prevention via treatment: the epidemic consequences of which may be quantified by the tPAFt . Steps to estimating the tPAFt include parameterizing the acquisition and onward transmission risks experienced by the subgroup of interest, defining the most relevant counterfactual scenario, and articulating the time-horizon of analyses and population among whom to estimate the relative difference in cumulative transmissions; such steps could reflect programme-relevant questions about onward transmission risks. Key properties of the tPAFt include larger onward transmission risks over longer time-horizons; seemingly mutually exclusive tPAFt measures summing to greater than 100%; an opportunity to quantify the magnitude of disproportionate onward transmission risks with a per-capita tPAFt ; and that estimates are conditional on what has been achieved so far in reducing prevention gaps and maintaining those conditions moving forward as the status quo. CONCLUSIONS: The next generation of HIV epidemic appraisals has the potential to support a more specific HIV response by characterizing heterogeneity in disproportionate risks of onward transmission which are defined and conditioned on the past, current and future prevention gaps across subsets of the population.

Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis.

OBJECTIVES: HIV pre-exposure prophylaxis (PrEP) may change serosorting patterns. We examined the influence of serosorting on the population-level HIV transmission impact of PrEP, and how impact could change if PrEP users stopped serosorting. DESIGN: We developed a compartmental HIV transmission model parameterized with bio-behavioural and HIV surveillance data among MSM in Canada. METHODS: We separately fit the model with serosorting and without serosorting [counterfactual; sero-proportionate mixing (random partner-selection proportional to availability by HIV status)], and reproduced stable HIV epidemics with HIV-prevalence 10.3-24.8%, undiagnosed fraction 4.9-15.8% and treatment coverage 82.5-88.4%. We simulated PrEP-intervention reaching stable pre-specified coverage by year-one and compared absolute difference in relative HIV-incidence reduction 10 years post-intervention (PrEP-impact) between models with serosorting vs. sero-proportionate mixing; and counterfactual scenarios when PrEP users immediately stopped vs. continued serosorting. We examined sensitivity of results to PrEP-effectiveness (44-99%; reflecting varying dosing or adherence levels) and coverage (10-50%). RESULTS: Models with serosorting predicted a larger PrEP-impact than models with sero-proportionate mixing under all PrEP-effectiveness and coverage assumptions [median (interquartile range): 8.1% (5.5-11.6%)]. PrEP users' stopping serosorting reduced PrEP-impact compared with when PrEP users continued serosorting: reductions in PrEP-impact were minimal [2.1% (1.4-3.4%)] under high PrEP-effectiveness (86-99%); however, could be considerable [10.9% (8.2-14.1%)] under low PrEP effectiveness (44%) and high coverage (30-50%). CONCLUSION: Models assuming sero-proportionate mixing may underestimate population-level HIV-incidence reductions due to PrEP. PrEP-mediated changes in serosorting could lead to programmatically important reductions in PrEP-impact under low PrEP-effectiveness. Our findings suggest the need to monitor sexual mixing patterns to inform PrEP implementation and evaluation.

Estimating effective reproduction number using generation time versus serial interval, with application to covid-19 in the Greater Toronto Area, Canada.

BACKGROUND: The effective reproduction number R e (t) is a critical measure of epidemic potential. R e (t) can be calculated in near real time using an incidence time series and the generation time distribution: the time between infection events in an infector-infectee pair. In calculating R e (t), the generation time distribution is often approximated by the serial interval distribution: the time between symptom onset in an infector-infectee pair. However, while generation time must be positive by definition, serial interval can be negative if transmission can occur before symptoms, such as in covid-19, rendering such an approximation improper in some contexts. METHODS: We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of R e (t) for covid-19 in the Greater Toronto Area of Canada using: negative-permitting versus non-negative serial interval distributions, versus the inferred generation time distribution. RESULTS: We estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of R e (t) due to larger mean, while negative-permitting serial interval distribution caused underestimation of R e (t) due to larger variance. IMPLICATIONS: Approximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating R e (t) may result in over or underestimation of transmission potential, respectively.

Contribution of high risk groups' unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysis.

