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Background: Three national career development programs (CDPs)—Early and Mid-Career Programs sponsored by the Association of American Medical Colleges and the Hedwig van Ameringen Executive Leadership in Academic Medicine sponsored by Drexel University—seek to expand gender diversity in faculty and institutional leadership of academic medical centers. Over 20 years of success and continued need are evident in the sustained interest and investment of individuals and institutions. However, their impact on promotion in academic rank remains unknown. The purpose of the study is to compare promotion rates of women CDP participants and other faculty of similar institutional environment and initial career stage. Methods: The study examined retrospective cohorts of 2,719 CDP participants, 12,865 nonparticipant women, and 26,810 men, from the same institutions, with the same degrees, and first years of appointment in rank. Rates of promotion to Associate and Full Professor ranks in respective cohorts of Assistant and of Associate Professors were compared using Kaplan-Meier survival curves and log-rank tests, and logistic regression adjusting for other predictors of academic success. Results: In adjusted analyses, participants were more likely than men and non-participant women to be promoted to Associate Professor and as likely as men and more likely than non-participant women to be promoted to Full Professor within 10 years. Within 5 years, CDP participants were more likely than nonparticipant women to be promoted to Associate Professor and as likely as to be promoted to Full Professor; in the same interval, participants were promoted to both higher ranks at the same rates as men. For both intervals, nonparticipant women were significantly less likely than men to be promoted to either rank. Conclusions: The higher rates of promotion for women participating in national CDPs support the effectiveness of these programs in building capacity for academic medicine.

In this article, we develop a Bayesian hierarchical mixture regression model for studying the association between a multivariate response, measured as counts on a set of features, and a set of covariates. We have available RNA-Seq and DNA methylation data measured on breast cancer patients at different stages of the disease. We account for the heterogeneity and over-dispersion of count data (here, RNA-Seq data) by considering a mixture of negative binomial distributions and incorporate the covariates (here, methylation data) into the model via a linear modeling construction on the mean components. Our modeling construction includes several innovative characteristics. First, it employs selection techniques that allow the identification of a small subset of features that best discriminate the samples while simultaneously selecting a set of covariates associated to each feature. Second, it incorporates known dependencies into the feature selection process via the use of Markov random field (MRF) priors. On simulated data, we show how incorporating existing information via the prior model can improve the accuracy of feature selection. In the analysis of RNA-Seq and DNA methylation data on breast cancer, we incorporate knowledge on relationships among genes via a gene-gene network, which we extract from the KEGG database. Our data analysis identifies genes which are discriminatory of cancer stages and simultaneously selects significant associations between those genes and DNA methylation sites. A biological interpretation of our findings reveals several biomarkers that can help understanding the effect of DNA methylation on gene expression transcription across cancer stages.

Human platelet lysate (hPL) is considered a valid substitute to fetal bovine serum (FBS) in the expansion of mesenchymal stromal cells (MSC), and it is commonly produced starting from intermediate side products of whole blood donations. Through freeze–thaw cycles, hPL is highly enriched in chemokines, growth factors, and adhesion and immunologic molecules. Cell therapy protocols, using hPL instead of FBS for the expansion of cells, are approved by regulatory authorities without concerns, and its administration in patients is considered safe. However, published data are fairly difficult to compare, since the production of hPL is highly variable. This study proposes to optimize and standardize the hPL productive process by using instruments, technologies, and quality/safety standards required for blood bank activities and products. The quality and improved selection of the starting material (i.e., the whole blood), together with the improvement of the production process, guarantee a product characterized by higher content and quality of growth factors as well as a reduction in batch-to-batch variability. By increasing the number of freeze/thaw cycles from one (hPL1c) to four (hPL4c), we obtained a favorable effect on the release of growth factors from platelet α granules. Those changes have directly translated into biological effects leading to a decreasing doubling time (DT) of MSC expansion at 7 days (49.41 ± 2.62 vs. 40.61 ± 1.11 h, p < 0.001). Furthermore, mass spectrometry (MS)-based evaluation has shown that the proliferative effects of hPL4c are also combined with a lower batch-to-batch variability (10–15 vs. 21–31%) at the proteomic level. In conclusion, we have considered lot-to-lot hPL variability, and by the strict application of blood bank standards, we have obtained a standardized, reproducible, safe, cheap, and ready-to-use product.

The use of large datasets for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this manuscript, we propose a nested common atoms model (CAM) that is particularly suited for the analysis of nested datasets where the distributions of the units are expected to differ only over a small fraction of the observations sampled from each unit. The proposed CAM allows a two-layered clustering at the distributional and observational level and is amenable to scalable posterior inference through the use of a computationally efficient nested slice sampler algorithm. We further discuss how to extend the proposed modeling framework to handle discrete measurements, and we conduct posterior inference on a real microbiome dataset from a diet swap study to investigate how the alterations in intestinal microbiota composition are associated with different eating habits. We further investigate the performance of our model in capturing true distributional structures in the population by means of a simulation study.

The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two-group model framework. Here, we investigate employing mixtures of two-parameter Poisson-Dirichlet Processes instead, and show how they provide a more flexible and effective tool for large-scale hypothesis testing. Our model further employs nonlocal prior densities to allow separation between the two mixture components. We obtain a closed-form expression for the exchangeable partition probability function of the two-group model, which leads to a straightforward Markov Chain Monte Carlo implementation. We compare the performance of our method for large-scale inference in a simulation study and illustrate its use on both a prostate cancer data set and a case-control microbiome study of the gastrointestinal tracts in children from underdeveloped countries who have been recently diagnosed with moderate-to-severediarrhea.

