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.
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
“Messaging in the Age of Microtargeting”
John Stafford
Assistant Vice President
Digital Strategy
Stanford University
Bjorn Carey
Senior Director
Digital Strategy
Stanford University
Join via Zoom: https://stanford.zoom.us/j/400566542
Abstract:
Communications has become increasingly data-driven, targeted, and personalized. This has changed how Stanford analyzes communications opportunities from a research perspective and how it engages with relevant audiences. In this presentation, John and Bjorn will share the data and communications strategy underlying three communications initiatives and the resulting execution. They will also provide practical advice for individual thought leadership and communications in this dynamic environment.
About:
John Stafford, MA ’06, is currently Assistant Vice President for Digital Strategy at Stanford, the most senior digital communications role in the university. John is responsible for all aspects of creating a world-class digital communications function: setting the group’s strategy, building analytics and insight programs, counseling on crisis communications, leading multi-channel messaging initiatives, and advising colleagues across the University. He received a Master’s Degree in Communication from Stanford, a B.A. in History from the University of San Francisco, and was a founding advisor to Stanford Medicine X.
Refreshments will be provided.
“Algorithm Development Lifecycle in Medical Imaging:
Current State and Considerations for the Future”
Luciano M. Prevedello, MD, MPH
Vice-Chair for Medical Informatics and Augmented Intelligence in Imaging
Division Chief, Medical Imaging Informatics
Director, 3D and Advanced Visualization Lab
Associate Professor, Division of Neuroradiology,
Department of Radiology
Ohio State University Wexner Medical Center
Join via Zoom: https://stanford.zoom.us/j/267814863
Abstract:
This presentation will describe some of the most important considerations involved in creating algorithms in medical imaging from inception to deployment as well as continued model improvement and/or monitoring. Examples of experience to date from the OSU laboratory for augmented intelligence in imaging will be provided. New paradigms in model creation and the role of image challenge competitions will also be covered. Current issues with model validation and generalizability will also be introduced as well as considerations for future work in this area.
Refreshments will be provided.
“A Deep Learning Framework for Efficient Registration of MRI and Histopathology Images of the Prostate”
Wei Shao, PhD
Postdoctoral Research Fellow
Department of Radiology
Stanford University
“Applications of Generative Adversarial Networks (GANs) in Medical Imaging”
Saeed Seyyedi, PhD
Paustenbach Research Fellow
Department of Radiology
Stanford University
Join via Zoom: https://stanford.zoom.us/j/593016899
Refreshments will be provided
ABSTRACT (Shao)
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, MRI interpretation suffers from high interobserver variability and often misses clinically significant cancers. Registration of histopathology images from patients who have undergone surgical resection of the prostate onto pre-operative MRI images allows direct mapping of cancer location onto MR images. This is essential for the discovery and validation of novel prostate cancer signatures on MRI. Traditional registration approaches can be computationally expensive and require a careful choice of registration hyperparameters. We present a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of preprocessing, transform estimation by deep neural networks, and postprocessing. We refined the registration neural networks, originally trained with 19,642 natural images, by adding 17,821 medical images of the prostate to the training set. The pipeline was evaluated using 99 prostate cancer patients. The addition of the images to the training set significantly (p < 0.001) improved the Dice coefficient and reduced the Hausdorff distance. Our pipeline also achieved comparable accuracy to an existing state-of-the-art algorithm while reducing the computation time from 4.4 minutes to less than 2 seconds.
ABSTRACT (Seyyedi)
Generative adversarial networks (GANs) are advanced types of neural networks where two networks are trained simultaneously to perform two tasks of generation and discrimination. GANs have gained a lot of attention to tackle well known and challenging problems in computer vision applications including medical image analysis tasks such as medical image de-noising, detection and classification, segmentation and reconstruction.In this talk, we will introduce some of the recent advancements of GANs in medical imaging applications and will discuss the recent developments of GAN models to resolve real world imaging challenges.
