Calendar

CEDSS: “The Origins and Detection of Lethal Prostate Cancer”

Paul Boutros, Ph.D., M.B.A.
Director, Cancer Data Sciences
UCLA

Please see zoom details below:
Meeting URL: https://stanford.zoom.us/s/93515779500
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 935 1577 9500
Meeting Passcode: 767148

ABOUT
Boutros earned his B.Sc. degree from the University of Waterloo in Chemistry in 2004, and his Ph.D. degree from the University of Toronto, Canada, in Medical Biophysics in 2008. At Toronto, he also earned an executive M.B.A. from the Rothman School of Management. In 2008, Boutros started his independent research career at the Ontario Institute for Cancer Research first as a fellow (2008–2010) and then as principal investigator (2010–2018). He moved to California to join the UCLA faculty in 2018.

Hosted by: Utkan Demirci, Ph.D.
Sponsored by the Canary Center & the Department of Radiology 
Stanford University – School of Medicine

In honor of the 30th anniversary of the Americans with Disabilities Act and October as National Disability Employment Awareness Month, join the Stanford Medicine Abilities Coalition (SMAC) for a first of its kind StanfordMed LIVE event focused on disability. Now more than ever during the COVID-19 pandemic, disabilities, health conditions, and illness impact not only our patients but also all of us, both personally and as members of the Stanford Medicine community. Stanford Medicine leadership will share information, answer questions, and engage in a roundtable discussion about the state of disability at Stanford and how best to support faculty, staff, and students living with disability and chronic illness. We encourage our community to submit questions and comments here to be shared broadly with the Stanford Medicine community. The same link can be used to request any accommodations needed for the livestream. Additional information for the webcast itself will be sent out closer to the event.

Livestream link: https://livestream.com/accounts/1973198/events/9288854

PHIND Seminar Series: Serum Modulation of Mitochondrial Function as a Scalable Sensor of Insulin Resistance

Andrew Lipchik, Ph.D.
Postdoctoral Fellow – Michael Snyder Lab
Stanford University

11:00am – 12:00pm Seminar & Discussion
12:00pm – 12:15pm Reception & Light Refreshments
RSVP: https://stanford.zoom.us/webinar/register/7716009863360/WN_dbeuo7csS8q_AhR88XET0g

Location: Zoom
Webinar URL: . https://stanford.zoom.us/s/96358568342
Webinar ID: 963 5856 8342
Dial: +1 650 724 9799  or +1 833 302 1536 (Toll Free)
Password: 767148

ABSTRACT
The global epidemic of obesity is associated with the dramatic increase in the prevalence of type 2 diabetes mellitus (T2D) with an estimated 400 million people worldwide will have T2D by 2030.  T2D is proceeded by insulin resistance (IR) for up to decades prior to onset of T2D. Current estimates suggest approximately one in three individuals are sufficiently insulin resistant to be at risk for IR complications including T2D, coronary heart disease and nonalcoholic fatty liver disease. IR often goes undiagnosed due to the complex, invasive and laborious nature of clamp assays preventing their universal application in the clinic. Surrogate measurements using fasting plasma glucose and insulin levels can estimate IR but are imprecise. There is a need for the identification of new biomarkers and assays for the detection and monitoring of IR. Here, we demonstrate the utility of cellular mitochondrial respiration in response to individuals’ serum as a sensor for personalized monitoring of insulin sensitivity. The modulation of insulin-dependent mitochondrial function by patient serum was highly correlated with insulin sensitivity as determined by the gold-standard modified insulin suppression test (IST). We further applied this methodology to monitor insulin sensitivity over time in response to illness as well as treatment with the insulin sensitizing medication, pioglitazone. Our results demonstrate the development and application of a novel surrogate measurement for the determination and monitoring of insulin sensitivity. This assay offers the advantages of minimal invasiveness and complexity compared to IST as well as superior correlation with IST compared to existing surrogate measurements.

ABOUT ANDREW LIPCHIK
Andrew Lipchik majored in Chemistry at Xavier University where he preformed research on the development of oxygen activation Ni(II) complexes with Dr. Craig Davis and Dr. Michael Baldwin at the University of Cincinnati. He went on to obtain his PhD from Purdue University under mentorship of Dr. Laurie Parker. His thesis work focused on identifying determinants of kinase substrate specificity. This understanding was applied to the development of novel kinase-specific peptide biosensors to monitor intracellular kinase activity. Following his graduate work, he joined the laboratory of Michael Snyder at Stanford University where he has focused on understanding the impact of the immune system on insulin resistance and glucose metabolism.

Hosted by: Garry Gold, M.D.
Sponsored by the PHIND Center and the Department of Radiology

ZOOM LINK HERE

“High Resolution Breast Diffusion Weighted Imaging”
Jessica McKay, PhD

ABSTRACT: Diffusion-weighted imaging (DWI) is a quantitative MRI method that measures the apparent diffusion coefficient (ADC) of water molecules, which reflects cell density and serves as an indication of malignancy. Unfortunately, however, the clinical value of DWI is severely limited by the undesirable features in images that common clinical methods produce, including large geometric distortions, ghosting and chemical shift artifacts, and insufficient spatial resolution. Thus, in order to exploit information encoded in diffusion characteristics and fully assess the clinical value of ADC measurements, it is first imperative to achieve technical advancements of DWI.

In this talk, I will largely focus on the background of breast DWI, providing the clinical motivation for this work and explaining the current standard in breast DWI and alternatives proposed throughout the literature. I will also present my PhD dissertation work in which a novel strategy for high resolution breast DWI was developed. The purpose of this work is to improve DWI methods for breast imaging at 3 Tesla to robustly provide diffusion-weighted images and ADC maps with anatomical quality and resolution. This project has two major parts: Nyquist ghost correction and the use of simultaneous multislice imaging (SMS) to achieve high resolution. Exploratory work was completed to characterize the Nyquist ghost in breast DWI, showing that, although the ghost is mostly linear, the three-line navigator is unreliable, especially in the presence of fat. A novel referenceless ghost correction, Ghost/Object minimization was developed that reduced the ghost in standard SE-EPI and advanced SMS. An advanced SMS method with axial reformatting (AR) is presented for high resolution breast DWI. In a reader study, AR-SMS was preferred by three breast radiologists compared to the standard SE-EPI and readout-segmented-EPI.

“Machine-learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study”
Michael Zhang, MD

ABSTRACT: Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) is a diagnostic challenge with important management implications. We sought to develop a radiomics classifier based on 900 features extracted from gadolinium-enhanced, T1-weighted MRI, using the Quantitative Imaging Feature Pipeline and the PyRadiomics package. Additional patient-specific clinical variables were recorded. A radiomic signature was derived from least absolute shrinkage and selection operator, followed by gradient boost machine learning. A training and test set were selected randomly in a 70:30 ratio. We further evaluated the performance of radiomics-based classifier models against human readers of varying medical-training backgrounds. Following image pre-processing, 95 malignant and 171 benign PNSTs were available. The final classifier included 21 features and achieved a sensitivity 0.676, specificity 0.882, and area under the curve (AUC) 0.845. Collectively, human readers achieved sensitivity 0.684, specificity 0.742, and AUC 0.704. We concluded that radiomics using routine gadolinium enhanced, T1-weighted MRI sequences and clinical features can aid in the evaluation of PNSTs, particularly by increasing specificity for diagnosing malignancy. Further improvement may be achieved with incorporation of additional imaging sequences.

Rachael Callcut, MD, MSPH
Associate Professor in Residence, Surgery
Division of Trauma and Acute Care Surgery
Chief Research Informatics Officer
Vice Chair of Clinical Sciences, Surgery
University of California, Davis

Challenges and Promises for Healthcare AI

Abstract: Dr. Callcut will review the current state of AI in Healthcare. She will also explore the hope for the future, the ethical and regulatory challenges, and the unknowns in bringing AI to Healthcare. She will provide examples of the state of the art and also discuss future compute options.

Bio: Rachael A. Callcut, MD, MSPH is the Chief Research Informatics Officer of the UC Davis School of Medicine/Health System and the Vice Chair of Clinical Sciences in the Department of Surgery. Dr. Callcut also serves through a joint appointment as the Director of Data Science for the Center for Digital Health Innovation based at UCSF.  Dr. Callcut is a double board certified surgeon specializing in general surgery, trauma, and surgical critical care. She also directs a NIH and DOD funded research lab.  Dr. Callcut is an internationally renowned expert in Artificial Intelligence and Health Services Research.

Zoom: https://stanford.zoom.us/j/95969844545?pwd=YUJCYzdEeHVnRWlQOG4wRmtZeW51QT09

PHIND Seminar Series: Identifying Microbiome Markers of Progression of Alzheimer’s Disease

Ami Bhatt, M.D., Ph.D.
Assistant Professor of Medicine (Hematology) and of Genetics
Stanford University

Gavin Sherlock, Ph.D.
Associate Professor of Genetics
Stanford University

11:00am – 12:00pm Seminar & Discussion
RSVP: https://stanford.zoom.us/webinar/register/8016040837299/WN_iBOM7R4XQjOPSb20rkUxbw

Location: Zoom Webinar
Webinar URL: https://stanford.zoom.us/s/99730716280
Webinar ID: 997 3071 6280
Dial: +1 650 724 9799  or +1 833 302 1536 (Toll Free)
Password: 767148

ABOUT AMI BHATT
In perpetual awe of how ‘simple’ microbial organisms can perturb complex, multicellular eukaryotic organisms, Ami Bhatt has chosen to dedicate her research program to inspecting, characterizing and dissecting the microbe-human interface. Nowhere is the interaction between hosts and microbes more potentially impactful than in immunocompromised hosts and global settings where infectious and environmental exposures result in drastic and sometimes fatal health consequences.

Ami’s group identifies problems and questions that arise in the course of routine clinical care. Often in collaboration with investigators at Stanford and beyond, the group applies modern genetic, molecular and computational techniques to seek answers to these questions, better understand host-microbe interactions and decipher how perturbation of these interactions may result in human disease phenotypes.

GAVIN SHERLOCK’S RESEARCH INTERESTS
Adaptive Evolution and the Fitness Landscape: When yeast are evolved under various selective pressures in a chemostat, mutations that arise and provide an adaptive advantage will expand within the population. We have pioneered the use of high throughput sequencing to determine the identity of such mutations, as well as to understand the dynamics of the mutations within the populations, and the interactions between the mutations (such as epistasis). Further, we have developed a DNA barcode based lineage tracking system to determine the distribution of fitness effects (DFE) for newly arising beneficial mutations. We have also characterized what we call the genotype-fitness map for beneficial mutations, and have investigated why beneficial mutations provide a positive fitness effect. We are also interested in how beneficial mutations trade-off for different traits, and how those trade-offs constrain adaptive evolution.

Hosted by: Garry Gold, M.D.
Sponsored by the PHIND Center and the Department of Radiology

Ge Wang, PhD
Clark & Crossan Endowed Chair Professor
Director of the Biomedical Imaging Center
Rensselaer Polytechnic Institute
Troy, New York

Abstract:
AI-based tomography is an important application and a new frontier of machine learning. AI, especially deep learning, has been widely used in computer vision and image analysis, which deal with existing images, improve them, and produce features. Since 2016, deep learning techniques are actively researched for tomography in the context of medicine. Tomographic reconstruction produces images of multi-dimensional structures from externally measured “encoded” data in the form of various transforms (integrals, harmonics, and so on). In this presentation, we provide a general background, highlight representative results, and discuss key issues that need to be addressed in this emerging field.

About:
AI-based X-ray Imaging System (AXIS) lab is led by Dr. Ge Wang, affiliated with the Department of Biomedical Engineering at Rensselaer Polytechnic Institute and the Center for Biotechnology and Interdisciplinary Studies in the Biomedical Imaging Center. AXIS lab focuses on innovation and translation of x-ray computed tomography, optical molecular tomography, multi-scale and multi-modality imaging, and AI/machine learning for image reconstruction and analysis, and has been continuously well funded by federal agencies and leading companies. AXIS group collaborates with Stanford, Harvard, Cornell, MSK, UTSW, Yale, GE, Hologic, and others, to develop theories, methods, software, systems, applications, and workflows.

PHIND Seminar Series: Topics Below

Ahmed Metwally, PhD
“Pre-symptomatic detection of COVID-19 via wearables biosensors”
Postdoctoral Scholar – Michael Snyder, PhD Lab
Department of Genetics
Stanford University

Pierre-Alexandre Fournier, MS
“Continuous remote cardiorespiratory and health monitoring using the Hexoskin biometric shirt”
Co-founder and CEO
Hexoskin

Location: Zoom Webinar
Webinar URL: https://stanford.zoom.us/s/98925964231
Dial: +1 650 724 9799 or +1 833 302 1536 (Toll Free)
Webinar ID: 989 2596 4231
Passcode: 298382

11:00am – 12:00pm Seminar & Discussion
RSVP: https://stanford.zoom.us/webinar/register/WN_bruT-pvvQUePuBLqm2SLkQ

Ahmed Metwally Abstract
Wearable devices digitally measuring vital signs have been used for monitoring health and illness onset and have a high potential for real-time monitoring and disease detection. As such, they are potentially useful during public health crises, such as the current COVID-19 global pandemic. In my talk, I’ll discuss how wearables biosensors can be used as a tool to early detect COVID19 onset using physiological and activity data. By using retrospective smartwatch data, we showed that 63% of the COVID-19 cases could be detected before symptom onset in real-time via the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that consumer wearables may be used for the large-scale real-time detection of respiratory infections, often pre-symptomatically, and provide an approach for managing epidemics using digital tracking and health monitoring.

About Ahmed Metwally
Ahmed Metwally is a postdoctoral scholar in the Snyder lab at Stanford University. Ahmed received his Ph.D. in Bioinformatics/Bioengineering and MS in Computer Science (focused on Deep Learning), both from the University of Illinois at Chicago (UIC) in 2018. He currently works on developing novel machine learning methods for longitudinal multimodal biomedical data fusion (omics and wearable biosensors data) to early detect cardiometabolic diseases and personalize their treatments. Ahmed has received numerous awards, such as NIH Predoctoral Translational Scientist fellowship, ISMB’20 best talk award, Stanford COVID-19 RISE Grant, second-place award at Stanford Health++ Hackathon, and many travel awards NSF, IEEE, ISCB, and UIUC for various educational and scholarly activities.

Pierre-Alexandre Fournier Abstract
The Hexoskin Connected Health platform will be discussed as an example of a biometric shirt validated for use in telehealth and clinical research.  Hexoskin has the only clinically validated biometric garment which provides continuous monitoring of numerous and unique physiological parameters.  Hexoskin is an enabling technology for telehealth use cases, such as remote patient monitoring, rehab, and detect the onset of illness.  Hexoskin offers a unique set of high-resolution biometric data that can continuously monitor activity, sleep, cardiac and respiratory data.  Projects in fields such as cardiology, respiratory, behavioral and physiological psychology, biofeedback research, sleep research, and health will be described.  The Hexoskin Connected Health Platform provides researchers with accessible solutions such as the Hexoskin Dashboards, Open API, and Apps to manage, visualize, annotate, analyze, and export raw & processed health data. Data extraction tools allow access to the raw data with time series for machine learning and artificial intelligence projects.

About Pierre-Alexandre Fournier
Pierre-Alexandre Fournier is co-founder and CEO of Hexoskin, a Montreal-based company focused on clinical-grade wearable sensors and AI software for health and clinical research. Hexoskin was founded in 2006 and in 2013 released the first iPhone compatible smart clothing for health monitoring, winning several international awards. In 2018 Hexoskin launched a remote health monitoring system for astronauts on the International Space Station. Hexoskin recently reached the milestone of 100 scientific publications. Pierre-Alexandre earned his MASc and his BEng in Electrical Engineering from the Ecole Polytechnique de Montreal and became a lecturer there teaching machine learning.  He completed the Harvard Business School HBX Core program with high honors.  Pierre-Alexandre is also an advocate for transparency in healthcare, patient empowerment, and healthcare innovation through design.

Hosted by: Angela McIntyre, Executive Director, eWEAR Initiative
Sponsored by: PHIND Center, Department of Radiology, eWEAR Initiative

ZOOM LINK HERE

“A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning with Application to the Semantic Hashing of CT Images”
Masoud Badiei Khuzani, PhD

“Prediction of Clinical Outcomes in Diffuse Large B-Cell Lymphoma (DLBCL) Utilizing Radiomic Features Derived from Pretreatment Positron Emission Tomography (PET) Scan”
Eduardo Somoza, MD