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Purpose Improving vocabulary knowledge is important for many adolescents, but there are few evidence-based vocabulary instruction programs available for high school students. The purpose of this article is to describe the iterative development of the DictionarySquared research platform, a web-based vocabulary program that provides individualized vocabulary instruction, and to report the results of 2 pilot studies examining the feasibility of implementation and potential effectiveness with high school students. Method We describe our theory of change and 5 phases of iterative development. In Phases 1 and 2, we evaluated the initial implementation of DictionarySquared and revised the instructional materials. In Phase 3, we conducted a feasibility study involving 169 high school students who used the program for 4 weeks. Student usage data and feedback from teachers were used to guide program revisions in Phase 4. In Phase 5, we examined potential effectiveness for 264 high school students who were assigned to use the program for 1 semester. Results Results of the Phase 3 study indicated that implementation of the program was feasible, although usage was below assigned levels. Results of the Phase 5 study indicated that the duration of active program usage significantly predicted posttest vocabulary scores on the proximal assessment after controlling for pretest standardized vocabulary scores. Analyses using propensity score matching revealed positive, but nonsignificant, gains on standardized assessments between pre- and posttests. Conclusion Together, the results of early-stage pilot studies are promising and suggest that a more rigorous test of efficacy is warranted. Successful implementation of the DictionarySquared research program, as well as lessons learned from the program development process, will expand the range of evidence-based treatment options that clinicians and educators can use to improve adolescent vocabulary and reading comprehension skills. Supplemental Material https://doi.org/10.23641/asha.9765161

We present a new experimental design procedure that divides a set of experimental units into two groups so that the two groups are balanced on a prespecified set of covariates and being almost as random as complete randomization. Under complete randomization, the difference in covariate balance as measured by the standardized average between treatment and control will be $O_p(n^{-1/2})$. If the sample size is not too large this may be material. In this article, we present an algorithm which greedily switches assignment pairs. Resultant designs produce balance of the much lower order $O_p(n^{-3})$ for one covariate. However, our algorithm creates assignments which are, strictly speaking, non-random. We introduce two metrics which capture departures from randomization: one in the style of entropy and one in the style of standard error and demonstrate our assignments are nearly as random as complete randomization in terms of both measures. The results are extended to more than one covariate, simulations are provided to illustrate the results and statistical inference under our design is discussed. We provide an open source R package available on CRAN called GreedyExperimentalDesign which generates designs according to our algorithm., 21 pages, 3 figures, 3 tables

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package "SeqExpMatch" for use by practitioners is available on CRAN.

BackgroundSufficiently accurate predictions of hospital readmissions are necessary for the allocation of scare clinical resources to reduce preventable readmissions. We describe the use of a data-driven approach that relies on machine learning algorithms to predict readmission at the time of discharge.MethodsWe employ random forests to clinical and administrative electronic health record data available from a cohort of 103,688 patients discharged from the acute inpatient settings of the University of Pennsylvania Health System between June 25th, 2011 and June 30th, 2013. We predict both 30-day all-cause readmissions and 7-day unplanned readmissions using only predictors available by the time of discharge. Using oversampling and undersampling of the different outcome classes of readmission and no readmission, we incorporate into our models the asymmetric costs of a false negative relative to a false positive from the perspective of a hospital. We calculate variable importance scores for included predictors. Our approach was derived and validated using split-sample internal validation.ResultsWe developed a machine learning-based model using random forests with a 5:1 relative cost ratio for 30-day all-cause readmissions that achieves a sensitivity of 65% and specificity of 71% on validation data, as well as a random forests model with a 20:1 cost ratio for 7-day unplanned readmissions that achieves a sensitivity of 62% and specificity of 66% on validation data. Prior health system utilization, clinical discharging service, and vital sign information were most predictive of readmissions.ConclusionBy modeling the complex relationships between many predictor variables and readmission data for a large health system, we demonstrate successful predictive models that can be used upon discharge to flag patients at high risk of readmission.

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.

