[Biological and genome characteristics of a novel broad host-range phage ΦEP1 infecting enteroinvasive Escherichia coli].

We analyzed the biological and genome characteristics of a phage infecting enteroinvasive Escherichia coli (EIEC), aiming to provide resources and a reference for the prevention and treatment of EIEC. With the EIEC preserved in our laboratory as the host bacterium, one strain of phage was isolated from the effluent sample from a chicken farm in Huzhou, Zhejiang and named ΦEP1. The titer, optimal multiplicity of infection, one-step growth curve, temperature, pH value, chloroform and bile salt sensitivity of ΦEP1 were determined by the double-layer agar plate method. The morphology of the phage was observed by transmission electron microscopy. The biocontrol effects of ΦEP1 in different food matrixes and the protective effect of this phage on Caco-2 cells were tested. The phage ΦEP1 showed the optimal multiplicity of infection of 0.1, the titer of 1.3×1010 PFU/mL, strong tolerance to temperature, pH, chloroform, and bile salt, and a broad host spectrum. Furthermore, it expressed lysis activity against multiple strains of multiple antibiotic-resistant pathogenic E. coli and Shigella with different serotypes. Phage ΦEP1 had an incubation period of 10 min, an outbreak period of 80 min, and an outbreak volume of 48 PFU/cell. According to the morphology observed by transmission electron microscopy, phage ΦEP1 belonged to the order of Caudovirales, and it had a good protective effect on Caco-2 cells. Phage ΦEP1 had a genome of 87 182 bp with the GC content of 39.80%, 128 putative open reading frames, and no antibiotic resistance genes or virulence genes. ΦEP1 inhibited the growth of EIEC in artificially contaminated milk and beef and eliminated EIEC in cell protection experiments. It significantly increased the survival rate of Caco-2 cells and down-regulated the expression of interleukin (IL)-6 and IL-1ß to reduce inflammation. We obtained an EIEC-targeting phage ΦEP1 with a high titer and strong tolerance to the environment, which provided a basis for the application of phages in food preservation and other fields.

Triple challenges - Small sample size in both exposure and control groups to scan rare maternal outcomes in a signal identification approach: A simulation study.

There is a dearth of safety data on maternal outcomes after perinatal medication exposure. Data-mining for unexpected adverse event occurrence in existing datasets is a potentially useful approach. One method, the Poisson tree-based scan statistic (TBSS), assumes that the expected outcome counts, based on incidence of outcomes in the control group, are estimated without error. This assumption may be difficult to satisfy with a small control group. Our simulation study evaluated the effect of imprecise incidence proportions from the control group on TBSS' ability to identify maternal outcomes in pregnancy research. We simulated base case analyses with "true" expected incidence proportions and compared these to imprecise incidence proportions derived from sparse control samples. We varied parameters impacting Type I error and statistical power (exposure group size, outcome's incidence proportion, and effect size). We found that imprecise incidence proportions generated by a small control group resulted in inaccurate alerting, inflation of Type I error, and removal of very rare outcomes for TBSS analysis due to "zero" background counts. Ideally, the control size should be at least several times larger than the exposure size to limit the number of false positive alerts and retain statistical power for true alerts.

Statistical methods to control for confounders in rare disease settings that use external control.

In the drug development for rare disease, the number of treated subjects in the clinical trial is often very small, whereas the number of external controls can be relatively large. There is no clear guidance on choosing an appropriate statistical method to control baseline confounding in this situation. To fill this gap, we conduct extensive simulations to evaluate the performance of commonly used matching and weighting methods as well as the more recently developed targeted maximum likelihood estimation (TMLE) and cardinality matching in small sample settings, mimicking the motivating data from a pediatric rare disease. Among the methods examined, the performance of coarsened exact matching (CEM) and TMLE are relatively robust under various model specifications. CEM is only feasible when the number of controls far exceeds the number of treated, whereas TMLE has better performance with less extreme treatment allocation ratios. Our simulations suggest bootstrap is useful for variance estimation in small samples after matching.

Statistical methods for handling missing data to align with treatment policy strategy.