Atherosclerosis represents one of the main risk factors for the development of cardiovascular diseases. Their etiologies have been studied in recent years in order to better define therapeutic targets for intervention and to identify diagnostic methods. Two different subtypes of macrophages, M1 and M2, have been described in physiological conditions. They can also be found in the atherosclerotic process, where they both have opposite roles in disease progression. Perivascular brown adipose tissue is also involved in inflammation and endothelial damage. In this work, we provide insights into the protective role of melatonin in the atherosclerotic process by morphological and 18F-FDG-PET/CT analyses. In particular, we examined the effects of melatonin on pathways that are linked to atherosclerosis development. We showed that melatonin, by suppressing M1 activity, reduced inflammation and directed macrophage polarization toward the M2 macrophage subtype. Moreover, melatonin preserved the activity of perivascular brown adipose tissue. In addition, 18F-FDG uptake is very high in mice treated with melatonin, confirming that other factors may alter 18F-FDG distribution. In conclusion, we showed that melatonin affects inflammatory pathways that have been linked to atherosclerosis, assessed the relationships of the 18F-FDG PET/CT parameters with macrophage markers and the production of their cytokines, which that have been defined by morphological evaluations.

Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumor heterogeneity through patterns of enhancement, texture, morphology, and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of Gray-Level Co-occurrence Matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero-inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine-learning approaches currently used in cancer radiomics with simulation studies., Comment: 45 pages, 7 figures, 1 Table

A standard approach for dealing with unobserved heterogeneity and clustered time‐to‐event data within the proportional hazards (PH) context has been the introduction of a cluster‐specific random effect (frailty), common to subjects within the same cluster. However, the conditional PH assumption could be too strong for some applications. For example, the marginal association of survival functions within a cluster does not depend on the subject‐specific covariates. We propose an alternative partially PH modeling approach based on the introduction of cluster‐dependent random hazard functions and on the use of mixture models induced by completely random measures. The proposed approach accommodates for different degrees of association within a cluster, which varies as a function of cluster‐level and individual covariates. Moreover, a particular specification of the proposed model has the appealing property of preserving marginally the PH structure. We illustrate the performances of the proposed modeling approach on simulated and real data sets.

Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Current approaches for studying dynamic connectivity often rely on ad-hoc approaches for inference, with the fMRI time courses segmented by a sequence of sliding windows. We propose a principled Bayesian approach to dynamic functional connectivity, which is based on the estimation of time varying networks. Our method utilizes a hidden Markov model for classification of latent cognitive states, achieving estimation of the networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. We apply our method to simulated task-based fMRI data, where we show how our approach allows the decoupling of the task-related activations and the functional connectivity states. We also analyze data from an fMRI sensorimotor task experiment on an individual healthy subject and obtain results that support the role of particular anatomical regions in modulating interaction between executive control and attention networks.

In this paper, we introduce NPBayes-fMRI, a user-friendly MATLAB GUI that implements a unified, probabilistically coherent non-parametric Bayesian framework for the analysis of task-related fMRI data from multi-subject experiments. The modeling approach is based on a spatio-temporal linear regression model that specifically accounts for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject non-parametric variable selection prior. A characteristic feature of the approach is that it results in a clustering of the subjects into subgroups characterized by similar brain responses, while simultaneously producing group-level as well as subject-level activation maps. This is distinct from two-stage “group analysis” approaches traditionally considered in the fMRI literature, which separate the inference on the individual fMRI time courses from the inference at the population level. Here, we first describe the models and a Variational Bayes algorithm for posterior inference. Next, we introduce the toolbox and illustrate its features via an example.

Discussion of “Nonparametric Bayesian Inference in Applications” by Peter Mueller, Fernando A. Quintana, Garritt Page: More Nonparametric Bayesian Inference in Applications.

Background and Objectives As a measure of nicotine dependence among adolescent smokers, the modified Fagerstrom Tolerance Questionnaire (mFTQ; seven items), has been successfully used in the United States (USA). Nonetheless, the validity and reliability of mFTQ at the international level is still needed. The current study is the first to test the validity and reliability of mFTQ in four countries: Thailand, Spain, the USA, and Russia. Methods In a cross-sectional survey, mFTQ, risk factors of nicotine dependence, and sociodemographic characteristics were assessed. Risk factors included age of first cigarette, frequency of alcohol use, frequency of marijuana use, and number of cigarettes smoked yesterday. Salivary cotinine was also obtained in Thailand and Spain. Results For all four countries, mFTQ exhibited a single factor structure, as supported by previous work in the USA. For all studied countries except Thailand, mFTQ presented acceptable internal reliability. Overall, risk factors of nicotine dependence have predicted mFTQ scores across countries. Frequency of alcohol use in the USA and frequency of marijuana use in Thailand and Spain were not associated with mFTQ scores. Discussion and Conclusions mFTQ is a single-factor measure of nicotine dependence that shows acceptable internal consistency and validity across countries. Further work can advance the scale and tailor it to different cultures. Scientific Significance mFTQ can be a clinically practical international measure of nicotine dependence. This study provides initial support for the utility of the mFTQ among Thai, Spanish, American, and Russian adolescents. Further research is needed to test and advance mFTQ across cultures. (Am J Addict 2017;XX:1–8)

In this paper, we consider the problem of modeling a matrix of count data, where multiple features are observed as counts over a number of samples. Due to the nature of the data generating mechanism, such data are often characterized by a high number of zeros and overdispersion. In order to take into account the skewness and heterogeneity of the data, some type of normalization and regularization is necessary for conducting inference on the occurrences of features across samples. We propose a zero-inflated Poisson mixture modeling framework that incorporates a model-based normalization through prior distributions with mean constraints, as well as a feature selection mechanism, which allows us to identify a parsimonious set of discriminatory features, and simultaneously cluster the samples into homogenous groups. We show how our approach improves on the accuracy of the clustering with respect to more standard approaches for the analysis of count data, by means of a simulation study and an application to a bag-of-words benchmark data set, where the features are represented by the frequencies of occurrence of each word.

The objectives of the study are (1) to distinguish lipoma (L) from atypical lipomatous tumor (ALT) using MRI qualitative features, (2) to assess the value of contrast enhancement, and (3) to evaluate the reproducibility and confidence level of radiological readings. Patients with pathologically proven L or ALT, who underwent MRI within 3 months from surgical excision were included in this retrospective multicenter international study. Two radiologists independently reviewed MRI centrally. Impressions were recorded as L or ALT. A third radiologist was consulted for discordant readings. The two radiologists re-read all non-contrast sequences; impression was recorded; then post-contrast images were reviewed and any changes were recorded. A total of 246 patients (135 females; median age, 59 years) were included. ALT was histopathologically confirmed in 70/246 patients. In multivariable analysis, in addition to the lesion size, deep location, proximal lower limb lesions, demonstrating incomplete fat suppression, or increased architectural complexity were the independent predictive features of ALT; but not the contrast enhancement. Post-contrast MRI changed the impression in a total of 5 studies (3 for R1 and 4 for R2; 2 studies are common); all of them were incorrectly changed from Ls to ALTs. Overall, inter-reader kappa agreement was 0.42 (95% CI 0.39–0.56). Discordance between the two readers was statistically significant for both pathologically proven L (p

While some individuals can defy the lure of temptation, many others find appetizing food irresistible. The goal of this study was to investigate the neuropsychological mechanisms that increase individuals' vulnerability to cue‐induced eating. Using ERPs, a direct measure of brain activity, we showed that individuals with larger late positive potentials in response to food‐related cues than to erotic images are more susceptible to cue‐induced eating and, in the presence of a palatable food option, eat more than twice as much as individuals with the opposite brain reactivity profile. By highlighting the presence of individual brain reactivity profiles associated with susceptibility to cue‐induced eating, these findings contribute to the understanding of the neurobiological basis of vulnerability to obesity., Our findings contribute to the understanding of the biological bases underlying individual differences in vulnerability to cue‐induced eating, and they represent a step toward identifying new targets for personalized weight control interventions.

In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.

© 2016 SAGE Publications. Opening and charting in the incorrect patient electronic record presents a patient safety issue. The authors investigated the prevalence of reported errors and whether efforts utilizing the anesthesia time-out and barcoding have decreased the incidence of errors in opening and charting in the patient electronic medical record in the perioperative environment. The authors queried the database for all surgeries and procedures requiring anesthesia from January 2009 to September 2012. Of the 115,760 records of anesthesia procedures identified, there were 57 instances of incorrect record opening and charting during the study period. A decreasing trend was observed for all sites combined (p < 0.0001) and at the off-site locations (p = 0.0032). All locations and the off-site locations demonstrated a statistically significant decreasing pattern of errors over time. Barcoding and the anesthesia time-out may play an important role in decreasing errors in incorrect patient record opening in the perioperative environment.

BACKGROUND Despite increasing data on the impact of the microbiome on cancer, the dynamics and role of the microbiome in infection during therapy for acute myelogenous leukemia (AML) are unknown. Therefore, the authors sought to determine correlations between microbiome composition and infectious outcomes in patients with AML who were receiving induction chemotherapy (IC). METHODS Buccal and fecal specimens (478 samples) were collected twice weekly from 34 patients with AML who were undergoing IC. Oral and stool microbiomes were characterized by 16S ribosomal RNA V4 sequencing using an Illumina MiSeq system. Microbial diversity and genera composition were associated with clinical outcomes. RESULTS Baseline stool α-diversity was significantly lower in patients who developed infections during IC compared with those who did not (P = .047). Significant decreases in both oral and stool microbial α-diversity were observed over the course of IC, with a linear correlation between α-diversity change at the 2 sites (P = .02). Loss of both oral and stool α-diversity was associated significantly with the receipt of a carbapenem P

Background Patients with cancer undergoing head and neck reconstruction can experience significant distress from alterations in appearance and bodily functioning. We sought to delineate salient dimensions of body image concerns in this patient population preparing for reconstructive surgery. Methods Participants completed self-report questionnaires evaluating numerous aspects of body image. We used Bayesian factor analysis modeling methods to identify latent factors emerging from the data. Results We identified 2 latent factors: appearance distress and functional difficulties. The highest level of preoperative body image concerns were related to distress about appearance changes and its perceived social consequences. Appearance distress items displayed greater variability compared with functional difficulties. Conclusion Appearance and functional changes to body image are important areas of concern for patients with head and neck cancer as they prepare for reconstructive surgery. Knowledge regarding specific body image issues can be used to guide psychosocial assessments and intervention to enhance patient care. © 2016 Wiley Periodicals, Inc. Head Neck, 2016

