Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of treatment toxicity. Along with the availability of patient repositories, this situation has lead to clinician interest in understanding and predicting RT outcomes based on previously treated similar patients. To enable this type of analysis, we introduce a novel topology-based spatial similarity measure, T-SSIM, and a predictive algorithm based on this similarity measure. We couple the algorithm with a visual steering interface that intertwines visual encodings for the spatial data and statistical results, including a novel parallel-marker encoding that is spatially aware. We report quantitative results on a cohort of 165 patients, as well as a qualitative evaluation with domain experts in radiation oncology, data management, biostatistics, and medical imaging, who are collaborating remotely.Comment: IEEE VIS (SciVis) 201

Database Methods for Copy Number Variant Analysis of One Hundred Disease Associated Genes in Human Congenital Heart Disease

Human genetic variation occurs more commonly than was recognized after the completion of the Human Genome Sequencing Project in 2003. Submicroscopic human DNA analysis has revealed copy number variation (CNV) as the deletion or duplication of a genomic region potentially affecting gene dosage. Advanced genetic research now includes the study of CNVs in diseased subject groups compared to in house controls or online published datasets of control CNV data. Research labs choose from different bioinformatic algorithms to make the copy number calls. Solutions for further processing the copy number data into quantifiable form require collaboration with data analysts and include the use of relational databases. The aim of this thesis work was to develop a relational database solution for human copy number variation in subjects with cardiac malformations. The multipurpose database served as a central repository for the cohort demographic data as well as the entire experimental set of copy number variant data. Quantification and frequency analyses of the CNVs were executed via SQL queries. Database SQL queries generated raw data used for essential visualization tools including a detailed subject profile and a one hundred gene CNV spectra. The stated purpose of the study was to develop a descriptive analysis of genomic copy number associations in a well phenotyped congenital heart disease (CHD) population over one hundred disease associated genes. The relational database created to advance the research proved valuable in its data storage and retrieval capacity. Results showing consistency with published literature validated the accuracy of the query results generated for the CHD cohort

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Structure Pharmaceutics Based on Synchrotron Radiation X-Ray Micro- Computed Tomography: From Characterization to Evaluation and Innovation of Pharmaceutical Structures

Drug delivery systems (DDS) are essentially pharmaceutical products for human therapy, typically involving a mixture of active ingredients and excipients. Based upon quantitative characterization of structure, the thesis introduces the concept of classifying the architecture of DDS into four levels by their spatial scale and the life time period. The primary level is recognised as the static structure of the whole dosage form with a size from μm to cm with the final structure generated by formulation design. The secondary level categorises the structures of particles or sub-units to form a DDS with sizes from nm to mm as key units in processing such as mixing, grinding, granulation and packing; The tertiary level represents the dynamic structures of DDS during the drug release phase in vitro or in vivo incorporating the structure size range from nm to mm, which undergo changes during dissolution, swelling, erosion or diffusion. The spatial scale for the quaternary level is defined as the meso or micro scale architecture of active and non-active molecules within a DDS with sizes from Å to μm for the molecular structure of drug and excipients. Methods combining X-ray tomography, image processing, and 3D reconstructions have been devised and evaluated to study systematically pharmaceutical structures and correlate them with drug release kinetics of DDS. Based on the quantitative structural information of pharmaceutical intermediates and dosage forms, it is possible now to correlate structures with production processing, behaviour and function, and the static and dynamic structures of DDS with the release kinetics. Thus, a structure-guided methodology has been established for the research of DDS.Chinese Academy of Science

Recuperação de informação multimodal em repositórios de imagem médica

The proliferation of digital medical imaging modalities in hospitals and other diagnostic facilities has created huge repositories of valuable data, often not fully explored. Moreover, the past few years show a growing trend of data production. As such, studying new ways to index, process and retrieve medical images becomes an important subject to be addressed by the wider community of radiologists, scientists and engineers. Content-based image retrieval, which encompasses various methods, can exploit the visual information of a medical imaging archive, and is known to be beneficial to practitioners and researchers. However, the integration of the latest systems for medical image retrieval into clinical workflows is still rare, and their effectiveness still show room for improvement. This thesis proposes solutions and methods for multimodal information retrieval, in the context of medical imaging repositories. The major contributions are a search engine for medical imaging studies supporting multimodal queries in an extensible archive; a framework for automated labeling of medical images for content discovery; and an assessment and proposal of feature learning techniques for concept detection from medical images, exhibiting greater potential than feature extraction algorithms that were pertinently used in similar tasks. These contributions, each in their own dimension, seek to narrow the scientific and technical gap towards the development and adoption of novel multimodal medical image retrieval systems, to ultimately become part of the workflows of medical practitioners, teachers, and researchers in healthcare.A proliferação de modalidades de imagem médica digital, em hospitais, clínicas e outros centros de diagnóstico, levou à criação de enormes repositórios de dados, frequentemente não explorados na sua totalidade. Além disso, os últimos anos revelam, claramente, uma tendência para o crescimento da produção de dados. Portanto, torna-se importante estudar novas maneiras de indexar, processar e recuperar imagens médicas, por parte da comunidade alargada de radiologistas, cientistas e engenheiros. A recuperação de imagens baseada em conteúdo, que envolve uma grande variedade de métodos, permite a exploração da informação visual num arquivo de imagem médica, o que traz benefícios para os médicos e investigadores. Contudo, a integração destas soluções nos fluxos de trabalho é ainda rara e a eficácia dos mais recentes sistemas de recuperação de imagem médica pode ser melhorada. A presente tese propõe soluções e métodos para recuperação de informação multimodal, no contexto de repositórios de imagem médica. As contribuições principais são as seguintes: um motor de pesquisa para estudos de imagem médica com suporte a pesquisas multimodais num arquivo extensível; uma estrutura para a anotação automática de imagens; e uma avaliação e proposta de técnicas de representation learning para deteção automática de conceitos em imagens médicas, exibindo maior potencial do que as técnicas de extração de features visuais outrora pertinentes em tarefas semelhantes. Estas contribuições procuram reduzir as dificuldades técnicas e científicas para o desenvolvimento e adoção de sistemas modernos de recuperação de imagem médica multimodal, de modo a que estes façam finalmente parte das ferramentas típicas dos profissionais, professores e investigadores da área da saúde.Programa Doutoral em Informátic