The International Council for Harmonization (ICH) E9(R1) addendum recommends choosing an appropriate estimand based on the study objectives in advance of trial design. One defining attribute of an estimand is the intercurrent event, specifically what is considered an intercurrent event and how it should be handled. The primary objective of a clinical study is usually to assess a product's effectiveness and safety based on the planned treatment regimen instead of the actual treatment received. The estimand using the treatment policy strategy, which collects and analyzes data regardless of the occurrence of intercurrent events, is usually utilized. In this article, we explain how missing data can be handled using the treatment policy strategy from the authors' viewpoint in connection with antihyperglycemic product development programs. The article discusses five statistical methods to impute missing data occurring after intercurrent events. All five methods are applied within the framework of the treatment policy strategy. The article compares the five methods via Markov Chain Monte Carlo simulations and showcases how three of these five methods have been applied to estimate the treatment effects published in the labels for three antihyperglycemic agents currently on the market.

Retrieved-Dropout-Based multiple imputation for time-to-event data in cardiovascular outcome trials.

Recently, retrieved-dropout-based multiple imputation has been used in some therapeutic areas to address the treatment policy estimand, mostly for continuous endpoints. In this approach, data from subjects who discontinued study treatment but remained in study were used to construct a model for multiple imputation for the missing data of subjects in the same treatment arm who discontinued study. We extend this approach to time-to-event endpoints and provide a practical guide for its implementation. We use a cardiovascular outcome trial dataset to illustrate the method and compare the results with those from Cox proportional hazard and reference-based multiple imputation methods.

Local field potentials identify features of cortico-hippocampal communication impacted by stroke and environmental enrichment therapy.

Objective. Cognitive and memory impairments are common sequelae after stroke, yet how middle cerebral artery (MCA) stroke chronically affects the neural activity of the hippocampus, a brain region critical for memory but remote from the stroke epicenter, is poorly understood. Environmental enrichment (EE) improves cognition following stroke; however, the electrophysiology that underlies this behavioral intervention is still elusive.Approach.We recorded extracellular local field potentials simultaneously from sensorimotor cortex and hippocampus in rats during urethane anesthesia following MCA occlusion and subsequent EE treatment.Main results.We found that MCA stroke significantly impacted the electrophysiology in the hippocampus, in particular it disrupted characteristics of sharp-wave associated ripples (SPW-Rs) altered brain state, and disrupted phase amplitude coupling (PAC) within the hippocampus and between the cortex and hippocampus. Importantly, we show that EE mitigates stroke-induced changes to SPW-R characteristics but does not restore hippocampal brain state or PAC.Significance.These results begin to uncover the complex interaction between cognitive deficit following stroke and EE treatment, providing a testbed to assess different strategies for therapeutics following stroke.

Resampling-based stepwise multiple testing procedures with applications to clinical trial data.

Endpoints in clinical trials are often highly correlated. However, the commonly used multiple testing procedures in clinical trials either do not take into consideration the correlations among test statistics or can only exploit known correlations. Westfall and Young constructed a resampling-based stepdown method that implicitly utilizes the correlation structure of test statistics in situations with unknown correlations. However, their method requires a "subset pivotality" assumption. Romano and Wolf proposed a more general stepdown method, which does not require such an assumption. There is at present little experience with the application of such methods in analyzing clinical trial data. We advocate the application of resampling-based multiple testing procedures to clinical trials data when appropriate. We have conjectured that the resampling-based stepdown methods can be extended to a stepup procedure under appropriate assumptions and examined the performance of both stepdown and stepup methods under a variety of correlation structures and distribution types. Results from our simulation studies support the use of the resampling-based methods under various scenarios, including binary data and small samples, with strong control of Family wise type I error rate (FWER). Under positive dependence and for binary data even under independence, the resampling-based methods are more powerful than the Holm and Hochberg methods. Last, we illustrate the advantage of the resampling-based stepwise methods with two clinical trial data examples: a cardiovascular outcome trial and an oncology trial.

Regression calibration to correct correlated errors in outcome and exposure.

Measurement error arises through a variety of mechanisms. A rich literature exists on the bias introduced by covariate measurement error and on methods of analysis to address this bias. By comparison, less attention has been given to errors in outcome assessment and nonclassical covariate measurement error. We consider an extension of the regression calibration method to settings with errors in a continuous outcome, where the errors may be correlated with prognostic covariates or with covariate measurement error. This method adjusts for the measurement error in the data and can be applied with either a validation subset, on which the true data are also observed (eg, a study audit), or a reliability subset, where a second observation of error prone measurements are available. For each case, we provide conditions under which the proposed method is identifiable and leads to consistent estimates of the regression parameter. When the second measurement on the reliability subset has no error or classical unbiased measurement error, the proposed method is consistent even when the primary outcome and exposures of interest are subject to both systematic and random error. We examine the performance of the method with simulations for a variety of measurement error scenarios and sizes of the reliability subset. We illustrate the method's application using data from the Women's Health Initiative Dietary Modification Trial.

