Создание коллекции МСКТ-изображений и клинических данных при острых нарушениях мозгового кровообращения

Background The use of neuroimaging methods is an integral part of the process of assisting patients with acute cerebrovascular events (ACVE), and computed tomography (CT) is the «gold standard» for examining this category of patients. The capabilities of the analysis of CT images may be significantly expanded with modern methods of machine learning including the application of the principles of radiomics. However, since the use of these methods requires large arrays of DICOM (Digital Imaging and Communications in Medicine)-images, their implementation into clinical practice is limited by the lack of representative sample sets. Inaddition, at present, collections (datasets) of CT images of stroke patients, that are suitable for machine learning, are practically not available in the public domain.Aim of study Regarding the aforesaid, the aim of this work was to create a DICOM images dataset of native CT and CT-angiography of patients with different types of stroke. Material and meth ods The collection was based on the medical cases of patients hospitalized in the Regional Vascular Center of the N.V. Sklifosovsky Research Institute for Emergency Medicine. We used a previously developed specialized platform to enter clinical data on the stroke cases, to attach CT DICOMimages to each case, to contour 3D areas of interest, and to tag (label) them. A dictionary was developed for tagging, where elements describe the type of lesion, location, and vascular territory.Results A dataset of clinical cases and images was formed in the course of the work. It included anonymous information about 220 patients, 130 of them with ischemic stroke, 40 with hemorrhagic stroke, and 50 patients without cerebrovascular disorders. Clinical data included information about type of stroke, presence of concomitant diseases and complications, length of hospital stay, methods of treatment, and outcome. The results of 370 studies of native CT and 102 studies of CT-angiography were entered for all patients. The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit.Актуальность Применение методов нейровизуализации является неотъемлемой частью процесса оказания помощи больным с острыми нарушениями мозгового кровообращения (ОНМК), при этом золотым стандартом обследования данной категории больных является компьютерная томография (КТ). Значительно расширить возможности анализа КТ-изображений возможно с помощью современных методов машинного обучения, в том числе на основе применения принципов радиомики. Однако, так как использование этих методов требует наличия больших массивов DICOM (Digital Imaging and Communications in Medicine)-изображений, их внедрение в клиническую практику ограничено проблемой набора репрезентативных выборок. Кроме того, в настоящее время в открытом доступе практически не представлены коллекции, содержащие КТ-изображения больных c ОНМК, которые были бы пригодны для машинного обучения.Цель В связи с вышесказанным, целью данной работы являлось создание коллекции DICOM-изображений нативной КТ и КТ-ангиографии у пациентов с различными типами ОНМК.Материал и методы Основой для создания коллекции стали истории болезни пациентов, госпитализированных в региональный сосудистый центр НИИ СП им. Н.В. Склифосовского. Для формирования коллекции использовалась разработанная нами ранее специализированная платформа, позволяющая вводить клинические данные о случаях ОНМК, прикреплять к каждому случаю DICOM-изображения проведенных исследований, а также оконтуривать и тегировать (размечать) 3D-области интереса. Для тегирования был разработан словарь, элементы которого описывают тип патологического образования, локализацию и бассейн кровоснабжения.Результаты В ходе работы была сформирована коллекция клинических случаев и изображений, включающая анонимизированную информацию о 220 пациентах, из них 130 - с ишемическим инсультом, 40 - с геморрагическим инсультом, а также 50 человек без цереброваскулярной патологии. Клинические данные включали сведения о типе ОНМК, наличии сопутствующих заболеваний и осложнений, длительности госпитализации, способе лечения и исходе. Всего для пациентов были введены результаты 370 исследований нативной КТ и 102 исследования КТ-ангиографии. На каждой серии изображений врачом-экспертом были оконтурены и протегированы области интереса, соответствующие прямым и косвенным признакам ОНМК.Вывод Сформированная коллекция изображений позволит в последующем применить различные методы анализа данных и машинного обучения в решении важнейших практических задач, в том числе диагностики типа ОНМК, оценки объема поражения, прогноза степени неврологического дефицита

List of lists-annotated (LOLA): A database for annotation and comparison of published microarray gene lists

Microarray profiling of RNA expression is a powerful tool that generates large lists of transcripts that are potentially relevant to a disease or treatment. However, because the lists of changed transcripts are embedded in figures and tables, they are typically inaccessible for search engines. Due to differences in gene nomenclatures, the lists are difficult to compare between studies. LOLA (Lists of Lists Annotated) is an internet-based database for comparing gene lists from microarray studies or other genomic-scale methods. It serves as a common platform to compare and reannotate heterogeneous gene lists from different microarray platforms or different genomic methodologies such as serial analysis of gene expression (SAGE) or proteomics. LOLA (www.lola.gwu.edu) provides researchers with a means to store, annotate, and compare gene lists produced from different studies or different analyses of the same study. It is especially useful in identifying potentially high interest genes which are reported as significant across multiple studies and species. Its application to the fields of stem cell, cancer, and aging research is demonstrated by comparing published papers. © 2005 Elsevier B.V. All rights reserved

Creation of a Dataset of MSCT-Images and Clinical Data for Acute Cerebrovascular Events

Background The use of neuroimaging methods is an integral part of the process of assisting patients with acute cerebrovascular events (ACVE), and computed tomography (CT) is the «gold standard» for examining this category of patients. The capabilities of the analysis of CT images may be significantly expanded with modern methods of machine learning including the application of the principles of radiomics. However, since the use of these methods requires large arrays of DICOM (Digital Imaging and Communications in Medicine)-images, their implementation into clinical practice is limited by the lack of representative sample sets. Inaddition, at present, collections (datasets) of CT images of stroke patients, that are suitable for machine learning, are practically not available in the public domain.Aim of study Regarding the aforesaid, the aim of this work was to create a DICOM images dataset of native CT and CT-angiography of patients with different types of stroke. Material and meth ods The collection was based on the medical cases of patients hospitalized in the Regional Vascular Center of the N.V. Sklifosovsky Research Institute for Emergency Medicine. We used a previously developed specialized platform to enter clinical data on the stroke cases, to attach CT DICOMimages to each case, to contour 3D areas of interest, and to tag (label) them. A dictionary was developed for tagging, where elements describe the type of lesion, location, and vascular territory.Results A dataset of clinical cases and images was formed in the course of the work. It included anonymous information about 220 patients, 130 of them with ischemic stroke, 40 with hemorrhagic stroke, and 50 patients without cerebrovascular disorders. Clinical data included information about type of stroke, presence of concomitant diseases and complications, length of hospital stay, methods of treatment, and outcome. The results of 370 studies of native CT and 102 studies of CT-angiography were entered for all patients. The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit

Case-study of a user-driven prosthetic arm design: bionic hand versus customized body-powered technology in a highly demanding work environment

BACKGROUND: Prosthetic arm research predominantly focuses on "bionic" but not body-powered arms. However, any research orientation along user needs requires sufficiently precise workplace specifications and sufficiently hard testing. Forensic medicine is a demanding environment, also physically, also for non-disabled people, on several dimensions (e.g., distances, weights, size, temperature, time). METHODS: As unilateral below elbow amputee user, the first author is in a unique position to provide direct comparison of a "bionic" myoelectric iLimb Revolution (Touch Bionics) and a customized body-powered arm which contains a number of new developments initiated or developed by the user: (1) quick lock steel wrist unit; (2) cable mount modification; (3) cast shape modeled shoulder anchor; (4) suspension with a soft double layer liner (Ohio Willowwood) and tube gauze (Molnlycke) combination. The iLimb is mounted on an epoxy socket; a lanyard fixed liner (Ohio Willowwood) contains magnetic electrodes (Liberating Technologies). An on the job usage of five years was supplemented with dedicated and focused intensive two-week use tests at work for both systems. RESULTS: The side-by-side comparison showed that the customized body-powered arm provides reliable, comfortable, effective, powerful as well as subtle service with minimal maintenance; most notably, grip reliability, grip force regulation, grip performance, center of balance, component wear down, sweat/temperature independence and skin state are good whereas the iLimb system exhibited a number of relevant serious constraints. CONCLUSIONS: Research and development of functional prostheses may want to focus on body-powered technology as it already performs on manually demanding and heavy jobs whereas eliminating myoelectric technology's constraints seems out of reach. Relevant testing could be developed to help expediting this. This is relevant as Swiss disability insurance specifically supports prostheses that enable actual work integration. Myoelectric and cosmetic arm improvement may benefit from a less forgiving focus on perfecting anthropomorphic appearance