QUANTITATIVE IMAGING FOR PRECISION MEDICINE IN HEAD AND NECK CANCER PATIENTS

The purpose of this work was to determine if prediction models using quantitative imaging measures in head and neck squamous cell carcinoma (HNSCC) patients could be improved when noise due to imaging was reduced. This was investigated separately for salivary gland function using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), overall survival using computed tomography (CT)-based radiomics, and overall survival using positron emission tomography (PET)-based radiomics. From DCE-MRI, where T1-weighted images are serially acquired after injection of contrast, quantitative measures of diffusion can be obtained from the series of images. Radiomics is the study of the relationship of voxels to one another providing measures of texture from the area of interest. Quantitative information obtained from imaging could help in radiation treatment planning by providing quantifiable spatial information with computational models for assigning dose to regions to improve patient outcome, both survival and quality of life. By reducing the noise within the quantitative data, the prediction accuracy could improve to move this type of work closer to clinical practice. For each imaging modality sources of noise that could impact the patient analysis were identified, quantified, and if possible minimized during the patient analysis. In MRI, a large potential source of uncertainty was the image registration. To evaluate this, both physical and synthetic phantoms were used, which showed that registration of MR images was high, with all root mean square errors below 3 mm. Then, 15 HNSCC patients with pre-, mid-, and post-treatment DCE-MRI scans were evaluated. However, differences in algorithm output were found to be a large source of noise as different algorithms could not consistently rank patients as above or below the median for quantitative metrics from DCE-MRI. Therefore, further analysis using this modality was not pursued. In CT, a large potential source of noise that could impact patient analysis was the inter-scanner variability. To investigate this a controlled protocol was designed and used to image, along with the local head and chest protocols, a radiomics phantom on 100 CT scanners. This demonstrated that the inter-scanner variability could be reduced by over 50% using a controlled protocol compared to local protocols. Additionally, it was shown that the reconstruction parameters impact feature values while most acquisition parameters do not, therefore, most of this benefit can be achieved using a radiomics reconstruction with no additional dose to the patient. Then to evaluate this impact in patient studies, 726 HNSCC patients with CT images were used to create and test a Cox proportional hazards model for overall survival. Those patients with the same imaging protocol were subset and a new Cox proportional hazards model was created and tested in order to determine if the reduction in noise due to controlling the imaging protocol translated into improved prediction. However, noise between patient populations from different institutions was shown to be larger than the reduction in noise due to a controlled imaging protocol. In PET, a large potential source of noise that could impact patient analysis was the imaging protocol. A phantom scanned on three different scanners and vendors demonstrated that on a single vendor, imaging parameter choices did not affect radiomics feature values, but inter-scanner variances could be large. Then, 686 HNSCC patients with PET images were used to create and test a Cox proportional hazards model for overall survival. Those patients with the same imaging protocol were subset and a new Cox proportional hazards model was created and tested in order to determine if the reduction in noise due to controlling the imaging protocol on a vendor translated into improved prediction. However, no predictive radiomics signature could be determined for any subset of the patient cohort that resulted in significant stratification of patients into high and low risk. This study demonstrated that the imaging variability could be quantified and controlled for in each modality. However, for each modality there were larger sources of noise identified that did not allow for improvement in prediction modeling of salivary gland function or overall survival using quantitative imaging metrics for MRI, CT, or PET

Physiologically gated microbeam radiation using a field emission x-ray source array

Microbeam radiation therapy (MRT) uses narrow planes of high dose radiation beams to treat cancerous tumors. This experimental therapy method based on synchrotron radiation has been shown to spare normal tissue at up to 1000 Gy of peak entrance dose while still being effective in tumor eradication and extending the lifetime of tumor-bearing small animal models. Motion during treatment can lead to significant movement of microbeam positions resulting in broader beam width and lower peak to valley dose ratio (PVDR), which reduces the effectiveness of MRT. Recently, the authors have demonstrated the feasibility of generating microbeam radiation for small animal treatment using a carbon nanotube (CNT) x-ray source array. The purpose of this study is to incorporate physiological gating to the CNT microbeam irradiator to minimize motion-induced microbeam blurring

RESCALE: Review and Simulate Climate and Catchment Responses at Burrishoole : Marine Research Sub-Programme (NDP 2007-’13) Series. ISSN 2009-3195

Abstract included in text

Towards a Framework for Understanding Fairtrade Purchase Intention in the Mainstream Environment of Supermarkets

© 2014, Springer Science+Business Media Dordrecht. Despite growing interest in ethical consumer behaviour research, ambiguity remains regarding what motivates consumers to purchase ethical products. While researchers largely attribute the growth of ethical consumerism to an increase in ethical consumer concerns and motivations, widened distribution (mainstreaming) of ethical products, such as fairtrade, questions these assumptions. A model that integrates both individual and societal values into the theory of planned behaviour is presented and empirically tested to challenge the assumption that ethical consumption is driven by ethical considerations alone. Using data sourced from fairtrade shoppers across the UK, structural equation modelling suggests that fairtrade purchase intention is driven by both societal and self-interest values. This dual value pathway helps address conceptual limitations inherent in the underlying assumptions of existing ethical purchasing behaviour m odels and helps advance understanding of consumers’ motivation to purchase ethical products

Multilaboratory Comparison of Pneumococcal Multiplex Immunoassays Used in lmmunosurveillance of Streptococcus pneumoniae across Europe

Surveillance studies are required to estimate the impact of pneumococcal vaccination in both children and the elderly across Europe. The World Health Organization (WHO) recommends use of enzyme immunoassays (EIAs) as standard methods for immune surveillance of pneumococcal antibodies. However, as levels of antibodies to multiple serotypes are monitored in thousands of samples, a need for a less laborious and more flexible method has evolved. Fluorescent-bead-based multiplex immunoassays (MIAs) are suitable for this purpose. An increasing number of public health and diagnostic laboratories use MIAs, although the method is not standardized and no international quality assessment scheme exists. The EU Pneumo Multiplex Assay Consortium was initiated in 2013 to advance harmonization of MIAs and to create an international quality assessment scheme. In a multilaboratory comparison organized by the consortium, agreement among nine laboratories that used their own optimized MIA was assessed on a panel of 15 reference sera for 13 pneumococcal serotypes with the new WHO standard 007sp. Agreement was assessed in terms of assay accuracy, reproducibility, repeatability, precision, and bias. The results indicate that the evaluated MIAs are robust and reproducible for measurement of vaccine-induced antibody responses. However, some serotype-specific variability in the results was observed in comparisons of polysaccharides from different sources and of different conjugation methods, especially for serotype 4. On the basis of the results, the consortium has contributed to the harmonization of MIA protocols to improve reliability of immune surveillance of Streptococcus pneumoniae

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead