Psychological Impacts of Retained Bullets From the Perspective of Survivors.

INTRODUCTION: Despite a high prevalence of retained bullet fragments (RBFs) after firearm related injury (FRI) there is limited data on the full spectrum of their consequences, particularly the psychological impacts on those injured. Further, the experiences of FRI survivors with RBFs are missing from existing literature. The objective of this study was to explore the psychological impacts of RBFs on individuals who have experienced recent FRI. METHODS: Adult (18-65 years) survivors of FRI with radiographically confirmed RBFs were purposively selected from an urban Level 1 trauma center in Atlanta, Georgia, to participate in an in-depth interview. Interviews were conducted between March 2019 and February 2020. Thematic analysis was used to identify a range of psychological effects from RBFs. RESULTS: Interviews from 24 FRI survivors were analyzed: the majority of participants were Black males (N = 22, 92%) with a mean age of 32 years whose FRI occurred ∼8.6 months prior to data collection. The psychological effects of RBFs were grouped into four categories: physical health (eg, pain, limited mobility), emotional well-being (eg, anger, fear), social isolation, and occupational welfare (eg, disability leading to inability to work). A range of coping mechanisms were also identified. CONCLUSION: Survivors of FRI with RBFs experience a range of psychological impacts that are far-reaching and affect daily activities, mobility, pain and emotional wellbeing. Study results indicate a need for enhanced resources to support those with RBFs. Further, changes to clinical protocols are warranted on removal of RBFs and communication about the effects of leaving RBFs in situ.

Multicentric Hospital-Based Surveillance of Pertussis Amongst Infants Admitted in Tertiary Care Facilities in India.

OBJECTIVE: To estimate the disease and economic burden of pertussis amongst hospitalised infants in India. DESIGN: Multicentric hospital-based surveillance study. PARTICIPANTS: Hospitalised infants with clinical suspicion of pertussis based on predefined criteria. OUTCOME MEASURES: Proportion of infants with laboratory-confirmed pertussis, economic burden of pertussis amongst hospitalised infants. RESULTS: 693 clinically suspected infants were recruited of which 32 (4.62%) infants had laboratory-confirmed pertussis. Progressive cough with post-tussive emesis (50%) and pneumonia (34%) were the common clinical presentations; apnea in young infants was significantly associated with pertussis. Infants with pertussis were more likely to be younger (median age 102.5 days vs.157 days) and born preterm (42.9% vs 24.5%). Almost 30% infants with pertussis had not received vaccine for pertussis with 50% of these infants aged less than 2 months. Pertussis was associated with higher costs of hospitalisation, pharmacy and loss of working days by caregivers as compared to non-pertussis cases. CONCLUSIONS: Younger infants, those born preterm and those inadequately immunised against pertussis are at higher risk of pertussis infection. Timely childhood immunisation and introduction of maternal immunisation for pertussis can help in reducing the disease burden.

Exploring feature-based approaches in PET images for predicting cancer treatment outcomes.

Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

Toward personalized dose-prescription in locally advanced non-small cell lung cancer: Validation of published normal tissue complication probability models.

PURPOSE: To identify published normal tissue complication probability (NTCP) models suitable for patient-specific dose-prescription in locally advanced non-small cell lung cancer (LA-NSCLC) through in-house validation. MATERIAL AND METHODS: From eight previously published candidate NTCP models (≥grade 2 acute esophagitis and radiation pneumonitis; AE2, RP2), patient-specific dose-responses were calculated using model variables and fractionation-corrected doses for 241 LA-NSCLC patients treated with chemo-IMRT to 50-80 Gy@1.8-2.0 Gy between 2004 and 2014 (AE2/RP2 rate: 50%/12%). A model was judged final if it significantly predicted AE2 or RP2 (p ≤ 0.05), was discriminative and well calibrated (AUC > 0.60; Hosmer-Lemeshow test pHL > 0.05), which were assessed as the median over 1000 bootstrap samples. RESULTS: Models for AE2 had superior discrimination to RP2 models (AUC = 0.63-0.65 vs. 0.51-0.65). The final AE2 model included mean esophageal dose and concurrent chemotherapy (AUC = 0.65; p < 0.0001). The final RP2 model was a slightly adjusted version of the RP2 model with the best discrimination, and included age, mean lung dose, and pulmonary comorbidity (AUC = 0.73; p < 0.0001). CONCLUSION: Of the eight investigated and published NTCP models, one model successfully described AE2 and one slightly adjusted model successfully described RP2 in the independent cohort. Estimates from these two NTCP models will, therefore, be considered internally when prescribing patient-specific doses in LA-NSCLC patients.

Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose-volume outcome relationships.

Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another open-source tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.

Single-stage immediate breast reconstruction with acellular dermal matrix: Experience gained and lessons learnt from patient reported outcome measures.

INTRODUCTION: Acellular Dermal Matrix (ADM) assisted breast reconstruction has transformed the single-stage Immediate Breast Reconstruction (IBR) with an impact on the cosmetic outcomes. However, there is limited data available on patient reported outcomes. This study highlights the Patient Reported Outcome Measures (PROMs), post-operative complications and lessons learnt from ADM assisted single-stage immediate breast reconstruction. METHODS: This prospective study enrolled consecutive patients from Feb 2012 - May 2015 undergoing mastectomy with direct-to-implant ADM assisted breast reconstruction, using Strattice™ (Acelity, San Antonio, TX, USA). Patients were recruited from the beginning of our unit's use of ADMs and completed a post-operative questionnaire at 6 weeks, covering pre-operative, operative and post-operative outcomes. Information on tumour biology and post-operative complications was obtained from the medical notes. RESULTS: This study included 49 patients undergoing a total of 53 procedures. Following surgery 93.3% of women reported a high level of body confidence when clothed. 6.7% of patients reported severe post-operative pain during the first week. Mean length of hospital stay was 1.7 days, return to light activities was within 2.5 weeks and normal activities in 5.4 weeks. Implant loss at 3 months occurred in 5.7% of procedures, of which two thirds were smokers. CONCLUSIONS: PROMs for Strattice™ ADM based reconstruction show high levels of satisfaction with cosmetic outcomes, low incidences of severe post-operative pain and a short recovery process. PROMs help us to better describe patients' experience, allowing women to make more informed choices about ADM based breast reconstruction, which reassures and helps to achieve better outcomes.

Anchor-ligated cDNA libraries: a technique for generating a cDNA library for the immediate cloning of the 5' ends of mRNAs.

A new, efficient and simple method based on single-stranded anchor ligation has been developed that allows for the generation of an anchor-ligated library from which the 5' ends in many mRNAs can be cloned. Random hexamers are used to prime poly A+ RNA for the first strand synthesis of a cDNA library, and a modified single-stranded anchor is then ligated to the 3' end of the cDNA using T4 RNA ligase. The library can then be used to amplify the 5' end of any mRNA expressed in the tissue. The efficiency and utility of this method has been demonstrated by amplification of three human genes: transferrin receptor, tissue-type plasminogen activator and beta actin.

Influence of image slice thickness on rectal dose-response relationships following radiotherapy of prostate cancer.

When pooling retrospective data from different cohorts, slice thicknesses of acquired computed tomography (CT) images used for treatment planning may vary between cohorts. It is, however, not known if varying slice thickness influences derived dose-response relationships. We investigated this for rectal bleeding using dose-volume histograms (DVHs) of the rectum and rectal wall for dose distributions superimposed on images with varying CT slice thicknesses. We used dose and endpoint data from two prostate cancer cohorts treated with three-dimensional conformal radiotherapy to either 74 Gy (N = 159) or 78 Gy (N = 159) at 2 Gy per fraction. The rectum was defined as the whole organ with content, and the morbidity cut-off was Grade ≥2 late rectal bleeding. Rectal walls were defined as 3 mm inner margins added to the rectum. DVHs for simulated slice thicknesses from 3 to 13 mm were compared to DVHs for the originally acquired slice thicknesses at 3 and 5 mm. Volumes, mean, and maximum doses were assessed from the DVHs, and generalized equivalent uniform dose (gEUD) values were calculated. For each organ and each of the simulated slice thicknesses, we performed predictive modeling of late rectal bleeding using the Lyman-Kutcher-Burman (LKB) model. For the most coarse slice thickness, rectal volumes increased (≤18%), whereas maximum and mean doses decreased (≤0.8 and ≤4.2 Gy, respectively). For all a values, the gEUD for the simulated DVHs were ≤1.9 Gy different than the gEUD for the original DVHs. The best-fitting LKB model parameter values with 95% CIs were consistent between all DVHs. In conclusion, we found that the investigated slice thickness variations had minimal impact on rectal dose-response estimations. From the perspective of predictive modeling, our results suggest that variations within 10 mm in slice thickness between cohorts are unlikely to be a limiting factor when pooling multi-institutional rectal dose data that include slice thickness variations within this range.

WE-E-BRB-11: Riview a Web-Based Viewer for Radiotherapy.

PURPOSE: Collaborations involving radiotherapy data collection, such as the recently proposed international radiogenomics consortium, require robust, web-based tools to facilitate reviewing treatment planning information. We present the architecture and prototype characteristics for a web-based radiotherapy viewer. METHODS: The web-based environment developed in this work consists of the following components: 1) Import of DICOM/RTOG data: CERR was leveraged to import DICOM/RTOG data and to convert to database friendly RT objects. 2) Extraction and Storage of RT objects: The scan and dose distributions were stored as .png files per slice and view plane. The file locations were written to the MySQL database. Structure contours and DVH curves were written to the database as numeric data. 3) Web interfaces to query, retrieve and visualize the RT objects: The Web application was developed using HTML 5 and Ruby on Rails (RoR) technology following the MVC philosophy. The open source ImageMagick library was utilized to overlay scan, dose and structures. The application allows users to (i) QA the treatment plans associated with a study, (ii) Query and Retrieve patients matching anonymized ID and study, (iii) Review up to 4 plans simultaneously in 4 window panes (iv) Plot DVH curves for the selected structures and dose distributions. RESULTS: A subset of data for lung cancer patients was used to prototype the system. Five user accounts were created to have access to this study. The scans, doses, structures and DVHs for 10 patients were made available via the web application. CONCLUSIONS: A web-based system to facilitate QA, and support Query, Retrieve and the Visualization of RT data was prototyped. The RIVIEW system was developed using open source and free technology like MySQL and RoR. We plan to extend the RIVIEW system further to be useful in clinical trial data collection, outcomes research, cohort plan review and evaluation.

SU-E-T-259: A Statistical and Machine Learning-Based Tool for Modeling and Visualization of Radiotherapy Treatment Outcomes.

PURPOSE: Effective radiotherapy outcomes modeling could provide physicians with better understanding of the underlying disease mechanism, enabling to early predict outcomes and ultimately allowing for individualizing treatment for patients at high risk. This requires not only sophisticated statistical methods, but user-friendly visualization and data analysis tools. Unfortunately, few tools are available to support these requirements in radiotherapy community. METHODS: Our group has developed Matlab-based in-house software called DREES for statistical modeling of radiotherapy treatment outcomes. We have noticed that advanced machine learning techniques can be used as useful tools for analyzing and modeling the outcomes data. To this end, we have upgraded DREES such that it takes advantage of useful Statistics and Bioinformatics toolboxes in Matlab that provide robust statistical data modeling and analysis methods as well as user-friendly visualization and graphical interface. RESULTS: Newly added key features include variable selection, discriminant analysis and decision tree for classification, and k-means and hierarchical clustering functions. Also, existing graphical tools and statistical methods in DREES were replaced with a library of the Matlab toolboxes. We analyzed several radiotherapy outcomes datasets with our tools and showed that these can be effectively used for building normal tissue complication probability (NTCP) and tumor control probability (TCP) models. CONCLUSIONS: We have developed an integrated software tool for modeling and visualization of radiotherapy outcomes data within the Matlab programming environment. It is our expectation that this tool could help physicians and scientists better understand the complex mechanism of disease and identify clinical and biological factors related to outcomes.

SU-E-T-620: Computational Boundary Sampling to Accelerate IMRT Optimization.

