Personalized Treatment Selection via Product Partition Models with Covariates

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model-based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the Normalized Generalized Gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model-based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment.Comment: 31 pages, 7 figure

Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes

The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS463 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Calibration strategies for use of the nanoDot OSLD in CT applications.

Aluminum oxide based optically stimulated luminescent dosimeters (OSLD) have been recognized as a useful dosimeter for measuring CT dose, particularly for patient dose measurements. Despite the increasing use of this dosimeter, appropriate dosimeter calibration techniques have not been established in the literature; while the manufacturer offers a calibration procedure, it is known to have relatively large uncertainties. The purpose of this work was to evaluate two clinical approaches for calibrating these dosimeters for CT applications, and to determine the uncertainty associated with measurements using these techniques. Three unique calibration procedures were used to calculate dose for a range of CT conditions using a commercially available OSLD and reader. The three calibration procedures included calibration (a) using the vendor-provided method, (b) relative to a 120 kVp CT spectrum in air, and (c) relative to a megavoltage beam (implemented with 60 Co). The dose measured using each of these approaches was compared to dose measured using a calibrated farmer-type ion chamber. Finally, the uncertainty in the dose measured using each approach was determined. For the CT and megavoltage calibration methods, the dose measured using the OSLD nanoDot was within 5% of the dose measured using an ion chamber for a wide range of different CT scan parameters (80-140 kVp, and with measurements at a range of positions). When calibrated using the vendor-recommended protocol, the OSLD measured doses were on average 15.5% lower than ion chamber doses. Two clinical calibration techniques have been evaluated and are presented in this work as alternatives to the vendor-provided calibration approach. These techniques provide high precision for OSLD-based measurements in a CT environment

Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer's Disease

Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity which may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches.Comment: Accepted to Biometrics January 202

Bayesian Inference of Networks Across Multiple Sample Groups and Data Types

In this paper, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sample groups and platforms. In addition, our model formulation allows the number of variables and number of subjects to differ across the data types, and only requires that we have data for the same set of groups. We illustrate the proposed approach through both simulation studies and an application to gene expression levels and metabolite abundances on subjects with varying severity levels of Chronic Obstructive Pulmonary Disease (COPD)

A BAYESIAN GRAPHICAL MODELING APPROACH TO MICRORNA REGULATORY NETWORK INFERENCE

It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in animals and plants. Genes regulated by miRNAs are called targets. Typically, methods for target prediction are based solely on sequence data and on the structure information. In this paper we propose a Bayesian graphical modeling approach that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information. We use a directed graphical model with a particular structure adapted to our data based on biological considerations. We then achieve network inference using stochastic search methods for variable selection that allow us to explore the huge model space via MCMC. A time-dependent coefficients model is also implemented. We consider experimental data from a study on a very well-known developmental toxicant causing neural tube defects, hyperthermia. Some of the pairs of target gene and miRNA we identify seem very plausible and warrant future investigation. Our proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS360 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Development and implementation of an anthropomorphic pediatric spine phantom for the assessment of craniospinal irradiation procedures in proton therapy

Purpose: To design an anthropomorphic pediatric spine phantom for use in the evaluation of proton therapy facilities for clinical trial participation by the Imaging and Radiation Oncology Core (IROC) Houston QA Center (formerly RPC).Methods: This phantom was designed to perform an end-to-end audit of the proton spine treatment process, including simulation, dose calculation by the treatment planning system (TPS), and proton treatment delivery. The design incorporated materials simulating the thoracic spinal column of a pediatric patient, along with two thermoluminescent dosimeter (TLD)-100 capsules and radiochromic film embedded in the phantom for dose evaluation. Fourteen potential materials were tested to determine relative proton stopping power (RSP) and Hounsfield unit (HU) values. Each material was CT scanned at 120 kVp, and the RSP was obtained from depth ionization scans using the Zebra multi-layer ion chamber (MLIC) at two energies: 160 MeV and 250 MeV. To determine tissue equivalency, the measured RSP for each material was compared to the RSP calculated by the Eclipse TPS for a given HU.Results: The materials selected as bone, tissue, and cartilage substitutes were Techron HPV Bearing Grade (Boedeker Plastics, Inc.), solid water, and blue water, respectively. The RSP values did not differ by more than 1.8% between the two energies. The measured RSP for each selected material agreed with the RSP calculated by the Eclipse TPS within 1.2%.Conclusion: An anthropomorphic pediatric proton spine phantom was designed to evaluate proton therapy delivery. The inclusion of multiple tissue substitutes increases heterogeneity and the level of difficulty for institutions to successfully treat the phantom. The following attributes will be evaluated: absolute dose agreement, distal range, field width, junction match and right/left dose profile alignment. The phantom will be tested at several institutions using a 5% dose agreement criterion, and a 5%/3mm gamma analysis criterion for the film planes.--------------------------------------Cite this article as: Lewis DJ, Summers PA, Followill DS, Sahoo N, Mahajan A, Stingo FC, Kry SF. Development and implementation of an anthropomorphic pediatric spine phantom for the assessment of craniospinal irradiation procedures in proton therapy. Int J Cancer Ther Oncol 2014; 2(2):020227. DOI: 10.14319/ijcto.0202.2

Evaluation of three commercial metal artifact reduction methods for CT simulations in radiation therapy

Purpose: To evaluate the success of three commercial metal artifact reduction methods (MAR) in the context of radiation therapy treatment planning.Methods: Three MAR strategies were evaluated: Philips O-MAR, monochromatic imaging using Gemstone Spectral Imaging (GSI) dual energy CT, and monochromatic imaging with metal artifact reduction software (GSI-MARs). The Gammex RMI 467 tissue characterization phantom with several metal rods and two anthropomorphic phantoms (pelvic phantom with hip prosthesis and head phantom with dental fillings), were scanned with and without metals (baseline). Each MAR method was evaluated based on CT number accuracy, metal size accuracy, and reduction in the severity of streak artifacts. CT number difference maps between the baseline and metal scan images were calculated, and the severity of streak artifacts was quantified using the percentage of pixels with > 40 HU error (“bad pixels”).Results: Philips O-MAR generally reduced HU errors in the RMI phantom. However, increased errors and induced artifacts were observed for lung materials. GSI monochromatic 70keV images generally showed similar HU errors as conventional 120kVp imaging, while 140keV images reduced HU errors. All the imaging techniques represented the diameter of a stainless steel rod to within ±1.6mm (2 pixels). For the hip prosthesis, O-MAR reduced the average % bad pixels from 47% to 32%. For GSI 140keV imaging, the % bad pixels was reduced from 37% to 29% compared to 120kVp imaging, and GSI-MARs further reduced it to 12%. For the head phantom, none of the MAR methods was particularly successful.Conclusion: O-MAR resulted in consistent artifact reduction but exhibited induced artifacts for metals located near lung tissue. GSI imaging at 140keV gave consistent reduction in HU errors and severity of artifacts. GSI-MARs at 140keV was the most successful MAR method for the hip prosthesis but exhibited induced artifacts at the edges of metals in some cases.---------------------------------Cite this article as: Huang JY, Kerns JR, Nute JL, Liu X, Stingo FC, Followill DS, Mirkovic D, Howell RM, Kry SF. Evaluation of three commercial metal artifact reduction methods for CT simulations in radiation therapy. Int J Cancer Ther Oncol 2014; 2(2):020224. DOI: 10.14319/ijcto.0202.2