Development of automated image analysis tools for verification of radiotherapy field accuracy with an electronic portal imaging device

The successful management of cancer with radiation relies on the accurate deposition of a prescribed dose to a prescribed anatomical volume within the patient. Treatment set-up errors are inevitable because the alignment of field shaping devices with the patient must be repeated daily up to eighty times during the course of a fractionated radiotherapy treatment. With the invention of electronic portal imaging devices (EPIDs), patient's portal images can be visualized daily in real-time after only a small fraction of the radiation dose has been delivered to each treatment field. However, the accuracy of human visual evaluation of low-contrast portal images has been found to be inadequate. The goal of this research is to develop automated image analysis tools to detect both treatment field shape errors and patient anatomy placement errors with an EPID. A moments method has been developed to align treatment field images to compensate for lack of repositioning precision of the image detector. A figure of merit has also been established to verify the shape and rotation of the treatment fields. Following proper alignment of treatment field boundaries, a cross-correlation method has been developed to detect shifts of the patient's anatomy relative to the treatment field boundary. Phantom studies showed that the moments method aligned the radiation fields to within 0.5mm of translation and 0.5$\sp\circ$ of rotation and that the cross-correlation method aligned anatomical structures inside the radiation field to within 1 mm of translation and 1$\sp\circ$ of rotation. A new procedure of generating and using digitally reconstructed radiographs (DRRs) at megavoltage energies as reference images was also investigated. The procedure allowed a direct comparison between a designed treatment portal and the actual patient setup positions detected by an EPID. Phantom studies confirmed the feasibility of the methodology. Both the moments method and the cross-correlation technique were implemented within an experimental radiotherapy picture archival and communication system (RT-PACS) and were used clinically to evaluate the setup variability of two groups of cancer patients treated with and without an alpha-cradle immobilization aid. The tools developed in this project have proven to be very effective and have played an important role in detecting patient alignment errors and field-shape errors in treatment fields formed by a multileaf collimator (MLC). ^

Dielectrophoretic approaches to sample preparation and analysis

Dielectrophoresis (DEP) has been used to manipulate cells in low-conductivity suspending media using AC electrical fields generated on micro-fabricated electrode arrays. This has created the possibility of performing automatically on a micro-scale more sophisticated cell processing than that currently requiring substantial laboratory equipment, reagent volumes, time, and human intervention. In this research the manipulation of aqueous droplets in an immiscible, low-permittivity suspending medium is described to complement previous work on dielectrophoretic cell manipulation. Such droplets can be used as carriers not only for air- and water-borne samples, contaminants, chemical reagents, viral and gene products, and cells, but also the reagents to process and characterize these samples. A long-term goal of this area of research is to perform chemical and biological assays on automated, micro-scaled devices at or near the point-of-care, which will increase the availability of modern medicine to people who do not have ready access to large medical institutions and decrease the cost and delays associated with that lack of access. In this research I present proofs-of-concept for droplet manipulation and droplet-based biochemical analysis using dielectrophoresis as the motive force. Proofs-of-concept developed for the first time in this research include: (1) showing droplet movement on a two-dimensional array of electrodes, (2) achieving controlled dielectric droplet injection, (3) fusing and reacting droplets, and (4) demonstrating a protein fluorescence assay using micro-droplets. ^

Hemodynamic changes associated with manual and automated lateral rotation in mechanically ventilated intensive care patients

Objective: To investigate hemodynamic responses to lateral rotation. ^ Design: Time-series within a randomized controlled trial pilot study. ^ Setting: A medical intensive care unit (ICU) and a medical-surgical ICU in two tertiary care hospitals. ^ Patients: Adult patients receiving mechanical ventilation. ^ Interventions: Two-hourly manual or continuous automated lateral rotation. ^ Measurements and Main Results: Heart rate (HR) and arterial pressure were sampled every 6 seconds for > 24 hours, and pulse pressure (PP) was computed. Turn data were obtained from a turning flow sheet (manual turn) or with an angle sensor (automated turn). Within-subject ensemble averages were computed for HR, mean arterial pressure (MAP), and PP across turns. Sixteen patients were randomized to either the manual (n = 8) or automated (n = 8) turn. Three patients did not complete the study due to hemodynamic instability, bed malfunction or extubation, leaving 13 patients (n = 6 manual turn and n = 7 automated turn) for analysis. Seven patients (54%) had an arterial line. Changes in hemodynamic variables were statistically significant increases ( p < .05), but few changes were clinically important, defined as ≥ 10 bpm (HR) or ≥ 10 mmHg (MAP and PP), and were observed only in the manual-turn group. All manual-turn patients had prolonged recovery to baseline in HR, MAP and PP of up to 45 minutes (p ≤ .05). No significant turning-related periodicities were found for HR, MAP, or PP. Cross-correlations between variables showed variable lead-lag relations in both groups. A statistically, but not clinically, significant increase in HR of 3 bpm was found for the manual-turn group in the back compared with the right lateral position ( F = 14.37, df = 1, 11, p = .003). ^ Conclusions: Mechanically ventilated critically ill patients experience modest hemodynamic changes with manual lateral rotation. A clinically inconsequential increase in HR, MAP, and PP may persist for up to 45 minutes. Automated lateral rotation has negligible hemodynamic effects. ^

Automated soldier medical readiness: Prevalence of " medically not ready" using data from the electronic soldier readiness processing system (ESRP)

Identifying accurate numbers of soldiers determined to be medically not ready after completing soldier readiness processing may help inform Army leadership about ongoing pressures on the military involved in long conflict with regular deployment. In Army soldiers screened using the SRP checklist for deployment, what is the prevalence of soldiers determined to be medically not ready? Study group. 15,289 soldiers screened at all 25 Army deployment platform sites with the eSRP checklist over a 4-month period (June 20, 2009 to October 20, 2009). The data included for analysis included age, rank, component, gender and final deployment medical readiness status from MEDPROS database. Methods.^ This information was compiled and univariate analysis using chi-square was conducted for each of the key variables by medical readiness status. Results. Descriptive epidemiology Of the total sample 1548 (9.7%) were female and 14319 (90.2%) were male. Enlisted soldiers made up 13,543 (88.6%) of the sample and officers 1,746 (11.4%). In the sample, 1533 (10.0%) were soldiers over the age of 40 and 13756 (90.0%) were age 18-40. Reserve, National Guard and Active Duty made up 1,931 (12.6%), 2,942 (19.2%) and 10,416 (68.1%) respectively. Univariate analysis. Overall 1226 (8.0%) of the soldiers screened were determined to be medically not ready for deployment. Biggest predictive factor was female gender OR (2.8; 2.57-3.28) p<0.001. Followed by enlisted rank OR (2.01; 1.60-2.53) p<0.001. Reserve component OR (1.33; 1.16-1.53) p<0.001 and Guard OR (0.37; 0.30-0.46) p<0.001. For age > 40 demonstrated OR (1.2; 1.09-1.50) p<0.003. Overall the results underscore there may be key demographic groups relating to medical readiness that can be targeted with programs and funding to improve overall military medical readiness.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.