PURPOSE: To reduce the time and memory requirements of Intensity Modulated Radiation Therapy (IMRT) treatment planning. METHODS: We propose a new sampling method, called Computational Boundary Sampling (CBS) for IMRT optimization, which samples all the boundary voxels and a certain percentage of inner voxels of each region of interest (ROI). Within CBS, we developed a grid-based sampling method for choosing inner voxels. In this method, each region is first evenly gridded and then sampling points are randomly selected from each sub-volume. We also developed a supporting theory to quantify the solution quality of CBS. We compared a variant of CBS that always keeps boundary voxels and a variant of CBS that does not. Finally, we quantified the impact of CBS on 10 different anonymized, clinical treatment cases using a prioritized prescription optimization method, including compute time, required memory and objective function values. RESULTS: (1) We have found that the D95 of the targets are generally 4% larger when boundary voxels are included. (2) Grid sampling, compared to completely random sampling, yields more uniformly distributed sampling, with better solution quality, and less variance between independent runs, using the same or less time. (3) We have compared our original IMRT optimization solver without sampling and the solver combined with CBS sampling. The result showed that CBS can reduce the solution time and memory consumption by up to 20x with < 2% change in dosimetric variables. CONCLUSIONS: We have proposed a new sampling method (CBS), along with corresponding new techniques including boundary sampling and grid sampling, to improve time and space efficiency of IMRT optimization. A corresponding theory is developed to quantify the error bound. Experimental results have shown that our new methods significantly reduce solution time and memory costs with negligible impact on resulting plan quality.

SU-E-J-92: CERR: New Tools to Analyze Image Registration Precision.

PURPOSE: To present new tools in CERR (The Computational Environment for Radiotherapy Research) to analyze image registration and other software updates/additions. METHODS: CERR continues to be a key environment (cited more than 129 times to date) for numerous RT-research studies involving outcomes modeling, prototyping algorithms for segmentation, and registration, experiments with phantom dosimetry, IMRT research, etc. Image registration is one of the key technologies required in many research studies. CERR has been interfaced with popular image registration frameworks like Plastimatch and ITK. Once the images have been autoregistered, CERR provides tools to analyze the accuracy of registration using the following innovative approaches (1)Distance Discordance Histograms (DDH), described in detail in a separate paper and (2)'MirrorScope', explained as follows: for any view plane the 2-d image is broken up into a 2d grid of medium-sized squares. Each square contains a right-half, which is the reference image, and a left-half, which is the mirror flipped version of the overlay image. The user can increase or decrease the size of this grid to control the resolution of the analysis. Other updates to CERR include tools to extract image and dosimetric features programmatically and storage in a central database and tools to interface with Statistical analysis software like SPSS and Matlab Statistics toolbox. RESULTS: MirrorScope was compared on various examples, including 'perfect' registration examples and 'artificially translated' registrations. for 'perfect' registration, the patterns obtained within each circles are symmetric, and are easily, visually recognized as aligned. For registrations that are off, the patterns obtained in the circles located in the regions of imperfections show unsymmetrical patterns that are easily recognized. CONCLUSIONS: The new updates to CERR further increase its utility for RT-research. Mirrorscope is a visually intuitive method of monitoring the accuracy of image registration that improves on the visual confusion of standard methods.

WE-E-213CD-11: A New Automatically Generated Metric for Evaluating the Spatial Precision of Deformable Image Registrations: The Distance Discordance Metric.

PURPOSE: We propose a new metric called Distance Discordance (DD), which is defined as the distance between two anatomic points from two moving images, which are co-located on some reference image, when deformed onto another reference image. METHODS: To demonstrate the concept of DD, we created a reference software phantom which contains two objects. The first object (1) consists of a hollow box with a fixed size core and variable wall thickness. The second object (2) consists of a solid box of fixed size and arbitrary location. 7 different variations of the fixed phantom were created. Each phantom was deformed onto every other phantom using two B-Spline DIR algorithms available in Elastix and Plastimatch. Voxels were sampled from the reference phantom [1], which were also deformed from moving phantoms [2…6], and we find the differences in their corresponding location on phantom [7]. Each voxel results in a distribution of DD values, which we call distance discordance histogram (DDH). We also demonstrate this concept in 8 Head & Neck patients. RESULTS: The two image registration algorithms produced two different DD results for the same phantom image set. The mean values of the DDH were slightly lower for Elastix (0-1.28 cm) as compared to the values produced by Plastimatch (0-1.43 cm). The combined DDH for the H&N patients followed a lognormal distribution with a mean of 0.45 cm and std. deviation of 0.42 cm. CONCLUSIONS: The proposed distance discordance (DD) metric is an easily interpretable, quantitative tool that can be used to evaluate the effect of inter-patient variability on the goodness of the registration in different parts of the patient anatomy. Therefore, it can be utilized to exclude certain images based on their DDH characteristics. In addition, this metric does not rely on 'ground truth' or the presence of contoured structures. Partially supported by NIH grant R01 CA85181.

