Calendar

May
28
Thu
2020
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link
May 28 @ 1:30 pm – 2:30 pm
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link

Thursday MIPS Roundtable: Faculty Lab Showcase

 

1:30-2:00 PM | Dr. Jianghong Rao, Ph.D.

Cellular and Molecular Imaging Laboratory (CMIL)

Professor of Radiology and, by courtesy, of Chemistry

Stanford University

 

2:00-2:30 PM | Dr. Zhen Cheng, Ph.D.

Cancer Molecular Imaging Chemistry Laboratory (CMICL)

Associate Professor 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.

5/28 Meeting URL: https://stanford.zoom.us/j/92834097988
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 928 3409 7988

Jun
11
Thu
2020
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link
Jun 11 @ 1:30 pm – 2:30 pm
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link

Thursday MIPS Roundtable: Meet our MIPS Instructors 

 

1:30-2:00 PM | Dr. Katie Wilson, Ph.D.
“Optical and Acoustic Molecular Imaging to Identify Lymph Node Metastasis in Head and Neck Cancer.”
Instructor, Radiology
Stanford University

2:00-2:30 PM | Dr. Corinne Beinat, Ph.D.
“Molecular Imaging of Tumor Metabolism”
Instructor, Radiology
Stanford University

 

MIPS Roundtables are every other Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.

 

Please note Zoom information does change week to week.

6/11 Meeting URL: https://stanford.zoom.us/j/95475611159
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 954 7561 1159

Jun
16
Tue
2020
PHIND Seminar – Anoop Rao, M.D. & Eric Dy, Ph.D. @ Zoom - See Description for Zoom Link
Jun 16 @ 11:00 am – 12:00 pm
PHIND Seminar - Anoop Rao, M.D. & Eric Dy, Ph.D. @ Zoom - See Description for Zoom Link

PHIND Seminar Series

 

11:00-11:30 AM | Dr. Anoop Rao, M.D., M.S.
“Wearable Sensing for Neonates”

Clinical Instructor, Pediatrics (Neonatology)

Lucile Packard Children’s Hospital

Stanford University School of Medicine

 

11:30-12:00 PM | Dr. Eric Dy, Ph.D.
“Crowdsourced data and machine learning to design the future of prenatal care”

Co-founder and CEO

Bloomlife

 

12:00pm – 1:00pm Seminar & Discussion
RSVP Here: https://www.onlineregistrationcenter.com/PHIND061620

This seminar will be available in person and via a Zoom live stream.
Meeting URL: https://stanford.zoom.us/j/92848236311
Dial: +1 650 724 9799  or +1 833 302 1536
Meeting ID: 928 4823 6311

 

Dr. Anoop Rao Bio
Anoop Rao is a Clinical Instructor in Pediatrics (Division of Neonatology) at the Lucile Packard Children’s Hospital. After completing early medical training in India, he obtained his MS from MIT, and completed clinical training in Pediatrics from Columbia and fellowships in Neonatal Critical Care from Stanford and Biomedical Informatics from Harvard. He spent over 5 years at AgaMatrix, a startup focused on developing high-accuracy blood glucose meters.

At Stanford, his research is on contact and non-contact monitoring of infants. Anoop collaborates extensively with industry and is actively supported by NIH/Maternal and Child Health Research Institute.

Dr. Eric Dy Bio
Eric Dy, PhD is co-founder and CEO of Bloomlife, a women’s health company designing remote prenatal care solutions to improve the health of women and babies.  Eric brings unique perspective on the opportunities and challenges in emerging healthcare technologies and delivery models informed by multidisciplinary technical expertise leading business development for Europe’s leading R&D institute, imec. Eric earned his BSc in Bioengineering from Cornell and his MSc and PhD in Biomedical Engineering from UCLA.  Bloomlife has been recognized for their pioneering work winning Fast Company World Changing Ideas, Johnson & Johnson Quickfire Challenge, Richard Branson’s Extreme Tech Challenge, MedTech Innovator Award, and speaking at the White House Precision Public Health Summit.

Dr. Eric Dy Abstract
The period from conception through the first 1000 days of life are the most critical for lifelong health and development, yet too often we are failing women and babies at this time.  High risk pregnancies are on the rise, access to care is increasingly a problem, and pregnancy complications such as preterm birth now affect 1 in 10 babies.   Despite these growing challenges, the way we deliver prenatal care has not fundamentally changed in over 60 years.  We need smarter tools, better data, and scalable solutions to improve the health of moms and babies globally.

In this talk Bloomlife co-founder and CEO will share their strategy for designing the future of prenatal care.  He will discuss how clinical grade wearables, in the hands of mom, has helped create the largest dataset on pregnancy in the world, and how AI applied to this dataset is seeding breakthrough screening and diagnostic tools to help solve global maternal health issues including preterm birth.