Experimental cortical stroke induces aberrant increase of sharp-wave-associated ripples in the hippocampus and disrupts cortico-hippocampal communication.

The functional consequences of ischemic stroke in the remote brain regions are not well characterized. The current study sought to determine changes in hippocampal oscillatory activity that may underlie the cognitive impairment observed following distal middle cerebral artery occlusion (dMCAO) without causing hippocampal structural damage. Local field potentials were recorded from the dorsal hippocampus and cortex in urethane-anesthetized rats with multichannel silicon probes during dMCAO and reperfusion, or mild ischemia induced by bilateral common carotid artery occlusion (CCAO). Bilateral change of brain state was evidenced by reduced theta/delta amplitude ratio and shortened high theta duration following acute dMCAO but not CCAO. An aberrant increase in the occurrence of sharp-wave-associated ripples (150-250 Hz), crucial for memory consolidation, was only detected after dMCAO reperfusion, coinciding with an increased occurrence of high-frequency discharges (250-450 Hz). dMCAO also significantly affected the modulation of gamma amplitude in the cortex coupled to hippocampal theta phase, although both hippocampal theta and gamma power were temporarily decreased during dMCAO. Our results suggest that MCAO may disrupt the balance between excitatory and inhibitory circuits in the hippocampus and alter the function of cortico-hippocampal network, providing a novel insight in how cortical stroke affects function in remote brain regions.

Marginal structural models to estimate the effects of time-varying treatments on clustered outcomes in the presence of interference.

Marginal structural models are a class of causal models useful for characterizing the effect of treatment in the presence of time-varying confounding. They are more widely used than structural nested models, partly because these models are easier to understand and to implement. We extend marginal structural models to situations with clustered observations with unit- and cluster-level treatment and introduce an appropriate inferential method. We consider how to formulate models with cluster-level and unit-level treatments. For unit-level treatments, we consider cases with and without interference. We also consider the use of unit-specific inverse probability weights and certain working correlation structures to improve the efficiency of estimators in some situations. We apply our method to different scenarios including 2 or 3 units per cluster and a mixture of larger clusters. Simulation examples and data from the treatment arm of a glaucoma clinical trial were used to illustrate our method.

Widespread optogenetic expression in macaque cortex obtained with MR-guided, convection enhanced delivery (CED) of AAV vector to the thalamus.

BACKGROUND: In non-human primate (NHP) optogenetics, infecting large cortical areas with viral vectors is often a difficult and time-consuming task. Previous work has shown that parenchymal delivery of adeno-associated virus (AAV) in the thalamus by convection-enhanced delivery (CED) can lead to large-scale transduction via axonal transport in distal areas including cortex. We used this approach to obtain widespread cortical expression of light-sensitive ion channels. NEW METHOD: AAV vectors co-expressing channelrhodopsin-2 (ChR2) and yellow fluorescent protein (YFP) genes were infused into thalamus of three rhesus macaques under MR-guided CED. After six to twelve weeks recovery, in vivo optical stimulation and single cell recording in the cortex was carried out using an optrode in anesthetized animals. Post-mortem immunostaining against YFP was used to estimate the distribution and level of expression of ChR2 in thalamus and cortex. RESULTS: Histological analysis revealed high levels of transduction in cortical layers. The patterns of expression were consistent with known thalamo-cortico-thalamic circuits. Dense expression was seen in thalamocortiocal axonal fibers in layers III, IV and VI and in pyramidal neurons in layers V and VI, presumably corticothalamic neurons. In addition we obtained reliable in vivo light-evoked responses in cortical areas with high levels of expression. COMPARISON WITH EXISTING METHODS: Thalamic CED is very efficient in achieving large expressing areas in comparison to convectional techniques both in minimizing infusion time and in minimizing damage to the brain. CONCLUSION: MR-guided CED infusion into thalamus provides a simplified approach to transduce large cortical areas by thalamo-cortico-thalamic projections in primate brain.

Validating a Local Failure Risk Stratification for Use in Prospective Studies of Adjuvant Radiation Therapy for Bladder Cancer.