PURPOSE: The recovery pace of absolute lymphocyte count (ALC) is prognostic after hematopoietic stem cell transplantation (SCT). Previous studies have evaluated a wide range of ALC cutoffs and time points to predict outcomes. We aimed to determine the optimal ALC measure for outcome prediction after SCT from bone marrow grafts (BMT). METHODS: 518 patients who underwent BMT for acute leukemia or myelodysplastic syndrome between 1999 and 2010 were divided into training and test sets to assess the prognostic values of ALC on days 30, 60, 90, 120, 180, as well as, the first post-transplant day on which a patient achieved ALC of 100, 200, 300, 400, 500, and 1000/μL. RESULTS: In the training set, the best predictor of overall survival (OS), relapse-free survival (RFS), and non-relapse mortality (NRM) was ALC on day 60. In the whole patient cohort, multivariable analyses demonstrated significantly better OS, RFS, NRM, and lower incidence of graft-versus-host disease among patients with ALC >300/μL on day 60, both including and excluding patients who had developed graft-versus-host disease prior to day 60. Among the patient-, disease-, and transplant-related factors assessed, only busulfan-based conditioning was significantly associated with higher ALC counts on day 60 in both cohorts. CONCLUSION: The optimal ALC cutoff to predict outcomes after BMT is ALC of 300/μL on day 60 post-transplant.

One of the main hurdles to improving scaffolds for regenerative medicine is the development of non-invasive methods to monitor cell proliferation within three-dimensional environments. Recently, an electrical impedance-based approach has been identified as promising for three-dimensional proliferation assays. A low-cost impedance-based solution, easily integrable with multi-well plates, is here presented. Sensors were developed using biocompatible carbon-based ink on foldable polyimide substrates by means of a novel aerosol jet printing technique. The setup was tested to monitor the proliferation of human mesenchymal stromal cells into previously validated gelatin-chitosan hybrid hydrogel scaffolds. Reliability of the methodology was assessed comparing variations of the electrical impedance parameters with the outcomes of enzymatic proliferation assay. Results obtained showed a magnitude increase and a phase angle decrease at 4 kHz (maximum of 2.5 kΩ and &minus, 9 degrees) and an exponential increase of the modeled resistance and capacitance components due to the cell proliferation (maximum of 1.5 kΩ and 200 nF). A statistically significant relationship with enzymatic assay outcomes could be detected for both phase angle and electric model parameters. Overall, these findings support the potentiality of this non-invasive approach for continuous monitoring of scaffold-based cultures, being also promising in the perspective of optimizing the scaffold-culture system.

BACKGROUND Previous studies have not evaluated the prevalence and specific risk factors for the development of left ventricular (LV) thrombus in patients with severely reduced left ventricular dysfunction due to chemotherapy-related cardiomyopathy. We sought to evaluate the prevalence and potential markers of LV thrombus in this patient population. METHODS From January 2009 to December 2013, patients with chemotherapy-related severe LV dysfunction (LV ejection fraction [LVEF] ≤ 30%) identified from MD Anderson Cancer Center database were reviewed. Patient characteristics and echocardiographic parameters were analyzed to determine potential risk factors for LV thrombus. RESULTS A total of 121 patients met inclusion criteria (age 54.8 ± 15.2 years; female 63.6%; LVEF 26.3 ± 4%). LV thrombus was present in 9 patients (7.4%). Patients with LV thrombus have significantly lower LVEF compared to those without (18.7 ± 3.8% vs 26.9 ± 3.4%, P

PURPOSE: To design and commission a head and neck (H&N) anthropomorphic phantom that the Imaging and Radiation Oncology Core Houston (IROC-H) can use to verify the quality of intensity-modulated proton therapy H&N treatments for institutions participating in National Cancer Institute–sponsored clinical trials. MATERIALS AND METHODS: The phantom design was based on a generalized oropharyngeal tumor, including critical H&N structures (parotid glands and spinal cord). Radiochromic film and thermoluminescent dosimeter (TLD)-100 capsules were embedded in the phantom and used to evaluate dose delivery. A spot-scanning treatment plan with typical clinical constraints for H&N cancer was created by using the Eclipse analytic algorithm. The treatment plan was approved by a radiation oncologist and the phantom was irradiated 4 times. The measured dose distribution using a ±7%/4 mm gamma analysis (85% of pixels passing) and point doses were compared with the treatment planning system calculations. The prescribed target dose was 6 Gy (RBE) with 646.2 cGy (RBE) and 648.6 cGy (RBE) planned to the superior and inferior TLD, respectively. RESULTS: For point dosimetry, the average measured-to-calculated dose ratios were 0.984 and 0.986 for the superior and inferior target TLD, respectively. Dose values for the superior and inferior target TLDs were 636.1 cGy and 639.6 cGy, respectively. For the relative dose comparison, the pixel passing rates for the axial and sagittal films, respectively, were 95.5% and 94.2% for trial 1, 97.3% and 93.2% for trial 2, 93.4% and 90.0% for trial 3, and 96.2% and 92.7% for trial 4. CONCLUSION: The anthropomorphic H&N phantom was successfully designed so that TLD measured-to-calculated ratios were within IROC-H's 7% acceptance criteria, 1.6% and 1.4% lower than expected for the superior and inferior target TLDs, respectively. All trials passed the 85% pixel passing criteria established at IROC-H for the relative dose comparison performed when using a gamma index of ±7%/4 mm.