Purpose: Our work introduces a highly accurate, safe, and sufficiently explicable machine-learning (artificial intelligence) model of intraocular lens power (IOL) translating into better post-surgical outcomes for patients with cataracts. We also demonstrate its improved predictive accuracy over previous formulas.Methods: We collected retrospective eye measurement data on 5,331 eyes from 3,276 patients across multiple centers who received a lens implantation during cataract surgery. The dependent measure is the post-operative manifest spherical equivalent error from intended and the independent variables are the patient- and eye-specific characteristics. This dataset was split so that one subset was for formula construction and the other for validating our new formula. Data excluded fellow eyes, so as not to confound the prediction with bilateral eyes.Results: Our formula is three times more precise than reported studies with a median absolute IOL error of 0.204 diopters (D). When converted to absolute predictive refraction errors on the cornea, the median error is 0.137 D which is close to the IOL manufacturer tolerance. These estimates are validated out-of-sample and thus are expected to reflect the future performance of our prediction formula, especially since our data were collected from a wide variety of patients, clinics, and manufacturers.Conclusion: The increased precision of IOL power calculations has the potential to optimize patient positive refractive outcomes. Our model also provides uncertainty plots that can be used in tandem with the clinician’s expertise and previous formula output, further enhancing the safety.Translational relavance: Our new machine learning process has the potential to significantly improve patient IOL refractive outcomes safely.

We present a new experimental design procedure that divides a set of experimental units into two groups in order to minimize error in estimating an additive treatment effect. One concern is minimizing error at the experimental design stage is large covariate imbalance between the two groups. Another concern is robustness of design to misspecification in response models. We address both concerns in our proposed design: we first place subjects into pairs using optimal nonbipartite matching, making our estimator robust to complicated non-linear response models. Our innovation is to keep the matched pairs extant, take differences of the covariate values within each matched pair and then we use the greedy switching heuristic of Krieger et al. (2019) or rerandomization on these differences. This latter step greatly reduce covariate imbalance to the rate $O_p(n^{-4})$ in the case of one covariate that are uniformly distributed. This rate benefits from the greedy switching heuristic which is $O_p(n^{-3})$ and the rate of matching which is $O_p(n^{-1})$. Further, our resultant designs are shown to be as random as matching which is robust to unobserved covariates. When compared to previous designs, our approach exhibits significant improvement in the mean squared error of the treatment effect estimator when the response model is nonlinear and performs at least as well when it the response model is linear. Our design procedure is found as a method in the open source R package available on CRAN called GreedyExperimentalDesign., 18 pages, 2 tables, 2 figures

SummaryObjective In the absence of specific metabolic disorders, accurate predictors of response to ketogenic dietary therapies (KDTs) for treating epilepsy are largely unknown. We hypothesized that specific biochemical parameters would be associated with the effectiveness of KDT in humans with epilepsy. The parameters tested were β-hydroxybutyrate, acetoacetate, nonesterified fatty acids, free and acylcarnitine profile, glucose, and glucose-ketone index (GKI). Methods Biochemical results from routine blood tests conducted at baseline prior to initiation of KDT and at 3-month follow-up were obtained from 13 adults and 215 children with KDT response data from participating centers. One hundred thirty-two (57%) of 228 participants had some data at both baseline and 3 months; 52 (23%) of 228 had data only at baseline; 22 (10%) of 228 had data only at 3 months; and 22 (10%) of 228 had no data. KDT response was defined as ≥50% seizure reduction at 3-month follow-up. Results Acetyl carnitine at baseline was significantly higher in responders (p < 0.007). It was not associated with response at 3-month follow-up. There was a trend for higher levels of free carnitine and other acylcarnitine esters at baseline and at 3-month follow-up in KDT responders. There was also a trend for greater differences in levels of propionyl carnitine and in β-hydroxybutyrate measured at baseline and 3-month follow-up in KDT responders. No other biochemical parameters were associated with response at any time point. Significance Our finding that certain carnitine fractions, in particular baseline acetyl carnitine, are positively associated with greater efficacy of KDT is consistent with the theory that alterations in energy metabolism may play a role in the mechanisms of action of KDT.

There is a long debate in experimental design between the classic randomization design of Fisher, Yates, Kempthorne, Cochran, and those who advocate deterministic assignments based on notions of optimality. In nonsequential trials comparing treatment and control, covariate measurements for each subject are known in advance, and subjects can be divided into two groups based on a criterion of imbalance. With the advent of modern computing, this partition can be made nearly perfectly balanced via numerical optimization, but these allocations are far from random. These perfect allocations may endanger estimation relative to classic randomization because unseen subject-specific characteristics can be highly imbalanced. To demonstrate this, we consider different performance criterions such as Efron’s worst-case analysis and our original tail criterion of mean squared error. Under our tail criterion for the differences-in-mean estimator, we prove asymptotically that the optimal design must be more random than perfect balance but less random than completely random. Our result vindicates restricted designs that are used regularly such as blocking and rerandomization. For a covariate-adjusted estimator, balancing offers less rewards and it seems good performance is achievable with complete randomization. Further work will provide a procedure to find the explicit optimal design in different scenarios in practice. Supplementary materials for this article are available online.

We present a method for incorporating missing data in non-parametric statistical learning without the need for imputation. We focus on a tree-based method, Bayesian Additive Regression Trees (BART), enhanced with "Missingness Incorporated in Attributes," an approach recently proposed incorporating missingness into decision trees (Twala, 2008). This procedure takes advantage of the partitioning mechanisms found in tree-based models. Simulations on generated models and real data indicate that our proposed method can forecast well on complicated missing-at-random and not-missing-at-random models as well as models where missingness itself influences the response. Our procedure has higher predictive performance and is more stable than competitors in many cases. We also illustrate BART's abilities to incorporate missingness into uncertainty intervals and to detect the influence of missingness on the model fit., Comment: 18 pages, 3 figures, 2 tables, 1 algorithm

Background: Depression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modelling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability. Methods: Using STAR*D and CO-MED trial data, we employed deep neural networks to predict remission after feature reduction. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using both naive and conservative approaches. The naive approach assessed population remission rate in five sets of 200 patients held apart from the training set; the conservative approach used bootstrapping for sample generation and focused on population remission rate for patients who actually received the drug predicted by the model compared to the general population. Outcomes: Our deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an AUC of 0.69 using 17 input features. Our naive analysis showed an improvement of remission of over 30% (from a 34.33% population remission rate to 46.12%). Our conservative analysis showed a 7.2% relative improvement in population remission rate (p= 0.01, C.I. 2.48% ± .5%). Interpretation: Our model serves as proof-of-concept that deep learning has utility in differential prediction of antidepressant response when selecting from more than two treatment options. These models may have significant real-world clinical implications. Funding: Aifred Health Declaration of Interest: Dr. Benrimoh reports other from Aifred Health, during the conduct of the study; other from Aifred Health, outside the submitted work; All other authors declare none. Ethical Approval: This work was approved by the Research Ethics Committee of the Douglas Mental Health University Institute.

In traditional publishing, female authors' titles command nearly half (45%) the price of male authors' and are underrepresented in more prestigious genres, and books are published by publishing houses, which determined whose books get published, subject classification, and retail price. In the last decade, the growth of digital technologies and sales platforms have enabled unprecedented numbers of authors to bypass publishers to publish and sell books. The rise of indie publishing (aka self-publishing) reflects the growth of the "gig" economy, where the influence of firms has diminished and workers are exposed more directly to external markets. Encompassing the traditional and the gig economy, the book industry illuminates how the gig economy may disrupt, replicate, or transform the gender discrimination mechanisms and inequality found in the traditional economy. In a natural experiment spanning from 2002 to 2012 and including over two million book titles, we compare discrimination mechanisms and inequality in indie and traditional publishing. We find that indie publishing, though more egalitarian, largely replicates traditional publishing's gender discrimination patterns, showing an unequal distribution of male and female authors by genre (allocative discrimination), devaluation of genres written predominantly by female authors (valuative discrimination), and lower prices within genres for books by female authors (within-job discrimination). However, these discrimination mechanisms are associated with far less price inequality in indie, only 7%, in large part due to the smaller and lower range of prices in indie publishing compared to traditional publishing. We conclude that, with greater freedom, workers in the gig economy may be inclined to greater equality but will largely replicate existing labor market segmentation and the lower valuation of female-typical work and of female workers. Nonetheless, price setting for work may be more similar for workers in the gig economy due to market competition that will compress prices ranges.

Depression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modeling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability. Using Sequenced Treatment Alternatives to Relieve Depression (STAR*D) and Combining Medications to Enhance Depression Outcomes (CO-MED) trial data, we employed deep neural networks to predict remission after feature selection. Treatments included were citalopram, escitalopram, bupropion SR plus escitalopram, and venlafaxine plus mirtazapine. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using two approaches: (1) using predictions generated directly from the model (the predicted improvement approach) and (2) using bootstrapping for sample generation and then estimating population remission rate for patients who actually received the drug predicted by the model compared to the general population (the actual improvement approach). Our deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an area under the curve of 0.69 using 17 input features. Our actual improvement analysis showed a statistically significant 2.48% absolute improvement (corresponding to a 7.2% relative improvement) in population remission rate (p = 0.01, CI 2.48% ± 0.5%). Our model serves as proof-of-concept that deep learning approaches, with further refinement and work to address concerns about differences between studies when multiple datasets are used for training, may have utility in differential prediction of antidepressant response when selecting from a number of treatment options.

When measuring Henry's Law constants ($k_H$) using the phase ratio method via headspace gas chromatography (GC), the value of $k_H$ of the compound under investigation is calculated from the ratio of the slope to the intercept of a linear regression of the the inverse GC response versus the ratio of gas to liquid volumes of a series of vials drawn from the same parent solution. Thus, an experimenter will collect measurements consisting of the independent variable (the gas/liquid volume ratio) and dependent variable (the inverse GC peak area). There is a choice of values of the independent variable during measurement. A review of the literature found that the common approach is a simple uniformly spacing of the liquid volumes. We present an optimal experimental design which estimates $k_H$ with minimum error and provides multiple means for building confidence intervals for such estimates. We illustrate efficiency improvements of our new design with an example measuring the $k_H$ for napthalene in aqueous solution as well as simulations on previous studies. The designs can be easily computed using our open source software optDesignSlopeInt, an R package on CRAN. We also discuss applicability of this method to other fields., 21 pages, 2 figures, 3 tables

We run a randomized experiment to examine gender discrimination in book purchasing with 2,544 subjects on Amazon’s Mechanical Turk. We manipulate author gender and book genre in a factorial design to study consumer preferences for male versus female versus androgynous authorship. Despite previous findings in the literature showing gender discrimination in book publishing and in evaluations of work, respondents expressed no gender preference across a variety of measures, including quality, interest, and the amount they were willing to pay to purchase the book. This nonfinding, if it holds up to additional research, suggests that book consumers may not express the same discriminatory tendencies observed among indie and traditional publishers.