Thursday MIPS Roundtable: “Ask the Radiologist about COVID-19”
Hosted by Dr. Heike Daldrup-Link & Dr. Gunilla Jacobson
Join us virtually to ask anything you would like to know about COVID-19 and the current status in our community. This week we will open the floor for questions. Please prepare questions related to COVID-19. Feel free to submit questions in advance to Ashley Williams (ashleylw@stanford.edu) or submit them in the chat on Zoom.
Meeting URL: https://stanford.zoom.us/j/186504700
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 186 504 700c talking about the future of medicine and science.
Thursday MIPS Roundtable
Hosted by Dr. Kathy Ferrara & Dr. Gunilla Jacobson
Join us virtually to ask anything you would like to know about career plans, collaborations, new expertise and online lectures during the shutdown. Feel free to submit questions in advance to Ashley Williams (ashleylw@stanford.edu) or submit them in the chat on Zoom.
1:30-2:00 PM | Dr. Kathy Ferrara, Ph.D.
“Developing a career plan, collaborations and new expertise during the shutdown”
Professor of Radiology
Stanford University
2:00-2:30 PM | Dr. Angie Louie, Ph.D.
“How to create online lectures using Camtasia, Playposit and Canvas”
Professor of Biomedical Engineering
UC Davis
Meeting URL: https://stanford.zoom.us/j/186504700
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 186 504 700
Thursday MIPS Roundtable: ePad: A Web-based Imaging Informatics Platform for Image Annotation and Quantitative Analysis
Come learn about ePAD (https://epad.stanford.edu) from Dr. Rubin’s lab, a freely-accessible web-based informatics platform for universal access to radiology images, annotations, and quantitative analysis. We’ll also open the floor to a discussion on integrating ePAD into the SCi3 preclinical imaging workflow and how together we can crowdsource data sharing and reuse, and accelerate quantitative imaging research for everyone.
Daniel Rubin, PhD
Professor of Biomedical Data Science, of Radiology, of Medicine, and, by courtesy, Ophthalmology and of Computer Science
Jason Thanh Lee, PhD
Director of the Clark Center Facility
Laura Pisani, PhD
Associate Director of the Clark Center Facility
Frezghi Habet, PhD
Director of the Porter Drive Facility
MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
4/9 Meeting URL: https://stanford.zoom.us/j/114866791
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 114 866 791
Thursday MIPS Roundtable: Dr. Michelle James & Dr. Ted Graves
1:30-2:00 PM – Michelle L. James, Ph.D.
Neuroimmune Imaging Research and Discovery (NiRD) Lab
Assistant Professor of Radiology and of Neurology
Stanford University
2:00-2:30 PM – Edward “Ted” Graves, Ph.D.
Imaging Radiobiology Laboratory, Division of Radiation Oncology Medical Physics
“Using Imaging to Study Radiation Biology”
Associate Professor of Radiation Oncology (Radiation Physics) and, by courtesy, of Radiology
Stanford University
MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
4/16 Meeting URL: https://stanford.zoom.us/j/406957830
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 406 957 830
Radiomics and Radio-Genomics: Opportunities for Precision Medicine
Zoom: https://stanford.zoom.us/j/99904033216?pwd=U2tTdUp0YWtneTNUb1E4V2x0OTFMQT09
Pallavi Tiwari, PhD
Assistant Professor of Biomedical Engineering
Associate Member, Case Comprehensive Cancer Center
Director of Brain Image Computing Laboratory
School of Medicine | Case Western Reserve University
Abstract:
In this talk, Dr. Tiwari will focus on her lab’s recent efforts in developing radiomic (extracting computerized sub-visual features from radiologic imaging), radiogenomic (identifying radiologic features associated with molecular phenotypes), and radiopathomic (radiologic features associated with pathologic phenotypes) techniques to capture insights into the underlying tumor biology as observed on non-invasive routine imaging. She will focus on clinical applications of this work for predicting disease outcome, recurrence, progression and response to therapy specifically in the context of brain tumors. She will also discuss current efforts in developing new radiomic features for post-treatment evaluation and predicting response to chemo-radiation treatment. Dr. Tiwari will conclude with a discussion on her lab’s findings in AI + experts, in the context of a clinically challenging problem of post-treatment response assessment on routine MRI scans.