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.
MIPS Seminar: “Tiny Bubbles, Big Impact: Exploring applications of nanobubbles in ultrasound molecular imaging and therapy”
Agata A. Exner, Ph.D.
Professor of Radiology and Biomedical Engineering
Department of Radiology
Case Western Reserve
Location: Beckman Center, B230
2:00pm – 3:00pm Seminar & Discussion
ABSTRACT
Sub-micron shell stabilized gas bubbles (aka nanobubbles (NB) or ultrafine bubbles) have gained momentum as a robust contrast agent for molecular imaging and therapy using ultrasound. The small size, extended stability and high concentration of nanobubbles make them an ideal tool for new applications of contrast enhanced ultrasound and ultra-
sound-mediated therapy, especially in oncology-related problems. Compared to microbub-bles, nanobubbles can provide superior tumor delineation, identify biomarkers on the vascu-lature and on tumors cells and facilitate drug and gene delivery into tumor tissue. The pat-terns of tissue enhancement under nonlinear ultrasound imaging of nanobubbles are distinct from conventional microbubbles especially in tissues exhibiting vascular hyperper-meability. Specifically, NB kinetics, quantified via time intensity curve analysis, typically show a marked delay in the washout rate and significantly increased area under the curve compared to larger bubbles. This effect is further enhanced by molecular targeting to cellular biomarkers, such as the prostate specific membrane antigen (PSMA) or the receptor protein tyrosine phosphatase, PTPmu. The unique contrast enhancement dynamics of nanobubbles are likely to be a result of direct bubble extravasation and prolonged retention of intact bubbles in target tissue. Thus, understanding the underlying mechanisms behind the unique nanobubble behavior can be the driver of significant future innovations in contrast enhanced ultrasound imaging applications. This presentation will discuss the fundamental challenges with nanobubble formulation and characterization and will showcase how the unique fea-tures of nanobubbles can be leveraged to improve disease detection and treatment using ultrasound.
MIPS Seminar
2:00-2:45 PM | Prof. Pawel Moskal
“Positronium Imaging with the J-PET Scanner”
Head of the Department of Experimental Particle Physics and Applications
Marian Smoluchowski Institute of Physics
Jagiellonian University, 30-348 Krakow, Poland
2:45-3:30 PM | Prof. Ewa Stepien
“Preclinical studies of positronium and extracellular vesicles biomarkers”
Head of the Department of Medical Physics
Marian Smoluchowski Institute of Physics
Jagiellonian University, 30-348 Krakow, Poland
ABSTRACT
As modern medicine develops towards personalized treatment of patients, there is a need for highly specific and sensitive tests to diagnose disease. Our research aims at improvement of specificity of positron emission tomography (PET) in assessment of cancer by use of positronium as a theranostic agent. During PET scanning about 40% of positron annihilations occur through the creation of positronium. “Positronium,” which may be formed in human tissues in the intramolecular spaces, is an exotic atom composed of an electron from tissue and the positron emitted by the radioinuclide. Positronium decay in the patient body is sensitive to the nanostructure and metabolism of human tissues. This phenomenon is not used in present PET diagnostics, yet it is in principle possible to exploit such environment modified properties of positronium as diagnostic biomarkers for cancer assessment. Our first in-vitro studies have shown differences of the positronium mean lifetime and production probability in healthy and cancerous tissues, indicating that they may be used as indicators for in-vivo cancer classification. For the application in medical diagnostics, the properties of positronium atoms need to be determined in a spatially resolved manner. For that purpose we have developed a method of positronium lifetime imaging in which the lifetime and position of positronium atoms are determined on an event-by-event basis. This method requires application of β+ decaying isotope that also emits a prompt gamma ray. We will argue that with total-body PET scanners, the sensitivity of positronium lifetime imaging, which requires coincident registration of the back-to-back annihilation photons and the prompt gamma, is comparable to the sensitivities for metabolic imaging with standard PET scanners.
Our research involves also development of diagnostic methods based on the extracellular vesicles (EVs), which are micro and nano-sized, closed membrane fragments. They are produced by native cells to facilitate the transfer of different signaling factors, structural proteins, nucleic acids or lipids even to distant cells. They are present in all body fluids and they are specific to their parental cells.
Our presentation will be divided into two parts. In the first, the method of positronium imaging and the pilot positronium images obtained with the J-PET detector (the first PET system built based on plastic scintillators) will be reported. This part of the presentation will include also description and perspectives of development of the J-PET technology in view of total-body PET imaging. The second part will concern preliminary results of the preclinical studies of positronium properties in cancerous and healthy tissues sampled from patients as well as in the frozen and living healthy and cancer skin cells in-vitro. The second part will include also description of the novel method for the diagnosis of diabetes and melanoma based on EVs used as biomarkers and drug delivery systems.
References:
P. Moskal, …. E. Ł. Stępień et al., Phys. Med. Biol. 64 (2019) 055017
- Moskal, B. Jasinska, E. Ł. Stępień, S. Bass, Nature Reviews Physics 1 (2019) 527
- Roman M… .E. Ł. Stępień, Nanomedicine 17 (2019) 137
- Ł. Stępień et al., Theranostics 8 (2018) 3874
Hosted by: Craig Levin, Ph.D.
Sponsored by the Molecular Imaging Program at Stanford and the Department of Radiology
Please note this seminar is now cancelled and will be rescheduled for a later date.
MIPS Seminar: Investigating and Imaging key molecular switches associated with Acquirement of Platinum-Taxol resistance in Epithelial Ovarian Cancer
Pritha Ray, Ph.D.
Principal Investigator & Scientific Officer F
Imaging Cell Signaling & Therapeutics Lab
ACTREC, Tata Memorial Center
Navi Mumbai, India
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.
Location & Timing
August 5, 2020
8:30am-4:30pm
Livestream: details to come
This event is free and open to all!
Registration and Event details
Overview
Advancements of machine learning and artificial intelligence into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. Sponsored by the Stanford Center for Artificial Intelligence in Medicine and Imaging, the 2020 AIMI Symposium is a virtual conference convening experts from Stanford and beyond to advance the field of AI in medicine and imaging. This conference will cover everything from a survey of the latest machine learning approaches, many use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing building and evaluating machine learning in healthcare applications.
Our goal is to make the best science accessible to a broad audience of academic, clinical, and industry attendees. Through the AIMI Symposium we hope to address gaps and barriers in the field and catalyze more evidence-based solutions to improve health for all.
Judy Gichoya, MD
Assistant Professor
Emory University School of Medicine
Measuring Learning Gains in Man-Machine Assemblage When Augmenting Radiology Work with Artificial Intelligence
Abstract
The work setting of the future presents an opportunity for human-technology partnerships, where a harmonious connection between human-technology produces unprecedented productivity gains. A conundrum at this human-technology frontier remains – will humans be augmented by technology or will technology be augmented by humans? We present our work on overcoming the conundrum of human and machine as separate entities and instead, treats them as an assemblage. As groundwork for the harmonious human-technology connection, this assemblage needs to learn to fit synergistically. This learning is called assemblage learning and it will be important for Artificial Intelligence (AI) applications in health care, where diagnostic and treatment decisions augmented by AI will have a direct and significant impact on patient care and outcomes. We describe how learning can be shared between assemblages, such that collective swarms of connected assemblages can be created. Our work is to demonstrate a symbiotic learning assemblage, such that envisioned productivity gains from AI can be achieved without loss of human jobs.
Specifically, we are evaluating the following research questions: Q1: How to develop assemblages, such that human-technology partnerships produce a “good fit” for visually based cognition-oriented tasks in radiology? Q2: What level of training should pre-exist in the individual human (radiologist) and independent machine learning model for human-technology partnerships to thrive? Q3: Which aspects and to what extent does an assemblage learning approach lead to reduced errors, improved accuracy, faster turn-around times, reduced fatigue, improved self-efficacy, and resilience?
Zoom: https://stanford.zoom.us/j/93580829522?pwd=ZVAxTCtEdkEzMWxjSEQwdlp0eThlUT09