Practices and challenges in clinical data sharing

The debate on data access and privacy is an ongoing one. It is kept alive by the never-ending changes/upgrades in (i) the shape of the data collected (in terms of size, diversity, sensitivity and quality), (ii) the laws governing data sharing, (iii) the amount of free public data available on individuals (social media, blogs, population-based databases, etc.), as well as (iv) the available privacy enhancing technologies. This paper identifies current directions, challenges and best practices in constructing a clinical data-sharing framework for research purposes. Specifically, we create a taxonomy for the framework, identify the design choices available within each taxon, and demonstrate thew choices using current legal frameworks. The purpose is to devise best practices for the implementation of an effective, safe and transparent research access framework

Sistemas interativos e distribuídos para telemedicina

doutoramento Ciências da ComputaçãoDurante as últimas décadas, as organizações de saúde têm vindo a adotar continuadamente as tecnologias de informação para melhorar o funcionamento dos seus serviços. Recentemente, em parte devido à crise financeira, algumas reformas no sector de saúde incentivaram o aparecimento de novas soluções de telemedicina para otimizar a utilização de recursos humanos e de equipamentos. Algumas tecnologias como a computação em nuvem, a computação móvel e os sistemas Web, têm sido importantes para o sucesso destas novas aplicações de telemedicina. As funcionalidades emergentes de computação distribuída facilitam a ligação de comunidades médicas, promovem serviços de telemedicina e a colaboração em tempo real. Também são evidentes algumas vantagens que os dispositivos móveis podem introduzir, tais como facilitar o trabalho remoto a qualquer hora e em qualquer lugar. Por outro lado, muitas funcionalidades que se tornaram comuns nas redes sociais, tais como a partilha de dados, a troca de mensagens, os fóruns de discussão e a videoconferência, têm o potencial para promover a colaboração no sector da saúde. Esta tese teve como objetivo principal investigar soluções computacionais mais ágeis que permitam promover a partilha de dados clínicos e facilitar a criação de fluxos de trabalho colaborativos em radiologia. Através da exploração das atuais tecnologias Web e de computação móvel, concebemos uma solução ubíqua para a visualização de imagens médicas e desenvolvemos um sistema colaborativo para a área de radiologia, baseado na tecnologia da computação em nuvem. Neste percurso, foram investigadas metodologias de mineração de texto, de representação semântica e de recuperação de informação baseada no conteúdo da imagem. Para garantir a privacidade dos pacientes e agilizar o processo de partilha de dados em ambientes colaborativos, propomos ainda uma metodologia que usa aprendizagem automática para anonimizar as imagens médicasDuring the last decades, healthcare organizations have been increasingly relying on information technologies to improve their services. At the same time, the optimization of resources, both professionals and equipment, have promoted the emergence of telemedicine solutions. Some technologies including cloud computing, mobile computing, web systems and distributed computing can be used to facilitate the creation of medical communities, and the promotion of telemedicine services and real-time collaboration. On the other hand, many features that have become commonplace in social networks, such as data sharing, message exchange, discussion forums, and a videoconference, have also the potential to foster collaboration in the health sector. The main objective of this research work was to investigate computational solutions that allow us to promote the sharing of clinical data and to facilitate the creation of collaborative workflows in radiology. By exploring computing and mobile computing technologies, we have designed a solution for medical imaging visualization, and developed a collaborative system for radiology, based on cloud computing technology. To extract more information from data, we investigated several methodologies such as text mining, semantic representation, content-based information retrieval. Finally, to ensure patient privacy and to streamline the data sharing in collaborative environments, we propose a machine learning methodology to anonymize medical images

Pan-cancer efficacy of pralsetinib in patients with RET fusion–positive solid tumors from the phase 1/2 ARROW trial

