A.I.-Based Decision Support Systems for Precision & Participative Medicine:
Focus on Oncology and Radiomics
Join via Zoom: https://stanford.zoom.us/j/499245954
Abstract:
Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708. As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data-clinical, imaging, biologic, genetic, cost-to produce validated predictive models. DSSs compare the personalized probable outcomes-toxicity, tumor control, quality of life, cost effectiveness-of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders-clinicians, medical directors, medical insurers, patient advocacy groups-and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on oncology and radiomics.
About:
Philippe Lambin is a Clinician, Radiation Oncologist, with a PhD in Molecular Radiation Biology, “ERC advanced grant laureate”, co-inventor of Radiomics, multifactorial Decision Support Systems, Distributed learning, the use of immunocytokines with Radiation and pioneer in translational research with a focus on tumour hypoxia and immunotherapy. He is/has been leading 30 clinical trials. He has and is Professor at the University of Maastricht (head of the new Dpt of Precision Oncology: The D-Lab & The M-Lab, focusing on “Convergence Sciences” and Decision Support Systems. He is co-author of more than 488 peer reviewed scientific papers (Hirsch Index: 94 Google scholar), co-inventor of more than 17 patents (filed or submitted) of which 5 are in the (pre)commercialization phase and (co) promoter of more than 59 completed PhD’s.
Refreshments will be provided.
Alex K. Shalek, PhD
Pfizer-Laubach Career Development Associate Professor
Institute for Medical Engineering & Science
Department of Chemistry and Koch Institute
Massachusetts Institute of Technology
Abstract: While several methods exist for sampling tissues in clinical contexts, without high-fidelity tools for comprehensively profiling them, we are both limited in our capacity to understand how constituent cells and their interactions impact prognosis, and to select and develop precision therapeutics. Recent years have witnessed transformative and intersecting advances in nanofabrication and molecular biology that now enable deep profiling of low-input samples. Collectively, these afford new and exciting opportunities to study cellular heterogeneity, starting from the level of the single cell, and may unlock the diagnostic, prognostic, and discovery potential of clinical isolates. Illustratively, I will introduce how we can leverage single-cell genomic approaches – and, in particular, single-cell RNA-Seq – to explore the extensive functional diversity between cells, uncovering, from the “bottom-up,” distinct cell states and their molecular drivers. Moreover, I will discuss high-throughput experimental strategies and demonstrate, in the context of Acute Lymphoblastic Leukemia, how they can be leveraged to achieve the statistical power necessary to reconstruct intracellular circuits, enumerate and redefine cell states and types, and transform our understanding of cellular decision-making in health and disease on a genomic scale.
Bio: Alex K. Shalek is currently the Pfizer-Laubach Career Development Associate Professor at MIT, as well as a Core Member of the Institute for Medical Engineering and Science (IMES), an Associate Professor of Chemistry, and an Extramural Member of The Koch Institute for Integrative Cancer Research. He is also an Institute Member of the Broad Institute, an Associate Member of the Ragon Institute, an Assistant in Immunology at MGH, and an Instructor in Health Sciences and Technology at HMS. His research is directed towards the development and application of new technologies that facilitate understanding of how cells collectively perform systems-level functions in healthy and diseased states. Dr. Shalek received his bachelor’s degree summa cum laude from Columbia University and his Ph.D. from Harvard University in chemical physics under the guidance of Hongkun Park, and performed postdoctoral training under Hongkun Park and Aviv Regev (Broad/MIT). To date, his interdisciplinary research has focused on realizing and utilizing nanoscale manipulation and measurement technologies to examine how small components (molecules, cells) drive systems of vast complexity (cellular responses, population behaviors).
Integrative Biomedical Imaging Informatics at Stanford (IBIIS) and Center for Artificial Intelligence in Medicine & Imaging (AIMI) Seminar: “AI-Aided Diagnostic and Prognostic Tools for Prostate Cancer”
Okyaz Eminaga, MD, PhD
Postdoctoral Research Fellow, Urology
Biomedical Data Sciences
Stanford University
James H. Clark Center, S360
12:00pm-1:00pm – Seminar and Discussion (light refreshments provided)
Join via Zoom: https://stanford.zoom.us/j/613898274
ABSTRACT: Prostate Cancer exhibits different clinical behavior, ranging from indolent to lethal disease. A critical clinical need is identifying characteristics that distinguish indolent from advanced disease to direct treatment to the latter. The recent renaissance of artificial intelligence (AI) research uncovered the potential of AI to improve clinical decision making. In this seminar, we will go through the potential of AI to enhance the diagnosis and the prognosis of prostate cancer using magnetic resonance images, clinical data, and histology images. We will stress the challenges and benefits of having such AI-based solutions in clinical routine.
ABOUT: Dr. Eminaga passed his medical examination (Staatsexamen) 2009 and received his Ph.D. in Medicine 2010 from University of Muenster (major topic: medical informatics) under the supervision of Professor Dr. Axel Semjonow (one of the pioneer physician-scientists and biomarker researcher who worked on the standardization of PSA measurement for prostate cancer which is used nowadays) and Professor Dr. Martin Dugas (who is the head of European Research Center for Information Systems and one of the most influential professors in medical informatics in Europe). For those who don’t know the institute of medical informatics in Muenster. The systematized Nomenclature of Human and Veterinary Medicine (SNOMED), which is now used worldwide in medical information systems, was initiated by this institute more than 30 years ago.
His doctoral dissertation presented a novel documentation architecture for clinical data and imaging called cMDX (clinical map document) that facilitates the concept of the single-source information system for clinical data storage and analysis, and is successfully used in clinical routine for generating the pathology reports with graphical information about the spatial tumor extent for prostatectomy specimens since 2009 at the prostate center of University Hospital Muenster. This work has been also utilized for more than 20 studies related to genomics, translational medicine, epidemiology, urology, radiology, and pathology. Dr. Eminaga also established the biobanking information management system to manage the samples of one of the largest biobanks for prostate cancer in Europe. This biobank is also part of the European P-Mark network for prostate cancer-related biorepositories initiated by Oxford University.
Dr. Eminaga completed his residency in Urology in the University Hospital of Cologne (Germany) with a major focus on uro-oncology. He was also a research fellow in Prostate Center of University Hospital Muenster, doing research in biomarkers, biobanking infrastructure, epidemiology and histopathology. During his residency fellowship, he further evaluated the role of certain miRNA in prostate cancer development under the supervision of the molecular biologist Dr. Warnecke-Eberz. After his residency, he started a research scholarship at the laboratory of Dr. Brooks, doing genomic research and bioinformatics for research topics related to prostate cancer evolution. Now, his current interests have expanded to statistical learning, medical imaging informatics, and integrative data analysis.
He is the recipient of 3 highly-competitive scholarships and his works have been recognized at national and international levels e.g., by the European Association of Urology. Currently, he is an early-investigator research awardee for prostate cancer managed by the department of defense and works on developing decision-aided tools for diagnosis and prognosis of prostate cancer.
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http://ibiis.stanford.edu/events/seminars/2019seminars.html