Cellular automata modelling of biomolecular networks dynamics.

The modelling of biological systems dynamics is traditionally performed by ordinary differential equations (ODEs). When dealing with intracellular networks of genes, proteins and metabolites, however, this approach is hindered by network complexity and the lack of experimental kinetic parameters. This opened the field for other modelling techniques, such as cellular automata (CA) and agent-based modelling (ABM). This article reviews this emerging field of studies on network dynamics in molecular biology. The basics of the CA technique are discussed along with an extensive list of related software and websites. The application of CA to networks of biochemical reactions is exemplified in detail by the case studies of the mitogen-activated protein kinase (MAPK) signalling pathway, the FAS-ligand (FASL)-induced and Bcl-2-related apoptosis. The potential of the CA method to model basic pathways patterns, to identify ways to control pathway dynamics and to help in generating strategies to fight with cancer is demonstrated. The different line of CA applications presented includes the search for the best-performing network motifs, an analysis of importance for effective intracellular signalling and pathway cross-talk.

Application of DNA diffusion assay in earthworm coelomocytes.

We have applied the DNA diffusion assay proposed by Singh (2000) Exp Cell Res 256:328-337, for quantitative estimation of apoptosis in earthworm coelomocytes, exposed to Chromium (VI) and cypermethrin as model toxicants in vitro. The DNA diffusion assay was originally described for mammalian cells. H2O2, Sodium ascorbate, and hyperthermia were used as positive controls in present study. Apoptosis such as DNA diffusion occurred in dose-dependent manner for Chromium (VI) and cypermethrin at very low concentration (1, 3, and 10 ppm for Chromium (VI) and 4, 8, and 16 ppm for cypermethrin). Three distinct patterns (apoptosis like DNA diffusion, necrosis, and normal) were observed in exposed and nonexposed cells. Present study is probably the first report of application of the DNA diffusion technique in earthworm coelomocytes. Findings of this study indicate that this assay has potential for use in invertebrate cells to differentiate between apoptosis and necrosis.

Breakup of shearless meanders and "outer" tori in the standard nontwist map.

The breakup of shearless invariant tori with winding number omega=(11+gamma)(12+gamma) (in continued fraction representation) of the standard nontwist map is studied numerically using Greene's residue criterion. Tori of this winding number can assume the shape of meanders [folded-over invariant tori which are not graphs over the x axis in (x,y) phase space], whose breakup is the first point of focus here. Secondly, multiple shearless orbits of this winding number can exist, leading to a new type of breakup scenario. Results are discussed within the framework of the renormalization group for area-preserving maps. Regularity of the critical tori is also investigated.

Meanders and reconnection-collision sequences in the standard nontwist map.

New global periodic orbit collision and separatrix reconnection scenarios exhibited by the standard nontwist map are described in detail, including exact methods for determining reconnection thresholds, methods that are implemented numerically. Results are compared to a parameter space breakup diagram for shearless invariant curves. The existence of meanders, invariant tori that are not graphs, is demonstrated numerically for both odd and even period reconnection for certain regions in parameter space. Implications for transport are discussed.

Renormalization and destruction of 1/gamma2 tori in the standard nontwist map.

Extending the work of del-Castillo-Negrete, Greene, and Morrison [Physica D 91, 1 (1996); 100, 311 (1997)] on the standard nontwist map, the breakup of an invariant torus with winding number equal to the inverse golden mean squared is studied. Improved numerical techniques provide the greater accuracy that is needed for this case. The new results are interpreted within the renormalization group framework by constructing a renormalization operator on the space of commuting map pairs, and by studying the fixed points of the so constructed operator.