© 2017 Chiang, Guindani, Yeh, Dewar, Haneef, Stern and Vannucci. We develop an integrative Bayesian predictive modeling framework that identifies individual pathological brain states based on the selection of fluoro-deoxyglucose positron emission tomography (PET) imaging biomarkers and evaluates the association of those states with a clinical outcome. We consider data from a study on temporal lobe epilepsy (TLE) patients who subsequently underwent anterior temporal lobe resection. Our modeling framework looks at the observed profiles of regional glucose metabolism in PET as the phenotypic manifestation of a latent individual pathologic state, which is assumed to vary across the population. The modeling strategy we adopt allows the identification of patient subgroups characterized by latent pathologies differentially associated to the clinical outcome of interest. It also identifies imaging biomarkers characterizing the pathological states of the subjects. In the data application, we identify a subgroup of TLE patients at high risk for post-surgical seizure recurrence after anterior temporal lobe resection, together with a set of discriminatory brain regions that can be used to distinguish the latent subgroups. We show that the proposed method achieves high cross-validated accuracy in predicting post-surgical seizure recurrence.

Objective The process of cancer-related breast reconstruction is typically multi-staged and can take months to years to complete, yet few studies have examined patient psychosocial well-being during the reconstruction process. We investigated the effects of reconstruction timing and reconstruction stage on body image and quality of life at specific time points during the breast reconstruction process. Methods In this cross-sectional study, 216 patients were grouped into four reconstructive stages: pre-reconstruction, completed stage 1, completed stage 2, and final stages. Multiple regression analyses examined the roles of reconstruction timing (immediate vs delayed reconstruction) and reconstruction stage as well as their interaction in predicting body image and quality of life, controlling for patient age, BMI, type of reconstruction, chemotherapy, radiation therapy, and major complication(s). Results A difference in pattern of body image was observed across the reconstructive stages, with those receiving delayed reconstruction showing significant decrease in body image dissatisfaction compared with those with immediate reconstruction. At pre-reconstruction, patients awaiting delayed reconstruction reported significantly lower social well-being compared with those awaiting immediate reconstruction. Reconstruction stage predicted emotional well-being, with higher emotional well-being observed in those who had commenced reconstruction. Conclusions Timing and stage of reconstruction are important to consider when examining psychosocial outcomes of breast cancer patients undergoing reconstruction. Those waiting to initiate delayed reconstruction appear at particular risk for body image, emotional, and social distress. Our findings have implications for delivery of psychosocial treatment to maximize body image and quality of life of patients undergoing cancer-related breast reconstruction. Copyright © 2015 John Wiley & Sons, Ltd.

While some individuals can defy the lure of temptation, many others find appetizing food irresistible. The goal of this study was to investigate the neuropsychological mechanisms that increase individuals' vulnerability to cue-induced eating. Using ERPs, a direct measure of brain activity, we showed that individuals with larger late positive potentials in response to food-related cues than to erotic images are more susceptible to cue-induced eating and, in the presence of a palatable food option, eat more than twice as much as individuals with the opposite brain reactivity profile. By highlighting the presence of individual brain reactivity profiles associated with susceptibility to cue-induced eating, these findings contribute to the understanding of the neurobiological basis of vulnerability to obesity.

Many commonly used statistical methods for data analysis or clinical trial design rely on incorrect assumptions, or assume an over-simplified framework that ignores important information. Such statistical practices may lead to incorrect conclusions about treatment effects, or clinical trial designs that are impractical or that do not accurately reflect the investigator’s goals. Bayesian nonparametric (BNP) models and methods are a very flexible new class of statistical tools that can overcome such limitations. This is because BNP models can accurately approximate any distribution or function, and can accommodate a broad range of statistical problems, including density estimation, regression, survival analysis, graphical modeling, neural networks, classification, clustering, population models, forecasting and prediction, spatio-temporal models, and causal inference. This paper describes three illustrative applications of BNP methods, including a randomized clinical trial to compare treatments for intraoperative air leaks after pulmonary resection, estimating survival time with different multi-stage chemotherapy regimes for acute leukemia, and evaluating joint effects of targeted treatment and an intermediate biological outcome on progression-free survival time in prostate cancer.

Radiomics, an emerging field of quantitative imaging, encompasses a broad class of analytical techniques. Recent literature have interrogated associations between quantitatively derived GLCM-based texture features and clinical/pathology information using machine learning algorithms in many cancer settings, but often fail to elucidate the predictive power of these features. Moreover, for many cancers characterized by complex histopathological profiles, such as adrenocortical carcinoma, reducing the multivariate functional structure of GLCM to a set of summary statistics is potentially reductive, masking the patterns that distinguish malignancy from benignity. We develop a Bayesian probabilistic framework for predictive classification of lesion types, based on the entire GLCM. Our method, which uses a spatial Gaussian random field to model dependencies among neighboring cells of the GLCMs, was applied in a cancer detection context to discriminant malignant from benign adrenal lesions using GLCMs arising from non-contrast CT scans. Our method is shown to yield improved predictive power both in simulations as well as the adrenal CT application when compared to state-of-the-art diagnostic algorithms that use GLCM derived features.