PURPOSE: To inform prospective trials of adjuvant radiation therapy (adj-RT) for bladder cancer after radical cystectomy, a locoregional failure (LF) risk stratification was proposed. This stratification was developed and validated using surgical databases that may not reflect the outcomes expected in prospective trials. Our purpose was to assess sources of bias that may affect the stratification model's validity or alter the LF risk estimates for each subgroup: time bias due to evolving surgical techniques; trial accrual bias due to inclusion of patients who would be ineligible for adj-RT trials because of early disease progression, death, or loss to follow-up shortly after cystectomy; bias due to different statistical methods to estimate LF; and subgrouping bias due to different definitions of the LF subgroups. METHODS AND MATERIALS: The LF risk stratification was developed using a single-institution cohort (n=442, 1990-2008) and the multi-institutional SWOG 8710 cohort (n=264, 1987-1998) treated with radical cystectomy with or without chemotherapy. We evaluated the sensitivity of the stratification to sources of bias using Fine-Gray regression and Kaplan-Meier analyses. RESULTS: Year of radical cystectomy was not associated with LF risk on univariate or multivariate analysis after controlling for risk group. By use of more stringent inclusion criteria, 26 SWOG patients (10%) and 60 patients from the single-institution cohort (14%) were excluded. Analysis of the remaining patients confirmed 3 subgroups with significantly different LF risks with 3-year rates of 7%, 17%, and 36%, respectively (P<.01), nearly identical to the rates without correcting for trial accrual bias. Kaplan-Meier techniques estimated higher subgroup LF rates than competing risk analysis. The subgroup definitions used in the NRG-GU001 adj-RT trial were validated. CONCLUSIONS: These sources of bias did not invalidate the LF risk stratification or substantially change the model's LF estimates.

Perturbation of Brain Oscillations after Ischemic Stroke: A Potential Biomarker for Post-Stroke Function and Therapy.

Brain waves resonate from the generators of electrical current and propagate across brain regions with oscillation frequencies ranging from 0.05 to 500 Hz. The commonly observed oscillatory waves recorded by an electroencephalogram (EEG) in normal adult humans can be grouped into five main categories according to the frequency and amplitude, namely δ (1-4 Hz, 20-200 µV), θ (4-8 Hz, 10 µV), α (8-12 Hz, 20-200 µV), ß (12-30 Hz, 5-10 µV), and γ (30-80 Hz, low amplitude). Emerging evidence from experimental and human studies suggests that groups of function and behavior seem to be specifically associated with the presence of each oscillation band, although the complex relationship between oscillation frequency and function, as well as the interaction between brain oscillations, are far from clear. Changes of brain oscillation patterns have long been implicated in the diseases of the central nervous system including ischemic stroke, in which the reduction of cerebral blood flow as well as the progression of tissue damage have direct spatiotemporal effects on the power of several oscillatory bands and their interactions. This review summarizes the current knowledge in behavior and function associated with each brain oscillation, and also in the specific changes in brain electrical activities that correspond to the molecular events and functional alterations observed after experimental and human stroke. We provide the basis of the generations of brain oscillations and potential cellular and molecular mechanisms underlying stroke-induced perturbation. We will also discuss the implications of using brain oscillation patterns as biomarkers for the prediction of stroke outcome and therapeutic efficacy.

Hemodynamic and Light-Scattering Changes of Rat Spinal Cord and Primary Somatosensory Cortex in Response to Innocuous and Noxious Stimuli.

Neuroimaging technologies with an exceptional spatial resolution and noninvasiveness have become a powerful tool for assessing neural activity in both animals and humans. However, the effectiveness of neuroimaging for pain remains unclear partly because the neurovascular coupling during pain processing is not completely characterized. Our current work aims to unravel patterns of neurovascular parameters in pain processing. A novel fiber-optic method was used to acquire absolute values of regional oxy- (HbO) and deoxy-hemoglobin concentrations, oxygen saturation rates (SO2), and the light-scattering coefficients from the spinal cord and primary somatosensory cortex (SI) in 10 rats. Brief mechanical and electrical stimuli (ranging from innocuous to noxious intensities) as well as a long-lasting noxious stimulus (formalin injection) were applied to the hindlimb under pentobarbital anesthesia. Interhemispheric comparisons in the spinal cord and SI were used to confirm functional activation during sensory processing. We found that all neurovascular parameters showed stimulation-induced changes; however, patterns of changes varied with regions and stimuli. Particularly, transient increases in HbO and SO2 were more reliably attributed to brief stimuli, whereas a sustained decrease in SO2 was more reliably attributed to formalin. Only the ipsilateral SI showed delayed responses to brief stimuli. In conclusion, innocuous and noxious stimuli induced significant neurovascular responses at critical centers (e.g., the spinal cord and SI) along the somatosensory pathway; however, there was no single response pattern (as measured by amplitude, duration, lateralization, decrease or increase) that was able to consistently differentiate noxious stimuli. Our results strongly suggested that the neurovascular response patterns differ between brief and long-lasting noxious stimuli, and can also differ between the spinal cord and SI. Therefore, a use of multiple-parameter strategy tailored by stimulus modality (brief or long-lasting) as well as region-dependent characteristics may be more effective in detecting pain using neuroimaging technologies.

The Scalp Confounds Near-Infrared Signal from Rat Brain Following Innocuous and Noxious Stimulation.

Functional near-infrared imaging (fNIRI) is a non-invasive, low-cost and highly portable technique for assessing brain activity and functions. Both clinical and experimental evidence suggest that fNIRI is able to assess brain activity at associated regions during pain processing, indicating a strong possibility of using fNIRI-derived brain activity pattern as a biomarker for pain. However, it remains unclear how, especially in small animals, the scalp influences fNIRI signal in pain processing. Previously, we have shown that the use of a multi-channel system improves the spatial resolution of fNIRI in rats (without the scalp) during pain processing. Our current work is to investigate a scalp effect by comparing with new data from rats with the scalp during innocuous or noxious stimulation (n = 6). Results showed remarkable stimulus-dependent differences between the no-scalp and intact-scalp groups. In conclusion, the scalp confounded the fNIRI signal in pain processing likely via an autonomic mechanism; the scalp effect should be a critical factor in image reconstruction and data interpretation.

An Olfactory Cilia Pattern in the Mammalian Nose Ensures High Sensitivity to Odors.

In many sensory organs, specialized receptors are strategically arranged to enhance detection sensitivity and acuity. It is unclear whether the olfactory system utilizes a similar organizational scheme to facilitate odor detection. Curiously, olfactory sensory neurons (OSNs) in the mouse nose are differentially stimulated depending on the cell location. We therefore asked whether OSNs in different locations evolve unique structural and/or functional features to optimize odor detection and discrimination. Using immunohistochemistry, computational fluid dynamics modeling, and patch clamp recording, we discovered that OSNs situated in highly stimulated regions have much longer cilia and are more sensitive to odorants than those in weakly stimulated regions. Surprisingly, reduction in neuronal excitability or ablation of the olfactory G protein in OSNs does not alter the cilia length pattern, indicating that neither spontaneous nor odor-evoked activity is required for its establishment. Furthermore, the pattern is evident at birth, maintained into adulthood, and restored following pharmacologically induced degeneration of the olfactory epithelium, suggesting that it is intrinsically programmed. Intriguingly, type III adenylyl cyclase (ACIII), a key protein in olfactory signal transduction and ubiquitous marker for primary cilia, exhibits location-dependent gene expression levels, and genetic ablation of ACIII dramatically alters the cilia pattern. These findings reveal an intrinsically programmed configuration in the nose to ensure high sensitivity to odors.

Structural Nested Mean Models to Estimate the Effects of Time-Varying Treatments on Clustered Outcomes.

In assessing the efficacy of a time-varying treatment structural nested models (SNMs) are useful in dealing with confounding by variables affected by earlier treatments. These models often consider treatment allocation and repeated measures at the individual level. We extend SNMMs to clustered observations with time-varying confounding and treatments. We demonstrate how to formulate models with both cluster- and unit-level treatments and show how to derive semiparametric estimators of parameters in such models. For unit-level treatments, we consider interference, namely the effect of treatment on outcomes in other units of the same cluster. The properties of estimators are evaluated through simulations and compared with the conventional GEE regression method for clustered outcomes. To illustrate our method, we use data from the treatment arm of a glaucoma clinical trial to compare the effectiveness of two commonly used ocular hypertension medications.

A mixture model using likelihood-based and Bayesian approaches for identifying responders and non-responders in longitudinal clinical trials.