Pharmacodynamics; Prognostic markers; Target validationFarmacodinámica; Marcadores pronósticos; Validación de objetivosFarmacodinàmica; Marcadors pronòstics; Validació d'objectiusOncogenic RET fusions occur in diverse cancers. Pralsetinib is a potent, selective inhibitor of RET receptor tyrosine kinase. ARROW (NCT03037385, ongoing) was designed to evaluate pralsetinib efficacy and safety in patients with advanced RET-altered solid tumors. Twenty-nine patients with 12 different RET fusion–positive solid tumor types, excluding non-small-cell lung cancer and thyroid cancer, who had previously received or were not candidates for standard therapies, were enrolled. The most common RET fusion partners in 23 efficacy-evaluable patients were CCDC6 (26%), KIF5B (26%) and NCOA4 (13%). Overall response rate, the primary endpoint, was 57% (95% confidence interval, 35–77) among these patients. Responses were observed regardless of tumor type or RET fusion partner. Median duration of response, progression-free survival and overall survival were 12 months, 7 months and 14 months, respectively. The most common grade ≥3 treatment-related adverse events were neutropenia (31%) and anemia (14%). These data validate RET as a tissue-agnostic target with sensitivity to RET inhibition, indicating pralsetinib’s potential as a well-tolerated treatment option with rapid, robust and durable anti-tumor activity in patients with diverse RET fusion–positive solid tumors.The authors would like to thank the patients, their families and all investigators involved in this study. V.S. is an Andrew Sabin Family Foundation Fellow at The University of Texas MD Anderson Cancer Center. V.S. acknowledges support of the Jacquelyn A. Brady Fund. V.S. is supported by US National Institutes of Health grants R01CA242845 and R01CA273168. MD Anderson Cancer Center Department of Investigational Cancer Therapeutics is supported by the Cancer Prevention and Research Institute of Texas (RP1100584), the Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy (1U01 CA180964), a National Center for Advancing Translational Sciences grant (UL1 TR000371) and the MD Anderson Cancer Center Support grant (P30 CA016672). Medical writing support, including assisting authors with the development of the outline as well as initial draft and incorporation of comments, was provided by N. Tracey and W. Wheddon; editorial support, including submission, was provided by E. Sims and T. Taylor, all of Paragon (Knutsford, United Kingdom), supported by Blueprint Medicines, according to Good Publication Practice guidelines. The sponsor was involved in the study design and collection, analysis and interpretation of data, as well as data checking of information provided in the article. However, ultimate responsibility for opinions, conclusions and data interpretation lies with the authors. E.G. is supported by the Caixa Research Advanced Oncology Research Program (supported by Fundació La Caixa, LCF/PR/CE07/50610001). E.N. is supported by the Carlos III National Health Institute grant (PI21/00789) and Horizon 2020 (H2020-SC1-2019-Single-Stage-RTD). M. Schuler is supported by the Oncology Center of Excellence Grant/German Cancer Aid (70112273) and the German Cancer Consortium, partner site: University Hospital Essen (BMBF 613-71043-1). G.C. is supported by an OPTIMA (optimal treatment for patients with solid tumors in Europe through artificial intelligence) grant (101034347). The ARROW study (NCT03037385) was supported by Blueprint Medicines and F. Hoffmann-La Roche

its goals, rationale, data infrastructure, and current developments

Background With multifaceted imaging capabilities, cardiovascular magnetic resonance (CMR) is playing a progressively increasing role in the management of various cardiac conditions. A global registry that harmonizes data from international centers, with participation policies that aim to be open and inclusive of all CMR programs, can support future evidence-based growth in CMR. Methods The Global CMR Registry (GCMR) was established in 2013 under the auspices of the Society for Cardiovascular Magnetic Resonance (SCMR). The GCMR team has developed a web-based data infrastructure, data use policy and participation agreement, data-harmonizing methods, and site-training tools based on results from an international survey of CMR programs. Results At present, 17 CMR programs have established a legal agreement to participate in GCMR, amongst them 10 have contributed CMR data, totaling 62,456 studies. There is currently a predominance of CMR centers with more than 10 years of experience (65%), and the majority are located in the United States (63%). The most common clinical indications for CMR have included assessment of cardiomyopathy (21%), myocardial viability (16%), stress CMR perfusion for chest pain syndromes (16%), and evaluation of etiology of arrhythmias or planning of electrophysiological studies (15%) with assessment of cardiomyopathy representing the most rapidly growing indication in the past decade. Most CMR studies involved the use of gadolinium-based contrast media (95%). Conclusions We present the goals, mission and vision, infrastructure, preliminary results, and challenges of the GCMR. Trial registration Identification number on ClinicalTrials.gov: NCT02806193. Registered 17 June 2016

Federated Learning in Medical Imaging:Part II: Methods, Challenges, and Considerations

Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging due to the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them