Background The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. Results In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. Conclusions Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1516-0) contains supplementary material, which is available to authorized users.

© 2017 Elsevier Inc. Purpose Long-term survival of women with advanced-stage ovarian cancer is relatively rare. Little is known about quality of life (QOL) and survivorship concerns of these women. Here, we describe QOL of women with advanced-stage ovarian cancer surviving for 8.5 years or longer and compare women with 0–1 recurrence to those with multiple recurrences. Methods Participants (n = 56) recruited from 5 academic medical centers and the Ovarian Cancer Research Fund Alliance completed surveys regarding QOL (FACT-O), mood (CESD), social support (SPS), physical activity (IPAQ-SF), diet, and clinical characteristics. Median survival was 14.0 years (range 8.8–33.3). Results QOL and psychological adjustment of long-term survivors was relatively good, with mean FACT-G scores (multiple recurrences: 80.81 ± 13.95; 0–1 recurrence: 89.05 ± 10.80) above norms for healthy community samples (80.1 ± 18.1). Survivors with multiple recurrences reported more compromised QOL in domains of physical and emotional well-being (p

© 2017 The Author(s). Background: The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. Results: In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. Conclusions: Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.

To identify unique echocardiographic features that could be used to reliably predict LVEF recovery upon resolution of sinus tachycardia in patients with cancer.Sinus tachycardia may be a manifestation of underlying cardiomyopathy or can lead to a reversible form of dilated cardiomyopathy known as tachycardia-mediated cardiomyopathy. While distinguishing the two can be challenging, predicting recovery regardless of cause can be of significant clinical importance in the cancer population.Results of echocardiograms performed were collected. Patients with a repeat echocardiogram within 6 months of the initial echocardiogram were included. Patients with structural heart disease, acute coronary syndrome, sepsis, and pericardial disease were excluded. A comparison between baseline echocardiogram and subsequent echocardiogram was made to determine whether specific echocardiographic parameters predicted LVEF recovery. Two groups of patients were defined at the outset of the study. The recovered group was comprised of patients with reduced LVEF in the setting of sinus tachycardia and normal LVEF with resolution of tachycardia to normal sinus rhythm (NSR). The unrecovered group was comprised of subjects with low LVEF in the setting of both sinus tachycardia and NSR.A total of 40 patients were included in the study. LVEF in the recovered group (n=18) was 42.8% with sinus tachycardia and increased to 58.3% with NSR. Average LVEF in the unrecovered group (n=22) was 35.1% with tachycardia and improved to 38.5% with NSR. Medial TDI (E') was significantly greater in the recovered group with both tachycardia (7.95 cm/s versus 4.56 cm/s, P.001) and NSR (8.11 cm/s versus 5.13 cm/s, P.001). Similarly, lateral TDI (E') was significantly greater in the recovered group than in the unrecovered group during tachycardia (8.97 cm/s versus 5.13 cm/s, P.001) and NSR (9.05 cm/s versus 5.13 cm/s, P.001). Multivariable logistic regression analysis showed that medial TDI6.5 cm/s (OR=30.9, P=.001) and lateral TDI7.8 cm/s (OR=52.5, P=.002) are positively associated with the probability of LVEF recovery.In conclusion, TDI (medial E'6.5 cm/s; lateral E'7.8 cm/s) appears to predict LVEF recovery in patients with sinus tachycardia upon resolution of the tachycardia in patients with cancer.

Imaging genetics has rapidly emerged as a promising approach for investigating the genetic determinants of brain mechanisms that underlie an individual’s behavior or psychiatric condition. In particular, for early detection and targeted treatment of schizophrenia, it is of high clinical relevance to identify genetic variants and imaging-based biomarkers that can be used as diagnostic markers, in addition to commonly used symptom-based assessments. By combining single-nucleotide polymorphism (SNP) arrays and functional magnetic resonance imaging (fMRI), we propose an integrative Bayesian risk prediction model that allows us to discriminate between individuals with schizophrenia and healthy controls, based on a sparse set of discriminatory regions of interest (ROIs) and SNPs. Inference on a regulatory network between SNPs and ROI intensities (ROI–SNP network) is used in a single modeling framework to inform the selection of the discriminatory ROIs and SNPs. We use simulation studies to assess the performance of our method and apply it to data collected from individuals with schizophrenia and healthy controls. We found our approach to outperform competing methods that do not link the ROI–SNP network to the selection of discriminatory markers.