BACKGROUND: Epidemic models of sexually transmitted infections (STIs) are often used to characterize the contribution of risk groups to overall transmission by projecting the transmission population attributable fraction (tPAF) of unmet prevention and treatment needs within risk groups. However, evidence suggests that STI risk is dynamic over an individual's sexual life course, which manifests as turnover between risk groups. We sought to examine the mechanisms by which turnover influences modelled projections of the tPAF of high risk groups. METHODS: We developed a unifying, data-guided framework to simulate risk group turnover in deterministic, compartmental transmission models. We applied the framework to an illustrative model of an STI and examined the mechanisms by which risk group turnover influenced equilibrium prevalence across risk groups. We then fit a model with and without turnover to the same risk-stratified STI prevalence targets and compared the inferred level of risk heterogeneity and tPAF of the highest risk group projected by the two models. RESULTS: The influence of turnover on group-specific prevalence was mediated by three main phenomena: movement of previously high risk individuals with the infection into lower risk groups; changes to herd effect in the highest risk group; and changes in the number of partnerships where transmission can occur. Faster turnover led to a smaller ratio of STI prevalence between the highest and lowest risk groups. Compared to the fitted model without turnover, the fitted model with turnover inferred greater risk heterogeneity and consistently projected a larger tPAF of the highest risk group over time. IMPLICATIONS: If turnover is not captured in epidemic models, the projected contribution of high risk groups, and thus, the potential impact of prioritizing interventions to address their needs, could be underestimated. To aid the next generation of tPAF models, data collection efforts to parameterize risk group turnover should be prioritized.

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge.

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.

Voxel-Wise Logistic Regression and Leave-One-Source-Out Cross Validation for white matter hyperintensity segmentation.

Many algorithms have been proposed for automated segmentation of white matter hyperintensities (WMH) in brain MRI. Yet, broad uptake of any particular algorithm has not been observed. In this work, we argue that this may be due to variable and suboptimal validation data and frameworks, precluding direct comparison of methods on heterogeneous data. As a solution, we present Leave-One-Source-Out Cross Validation (LOSO-CV), which leverages all available data for performance estimation, and show that this gives more realistic (lower) estimates of segmentation algorithm performance on data from different scanners. We also develop a FLAIR-only WMH segmentation algorithm: Voxel-Wise Logistic Regression (VLR), inspired by the open-source Lesion Prediction Algorithm (LPA). Our variant facilitates more accurate parameter estimation, and permits intuitive interpretation of model parameters. We illustrate the performance of the VLR algorithm using the LOSO-CV framework with a dataset comprising freely available data from several recent competitions (96 images from 7 scanners). The performance of the VLR algorithm (median Similarity Index of 0.69) is compared to its LPA predecessor (0.58), and the results of the VLR algorithm in the 2017 WMH Segmentation Competition are also presented.

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure.

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

Does body mass index predict tracheal airway size?

OBJECTIVES/HYPOTHESIS: To determine the relationship between body mass index along with other anthropomorphic variables as they relate to tracheal airway dimensions. STUDY DESIGN: Retrospective case series. METHODS: This was a radiographic study of 123 consecutive hospitalized patients undergoing tracheotomy over a 4-year period (2007-2011). We measured airway dimensions in axial computed tomography imaging and made comparisons with height, weight, body mass index, gender, and age. Measurements were taken at the first tracheal ring level including anterior-posterior length, width, and calculated area. We expected higher body mass index not to be a good predictor of larger airway dimensions. RESULTS: The linear regression model showed body mass index was significantly inversely related to tracheal width after controlling for gender and age (P = .0389). For every 1 kg/m(2) increase in body mass index, the tracheal width decreased by 0.05 mm. There was a trend for airway area to diminish with increasing body mass index. CONCLUSIONS: These results are consistent with the hypothesis that obese patients do not have larger airways. Our study indicated a trend toward smaller airways as body mass index increased. Specifically, as body mass index increases, tracheal width appears to decrease. This information should help medical professionals avoid the tendency to use a larger tube to secure the airway of an obese patient. Hopefully, this will result in further research into the field and may prevent future airway injuries in a society where obesity has become epidemic. LEVEL OF EVIDENCE: 4 Laryngoscope, 125:1093-1097, 2015.