A longitudinal mixture model for classifying patients into responders and non-responders is established using both likelihood-based and Bayesian approaches. The model takes into consideration responders in the control group. Therefore, it is especially useful in situations where the placebo response is strong, or in equivalence trials where the drug in development is compared with a standard treatment. Under our model, a treatment shows evidence of being effective if it increases the proportion of responders or increases the response rate among responders in the treated group compared with the control group. Therefore, the model has flexibility to accommodate different situations. The proposed method is illustrated using simulation and a depression clinical trial dataset for the likelihood-based approach, and the same depression clinical trial dataset for the Bayesian approach. The likelihood-based and Bayesian approaches generated consistent results for the depression trial data. In both the placebo group and the treated group, patients are classified into two components with distinct response rate. The proportion of responders is shown to be significantly higher in the treated group compared with the control group, suggesting the treatment paroxetine is effective.

Optimizing bladder cancer locoregional failure risk stratification after radical cystectomy using SWOG 8710.

BACKGROUND: Clinical trials of radiation after radical cystectomy (RC) and chemotherapy for bladder cancer are in development, but inclusion and stratification factors have not been clearly established. In this study, the authors evaluated and refined a published risk stratification for locoregional failure (LF) by applying it to a multicenter patient cohort. METHODS: The original stratification, which was developed using a single-institution series, produced 3 subgroups with significantly different LF risk based on pathologic tumor (pT) classification and the number of lymph nodes identified. This model was then applied to patients in Southwest Oncology Group (SWOG) 8710, a randomized trial of RC with or without chemotherapy. LF was defined as any pelvic failure before or within 3 months of distant failure. RESULTS: Patients in the development cohort and the SWOG cohort had significantly different baseline characteristics. The original risk model was not fully validated in the SWOG cohort, because lymph node yield was not as strongly associated with LF as in the development cohort. Regression analysis indicated that margin status could improve the model. A revised stratification using pT classification, margin status, and the number of lymph nodes identified produced 3 subgroups with significantly different LF risk in both cohorts: low risk (≤pT2), intermediate risk (≥pT3 with negative margins AND ≥10 lymph nodes identified), and high risk (≥pT3 with positive margins OR <10 lymph nodes identified) with 5-year LF rates of 8%, 20%, and 41%, respectively, in the SWOG cohort and 8%, 19%, and 41%, respectively, in the development cohort. CONCLUSIONS: A model incorporating pT classification, margin status, and the number of lymph nodes identified stratified LF risk in 2 different RC populations and may inform the design of future trials.

Occult pelvic lymph node involvement in bladder cancer: implications for definitive radiation.

PURPOSE: To inform radiation treatment planning for clinically staged, node-negative bladder cancer patients by identifying clinical factors associated with the presence and location of occult pathologic pelvic lymph nodes. METHODS AND MATERIALS: The records of patients with clinically staged T1-T4N0 urothelial carcinoma of the bladder undergoing radical cystectomy and pelvic lymphadenectomy at a single institution were reviewed. Logistic regression was used to evaluate associations between preoperative clinical variables and occult pathologic pelvic or common iliac lymph nodes. Percentages of patient with involved lymph node regions entirely encompassed within whole bladder (perivesicular nodal region), small pelvic (perivesicular, obturator, internal iliac, and external iliac nodal regions), and extended pelvic clinical target volume (CTV) (small pelvic CTV plus common iliac regions) were calculated. RESULTS: Among 315 eligible patients, 81 (26%) were found to have involved pelvic lymph nodes at the time of surgery, with 38 (12%) having involved common iliac lymph nodes. Risk of occult pathologically involved lymph nodes did not vary with clinical T stage. On multivariate analysis, the presence of lymphovascular invasion (LVI) on preoperative biopsy was significantly associated with occult pelvic nodal involvement (odds ratio 3.740, 95% confidence interval 1.865-7.499, P<.001) and marginally associated with occult common iliac nodal involvement (odds ratio 2.307, 95% confidence interval 0.978-5.441, P=.056). The percentages of patients with involved lymph node regions entirely encompassed by whole bladder, small pelvic, and extended pelvic CTVs varied with clinical risk factors, ranging from 85.4%, 95.1%, and 100% in non-muscle-invasive patients to 44.7%, 71.1%, and 94.8% in patients with muscle-invasive disease and biopsy LVI. CONCLUSIONS: Occult pelvic lymph node rates are substantial for all clinical subgroups, especially patients with LVI on biopsy. Extended coverage of pelvic lymph nodes up to the level of the common iliac nodes may be warranted in subsets of patients.