© Mary Ann Liebert, Inc. 2016. Background: For more than two decades, national career development programs (CDPs) have addressed underrepresentation of women faculty in academic medicine through career and leadership curricula. We evaluated CDP participation impact on retention. Methods: We used Association of American Medical Colleges data to compare 3268 women attending CDPs from 1988 to 2008 with 17,834 women and 40,319 men nonparticipant faculty similar to CDP participants in degree, academic rank, first year of appointment in rank, and home institution. Measuring from first year in rank to departure from last position held or December 2009 (study end date), we used Kaplan-Meier curves; Cox survival analysis adjusted for age, degree, tenure, and department; and 10-year rates to compare retention. Results: CDP participants were significantly less likely to leave academic medicine than their peers for up to 8 years after appointment as Assistant and Associate Professors. Full Professor participants were significantly less likely to leave than non-CDP women. Men left less often than non-CDP women at every rank. Participants attending more than one CDP left less often than those attending one, but results varied by rank. Patterns of switching institutions after 10 years varied by rank; CDP participants switched significantly less often than men at Assistant and Associate Professor levels and significantly less often than non-CDP women among Assistant Professors. Full Professors switched at equal rates. Conclusion: National CDPs appear to offer retention advantage to women faculty, with implications for faculty performance and capacity building within academic medicine. Intervals of retention advantage for CDP participants suggest vulnerable periods for intervention.

In this paper we propose a unified, probabilistically coherent framework for the analysis of task-related brain activity in multi-subject fMRI experiments. This is distinct from two-stage “group analysis” approaches traditionally considered in the fMRI literature, which separate the inference on the individual fMRI time courses from the inference at the population level. In our modeling approach we consider a spatiotemporal linear regression model and specifically account for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject nonparametric variable selection prior. For posterior inference, in addition to Markov chain Monte Carlo sampling algorithms, we develop suitable variational Bayes algorithms. We show on simulated data that variational Bayes inference achieves satisfactory results at more reduced computational costs than using MCMC, allowing scalability of our methods. In an application to data collected to assess brain responses to emotional stimuli our method correctly detects activation in visual areas when visual stimuli are presented.

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations.

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems. © 2016 Trevino et al.

© 2015, © 2015 SAGE Publications. Computed tomography perfusion (CTp) is an emerging functional imaging technology that provides a quantitative assessment of the passage of fluid through blood vessels. Tissue perfusion plays a critical role in oncology due to the proliferation of networks of new blood vessels typical of cancer angiogenesis, which triggers modifications to the vasculature of the surrounding host tissue. In this article, we consider a Bayesian semiparametric model for the analysis of functional data. This method is applied to a study of four interdependent hepatic perfusion CT characteristics that were acquired under the administration of contrast using a sequence of repeated scans over a period of 590 seconds. More specifically, our modeling framework facilitates borrowing of information across patients and tissues. Additionally, the approach enables flexible estimation of temporal correlation structures exhibited by mappings of the correlated perfusion biomarkers and thus accounts for the heteroskedasticity typically observed in those measurements, by incorporating change-points in the covariance estimation. This method is applied to measurements obtained from regions of liver surrounding malignant and benign tissues, for each perfusion biomarker. We demonstrate how to cluster the liver regions on the basis of their CTp profiles, which can be used in a prediction context to classify regions of interest provided by future patients, and thereby assist in discriminating malignant from healthy tissue regions in diagnostic settings.

Purpose: To design a Head and Neck (H&N) anthropomorphic QA phantom that the Imaging and Radiation Oncology Core Houston (IROC-H) can use to verify the quality of intensity modulated proton therapy (IMPT) H&N treatments for institutions participating in NCI clinical trials. Methods: The phantom was created to serve as a remote auditing tool for IROC-H to evaluate an institution's IMPT planning and delivery abilities. The design was based on the composition, size, and geometry of a generalized oropharyngeal tumor and contains critical structures (parotids and spinal cord). Radiochromic film in the axial and sagittal planes and thermoluminescent dosimeters (TLD)-100 capsules were embedded in the phantom and used to perform the dose delivery evaluation. A CT simulation was used to create a passive scatter and a spot scanning treatment plan with typical clinical constraints for H&N cancer. The IMPT plan was approved by a radiation oncologist and the phantom was irradiated multiple times. The measured dose distribution using a 7%/4mm gamma analysis (85% of pixels passing) and point doses were compared with the treatment planning system calculations. Results: The designed phantom could not achieve the target dose prescription and organ at risk dose constraints with the passive scatter treatment plan. The target prescription dose could be met but not the parotid dose constraint. The average TLD point dose ratio in the target was 0.975, well within the 5% acceptance criterion. The dose distribution analysis using various acceptance criteria, 5%/4mm, 5%/3mm, 7%/4mm and 7%/5mm, had average pixel passing rates of 85.9%, 81.8%, 89.6% and 91.6%, and respectively. Conclusion: An anthropomorphic IMPT H&N phantom was designed that can assess the dose delivery of proton sites wishing to participate in clinical trials using a 5% TLD dose and 7%/4mm gamma analysis acceptance criteria.

Purpose: To evaluate the use of post-irradiation changes in respiratory rate and CBCT-based morphology as predictors of survival in mice. Methods: C57L/J mice underwent whole-thorax irradiation with a Co-60 beam to four different doses [0Gy (n=3), 9Gy (n=5), 11Gy (n=7), and 13Gy (n=5)] in order to induce varying levels of pneumonitis. Respiratory rate measurements, breath-hold CBCTs, and free-breathing CBCTs were acquired pre-irradiation and at six time points between two and seven months post-irradiation. For respiratory rate measurements, we developed a novel computer-vision-based technique. We recorded mice sleeping in standard laboratory cages with a 30 fps, 1080p webcam (Logitech C920). We calculated respiratory rate using corner detection and optical flow to track cyclical motion in the fur in the recorded video. Breath-hold and free-breathing CBCTs were acquired on the X-RAD225Cx system. For breathhold imaging, the mice were intubated and their breath was held at full-inhale for 20 seconds. Healthy lung tissue was delineated in the scans using auto-threshold contouring (0–0.7 g/cm3). The volume of healthy lung was measured in each of the scans. Next, lung density was measured in a 6-mm2 ROI in a fixed anatomic location in each of the scans. Results: Day-to-day variability in respiratory rate with our technique was 13%. All metrics except for breath-hold lung volume were correlated with survival: lung density on free-breathing (r=−0.7482,p