We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [BART, Ann. Appl. Stat. 4 (2010) 266-298] provides a novel nonparametric alternative to parametric regression approaches, such as the lasso or stepwise regression, especially when the number of relevant predictors is sparse relative to the total number of available predictors and the fundamental relationships are nonlinear. We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real. Going further, we adapt the BART procedure to incorporate informed prior information about variable importance. We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings. To demonstrate the potential of our approach in a biological context, we apply it to the task of inferring the gene regulatory network in yeast (Saccharomyces cerevisiae). We find that our BART-based procedure is best able to recover the subset of covariates with the largest signal compared to other variable selection methods. The methods developed in this work are readily available in the R package bartMachine., Published in at http://dx.doi.org/10.1214/14-AOAS755 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves the systematic use of individual patient characteristics to determine which treatment option is most likely to result in a better outcome for the patient on average. Why is personalized medicine not done more in practice? One of many reasons is because practitioners do not have any easy way to holistically evaluate whether their personalization procedure does better than the standard of care. Our software, "Personalized Treatment Evaluator" (the R package PTE), provides inference for improvement out-of-sample in many clinical scenarios. We also extend current methodology by allowing evaluation of improvement in the case where the endpoint is binary or survival. In the software, the practitioner inputs (1) data from a single-stage randomized trial with one continuous, incidence or survival endpoint and (2) a functional form of a model for the endpoint constructed from domain knowledge. The bootstrap is then employed on data unseen during model fitting to provide confidence intervals for the improvement for the average future patient (assuming future patients are similar to the patients in the trial). One may also test against a null scenario where the hypothesized personalization are not more useful than a standard of care. We demonstrate our method's promise on simulated data as well as on data from a randomized comparative trial investigating two treatments for depression., Comment: 36 pages, 3 figures, 1 table

Neoplasms are highly dependent on glucose as their substrate for energy production and are generally not able to catabolize other fuel sources such as ketones and fatty acids. Thus, removing access to glucose has the potential to starve cancer cells and induce apoptosis. Unfortunately, other body tissues are also dependent on glucose for energy under normal conditions. However, in human starvation (or in the setting of diet-induced ketogenesis), the body "keto-adapts" and glucose requirements of most tissues drop to almost nil. Exceptions include the central nervous system (CNS) and various other tissues which have a small but obligatory requirement of glucose. Our hypothesized treatment takes keto-adaptation as a prerequisite. We then propose the induction of severe hypoglycemia by depressing gluconeogenesis while administering glucose to the brain. Although severe hypoglycemia normally produces adverse effects such as seizure and coma, it is relatively safe following ketoadaptation. We hypothesize that our therapeutic hypoglycemia treatment has potential to rapidly induce tumor cell necrosis., Comment: 17 pages, 1 figure, 1 table

This article presents Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. Classical partial dependence plots (PDPs) help visualize the average partial relationship between the predicted response and one or more features. In the presence of substantial interaction effects, the partial response relationship can be heterogeneous. Thus, an average curve, such as the PDP, can obfuscate the complexity of the modeled relationship. Accordingly, ICE plots refine the partial dependence plot by graphing the functional relationship between the predicted response and the feature for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate, suggesting where and to what extent heterogeneities might exist. In addition to providing a plotting suite for exploratory analysis, we include a visual test for additive structure in the data generating model. Through simulated examples and real data sets, we demonstrate how ICE plots can shed light on estimated models in ways PDPs cannot. Procedures outlined are available in the R package ICEbox., 22 pages, 14 figures, 2 algorithms

We conduct the first natural field experiment to explore the relationship between the "meaningfulness" of a task and worker effort. We employed about 2,500 workers from Amazon's Mechanical Turk (MTurk), an online labor market, to label medical images. Although given an identical task, we experimentally manipulated how the task was framed. Subjects in the meaningful treatment were told that they were labeling tumor cells in order to assist medical researchers, subjects in the zero-context condition (the control group) were not told the purpose of the task, and, in stark contrast, subjects in the shredded treatment were not given context and were additionally told that their work would be discarded. We found that when a task was framed more meaningfully, workers were more likely to participate. We also found that the meaningful treatment increased the quantity of output (with an insignificant change in quality) while the shredded treatment decreased the quality of output (with no change in quantity). We believe these results will generalize to other short-term labor markets. Our study also discusses MTurk as an exciting platform for running natural field experiments in economics., 26 pages, 15 figures, 4 tables

Supervised learning can be used to segment/identify regions of interest in images using both color and morphological information. A novel object identification algorithm was developed in Java to locate immune and cancer cells in images of immunohistochemically-stained lymph node tissue from a recent study published by Kohrt et al. (2005). The algorithms are also showing promise in other domains. The success of the method depends heavily on the use of color, the relative homogeneity of object appearance and on interactivity. As is often the case in segmentation, an algorithm specifically tailored to the application works better than using broader methods that work passably well on any problem. Our main innovation is the interactive feature extraction from color images. We also enable the user to improve the classification with an interactive visualization system. This is then coupled with the statistical learning algorithms and intensive feedback from the user over many classification-correction iterations, resulting in a highly accurate and user-friendly solution. The system ultimately provides the locations of every cell recognized in the entire tissue in a text file tailored to be easily imported into R (Ihaka and Gentleman 1996; R Development Core Team 2009) for further statistical analyses. This data is invaluable in the study of spatial and multidimensional relationships between cell populations and tumor structure. This system is available at http://www.GemIdent.com together with three demonstration videos and a manual. The code is now open-sourced and available on github at: https://github.com/kapelner/GemIdent

Background: To date, pathological examination of specimens remains largely qualitative. Quantitative measures of tissue spatial features are generally not captured. To gain additional mechanistic and prognostic insights, a need for quantitative architectural analysis arises in studying immune cell-cancer interactions within the tumor microenvironment and tumor-draining lymph nodes (TDLNs). Methodology/Principal Findings: We present a novel, quantitative image analysis approach incorporating 1) multi-color tissue staining, 2) high-resolution, automated whole-section imaging, 3) custom image analysis software that identifies cell types and locations, and 4) spatial statistical analysis. As a proof of concept, we applied this approach to study the architectural patterns of T and B cells within tumor-draining lymph nodes from breast cancer patients versus healthy lymph nodes. We found that the spatial grouping patterns of T and B cells differed between healthy and breast cancer lymph nodes, and this could be attributed to the lack of B cell localization in the extrafollicular region of the TDLNs. Conclusions/Significance: Our integrative approach has made quantitative analysis of complex visual data possible. Our results highlight spatial alterations of immune cells within lymph nodes from breast cancer patients as an independent variable from numerical changes. This opens up new areas of investigations in research and medicine. Future application of this approach will lead to a better understanding of immune changes in the tumor microenvironment and TDLNs, and how they affect clinical outcomes

Background Dendritic cells (DCs) are important mediators of anti-tumor immune responses. We hypothesized that an in-depth analysis of dendritic cells and their spatial relationships to each other as well as to other immune cells within tumor draining lymph nodes (TDLNs) could provide a better understanding of immune function and dysregulation in cancer. Methods We analyzed immune cells within TDLNs from 59 breast cancer patients with at least 5 years of clinical follow-up using immunohistochemical staining with a novel quantitative image analysis system. We developed algorithms to analyze spatial distribution patterns of immune cells in cancer versus healthy intra-mammary lymph nodes (HLNs) to derive information about possible mechanisms underlying immune-dysregulation in breast cancer. We used the non-parametric Mann–Whitney test for inter-group comparisons, Wilcoxon Matched-Pairs Signed Ranks test for intra-group comparisons and log-rank (Mantel-Cox) test for Kaplan Maier analyses. Results Degree of clustering of DCs (in terms of spatial proximity of the cells to each other) was reduced in TDLNs compared to HLNs. While there were more numerous DC clusters in TDLNs compared to HLNs,DC clusters within TDLNs tended to have fewer member DCs and also consisted of fewer cells displaying the DC maturity marker CD83. The average number of T cells within a standardized radius of a clustered DC was increased compared to that of an unclustered DC, suggesting that DC clustering was associated with T cell interaction. Furthermore, the number of T cells within the radius of a clustered DC was reduced in tumor-positive TDLNs compared to HLNs. Importantly, clinical outcome analysis revealed that DC clustering in tumor-positive TDLNs correlated with the duration of disease-free survival in breast cancer patients. Conclusions These findings are the first to describe the spatial organization of DCs within TDLNs and their association with survival outcome. In addition, we characterized specific changes in number, size, maturity, and T cell co-localization of such clusters. Strategies to enhance DC function in-vivo, including maturation and clustering, may provide additional tools for developing more efficacious DC cancer vaccines.