Clustering cliques for graph-based summarization of the biomedical research literature

BACKGROUND: Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts). RESULTS: SemRep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm based on common arguments shared among cliques. The validity of the clusters in the summaries produced was compared to the Silhouette-generated baseline for cohesion, separation and overall validity. The theme labels were also compared to a reference standard produced with major MeSH headings. CONCLUSIONS: For 11 topics in the testing data set, the overall validity of clusters from the system summary was 10% better than the baseline (43% versus 33%). While compared to the reference standard from MeSH headings, the results for recall, precision and F-score were 0.64, 0.65, and 0.65 respectively

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams

Machine learning using radiomics and dosiomics for normal tissue complication probability modeling of radiation-induced xerostomia

In routine clinical practice, the risk of xerostomia is typically managed by limiting the mean radiation dose to parotid glands. This approach used to give satisfying results. In recent years, however, several studies have reported mean-dose models to fail in the recognition of xerostomia risk. This can be explained by a strong improvement of overall dose conformality in radiotherapy due to recent technological advances, and thereby a substantial reduction of the mean dose to parotid glands. This thesis investigated novel approaches to building reliable normal tissue complication probability (NTCP) models of xerostomia in this context. For the purpose of the study, a cohort of 153 head-and-neck cancer patients treated with radiotherapy at Heidelberg University Hospital was retrospectively collected. The predictive performance of the mean-dose to parotid glands was evaluated with the Lyman-Kutcher-Burman (LKB) model. In order to examine the individual predictive power of predictors describing parotid shape (radiomics), dose shape (dosiomics), and demographic characteristics, a total of 61 different features was defined and extracted from the DICOM files. These included the patient’s age and sex, parotid shape features, features related to the dose-volume histogram, the mean dose to subvolumes of parotid glands, spatial dose gradients, and three-dimensional dose moments. In the multivariate analysis, a variety of machine learning algorithms was evaluated: 1) classification methods, that discriminated patients between a high and a low risk of complication, 2) feature selection techniques, that aimed to select a number of highly informative covariates from a large set of predictors, 3) sampling methods, that reduced the class imbalance, 4) data cleaning methods, that reduced noise in the data set. The predictive performance of the models was validated internally, using nested cross-validation, and externally, using an independent patient cohort from the PARSPORT clinical trial. The LKB model showed fairly good performance on mild-to-severe (G1+) xerostomia predictions. The corresponding dose-response curve revealed that even small doses to parotid glands increase the risk of xerostomia and should be kept as low as possible. For the patients who did develop moderate-to-severe (G2+) xerostomia, the mean dose was not an informative predictor, even though the efficient sparing of parotid glands allowed to achieve low G2+ xerostomia rates. The features describing the shape of a parotid gland and the shape of a dose proved to be highly predictive of xerostomia. In particular, the parotid volume and the spatial dose gradients in the transverse plane explained xerostomia well. The results of the machine learning algorithms comparison showed that a particular choice of a classifier and a feature selection method can significantly influence predictive performance of the NTCP model. In general, support vector machines and extra-trees achieved top performance, especially for the endpoints with a large number of observations. For the endpoints with a smaller number of observations, simple logistic regression often performed on a par with the top-ranking machine learning algorithms. The external validation showed that the analyzed multivariate models did not generalize well to the PARSPORT cohort. The only features that were predictive of xerostomia both in the Heidelberg (HD) and the PARSPORT cohort were the spatial dose gradients in the right-left and the anterior-posterior directions. Substantial differences in the distribution of covariates between the two cohorts were observed, which may be one of the reasons for the weak generalizability of the HD models. The results presented in this thesis undermine the applicability of NTCP models of xerostomia based only on the mean dose to parotid glands in highly conformal radiotherapy treatments. The spatial dose gradients in the left-right and the anterior-posterior directions proved to be predictive of xerostomia both in the HD and the PARSPORT cohort. This finding is especially important as it is not limited to a single cohort but describes a general pattern present in two independent data sets. The performance of the sophisticated machine learning methods may indicate a need for larger patient cohorts in studies on NTCP models in order to fully benefit from their advantages. Last but not least, the observed covariate-shift between the HD and the PARSPORT cohort motivates, in the author’s opinion, a need for reporting information about the covariate distribution when publishing novel NTCP models