Purpose: To simulate and measure magnetic-field-induced radiation dose effects in a mouse lung phantom. This data will be used to support pre-clinical experiments related to MRI-guided radiation therapy systems. Methods: A mouse lung phantom was constructed out of 1.5×1.5×2.0-cm3 lung-equivalent material (0.3 g/cm3) surrounded by a 0.6-cm solid water shell. EBT3 film was inserted into the phantom and the phantom was placed between the poles of an H-frame electromagnet. The phantom was irradiated with a cobalt-60 beam (1.25 MeV) with the electromagnet set to various magnetic field strengths (0T, 0.35T, 0.9T, and 1.5T). These magnetic field strengths correspond to the range of field strengths seen in MRI-guided radiation therapy systems. Dose increases at the solid-water-to-lung-interface and dose decreases at the lung-to-solid-water interface were compared with results of Monte Carlo simulations performed with MCNP6. Results: The measured dose to lung at the solid-water-to-lung interface increased by 0%, 16%, and 29% with application of the 0.35T, 0.9T, and 1.5T magnetic fields, respectively. The dose to lung at the lung-to-solid-water interface decreased by 4%, 18%, and 24% with application of the 0.35T, 0.9T, and 1.5T magnetic fields, respectively. Monte Carlo simulations showed dose increases of 0%, 16%, and 31% and dose decreases of 4%, 16%, and 25%. Conclusion: Only small dose perturbations were observed at the lung-solid-water interfaces for the 0.35T case, while more substantial dose perturbations were observed for the 0.9T and 1.5T cases. There is good agreement between the Monte Carlo calculations and the experimental measurements (within 2%). These measurements will aid in designing pre-clinical studies which investigate the potential biological effects of radiation therapy in the presence of a strong magnetic field. This work was partially funded by Elekta.

The clinical and biological effects of metabolic alterations in cancer are not fully understood.In high-grade serous ovarian cancer (HGSOC) samples (n = 101), over 170 metabolites were profiled and compared with normal ovarian tissues (n = 15). To determine NAT8L gene expression across different cancer types, we analyzed the RNA expression of cancer types using RNASeqV2 data available from the open access The Cancer Genome Atlas (TCGA) website (http://www.cbioportal.org/public-portal/). Using NAT8L siRNA, molecular techniques and histological analysis, we determined cancer cell viability, proliferation, apoptosis, and tumor growth in in vitro and in vivo (n = 6-10 mice/group) settings. Data were analyzed with the Student's t test and Kaplan-Meier analysis. Statistical tests were two-sided.Patients with high levels of tumoral NAA and its biosynthetic enzyme, aspartate N-acetyltransferase (NAT8L), had worse overall survival than patients with low levels of NAA and NAT8L. The overall survival duration of patients with higher-than-median NAA levels (3.6 years) was lower than that of patients with lower-than-median NAA levels (5.1 years, P = .03). High NAT8L gene expression in other cancers (melanoma, renal cell, breast, colon, and uterine cancers) was associated with worse overall survival. NAT8L silencing reduced cancer cell viability (HEYA8: control siRNA 90.61% ± 2.53, NAT8L siRNA 39.43% ± 3.00, P.001; A2780: control siRNA 90.59% ± 2.53, NAT8L siRNA 7.44% ± 1.71, P.001) and proliferation (HEYA8: control siRNA 74.83% ± 0.92, NAT8L siRNA 55.70% ± 1.54, P.001; A2780: control siRNA 50.17% ± 4.13, NAT8L siRNA 26.52% ± 3.70, P.001), which was rescued by addition of NAA. In orthotopic mouse models (ovarian cancer and melanoma), NAT8L silencing reduced tumor growth statistically significantly (A2780: control siRNA 0.52 g ± 0.15, NAT8L siRNA 0.08 g ± 0.17, P.001; HEYA8: control siRNA 0.79 g ± 0.42, NAT8L siRNA 0.24 g ± 0.18, P = .008, A375-SM: control siRNA 0.55 g ± 0.22, NAT8L siRNA 0.21 g ± 0.17 g, P = .001). NAT8L silencing downregulated the anti-apoptotic pathway, which was mediated through FOXM1.These findings indicate that the NAA pathway has a prominent role in promoting tumor growth and represents a valuable target for anticancer therapy.Altered energy metabolism is a hallmark of cancer (1). Proliferating cancer cells have much greater metabolic requirements than nonproliferating differentiated cells (2,3). Moreover, altered cancer metabolism elevates unique metabolic intermediates, which can promote cancer survival and progression (4,5). Furthermore, emerging evidence suggests that proliferating cancer cells exploit alternative metabolic pathways to meet their high demand for energy and to accumulate biomass (6-8).

Background Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy. Methods The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses. Results Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002). Conclusions These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0409-1) contains supplementary material, which is available to authorized users.