Hosted by: Sanjiv Sam Gambhir, M.D., Ph.D.
Sponsored by: PHIND Center, Department of Radiology and eWEAR Initiative

Jun
17
Wed
2020
IBIIS & AIMI Seminar: The Cancer Imaging Archive: Addressing the Cancer Imaging Community’s Data Sharing Needs @ Zoom: https://stanford.zoom.us/j/99478042201?pwd=Vzg0ViswWlNBbDVoUXh3R01zSStUZz09
Jun 17 @ 12:00 pm – 1:00 pm

Justin Kirby
Technical Project Manager
Frederick National Laboratory for Cancer Research
Technical Director, Cancer Imaging Informatics Lab

The Cancer Imaging Archive:
Addressing the Cancer Imaging Community’s Data Sharing Needs
Access to large, high quality datasets collected from heterogeneous patient populations and imaging modalities is essential for researchers to explore and validate hypotheses that will generalize beyond their own institution. This is especially important as researchers increasingly apply deep learning or radiomics techniques where minor variations in the image data can lead to spurious correlations with patient diagnoses and outcomes.Unfortunately, regulatory constraints make broad sharing of medical data from human subjects a complex process which most individual investigators and institutions are not equipped to handle. At the same time, there is increasing pressure on researchers from funding agencies and publishers to share data.  The National Cancer Institute funded The Cancer Imaging Archive (TCIA) in 2011 to help bridge this gap.  TCIA provides hands-on assistance to help investigators safely de-identify their data, as well as long term hosting to assure stable access to research community.  Each dataset is published with a title, author list, abstract, and persistent Digital Object Identifier so that they can be properly cited by other researchers and linked to their ORCiD profile.TCIA now contains over 115 unique data collections of more than 50 million images. Recognizing that images alone are not enough to conduct meaningful research, most collections are linked to rich supporting data including patient outcomes, treatment information, genomic / proteomic analyses, and expert image analyses (segmentations, annotations, and image features). This lecture will teach attendees how to propose new datasets for publication and how to access existing datasets on TCIA.  It will also touch on common use cases for public data sharing and highlight a variety of research activities conducted using TCIA data.
About:
Justin Kirby received his undergraduate degree in information technology at Duquesne University, Pittsburgh.  In 2008 he joined the Frederick National Laboratory for Cancer Research to support NCI’s Cancer Imaging Program.  During his tenure at the lab he has focused on image de-identification and projects designed to improve reproducibility and transparency in cancer imaging research.  Most notably, his team founded The Cancer Imaging Archive (TCIA) in 2011.  Through this research resource he has helped support the data sharing requirements of journals, NIH grants, challenge competitions, and major NCI research initiatives including the National Clinical Trials Network, Quantitative Imaging Network, The Cancer Genome Atlas and the Clinical Proteomics Tumor Analysis Consortium.  He serves as a member of the Medical Physics Dataset Article Subcommittee, and has been an invited instructor for imaging informatics courses at the Radiological Society of North America’s annual meetings since 2012.
Jun
20
Sat
2020
Stanford School of Medicine’s – 1st Annual Conference on Disability in Healthcare and Medicine @ Zoom Webinar
Jun 20 @ 8:00 am – 2:30 pm
Stanford School of Medicine's - 1st Annual Conference on Disability in Healthcare and Medicine @ Zoom Webinar

Stanford School of Medicine’s
1st Annual Conference on Disability in Healthcare and Medicine

Saturday, June 20, 2020

8:00am – 2:30pm Pacific Daylight Time (PDT)
Zoom Webinar

The conference goals are:

  • Supporting students and healthcare providers with disabilities
  • Training healthcare providers to better care for patients with disabilities
  • Research into the intersection of providers and patients with disabilities

Target audience:

  • Nursing students and nurses
  • PA students and PA’s
  • Medical students and medical doctors
  • All other interested healthcare providers and allies

Register Today!

Jun
25
Thu
2020
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link
Jun 25 @ 1:30 pm – 2:30 pm
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link

Thursday MIPS Roundtable: Faculty Lab Showcase

 

MIPS Roundtables are every other Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.

 

1:30-2:00 PM | Dr. Brian Rutt, Ph.D.
Cellular & Molecular MRI Laboratory (CMMRIL)
Professor of Radiology
Stanford University

2:00-2:30 PM | Dr. Kathy Ferrara, Ph.D.
Ferrara Laboratory: Image-guided Drug Delivery
Professor of Radiology
Stanford University

 

Please note Zoom information does change week to week.

6/25 Webinar URL: https://stanford.zoom.us/j/91635637393?pwd=c09vUXYyeU5VeHJBaUJVRHQrT3FJdz09
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 916 3563 7393
Webinar Password: 271364

Jul
9
Thu
2020
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link
Jul 9 @ 1:30 pm – 2:30 pm
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link

Thursday MIPS Roundtable: Meet our MIPS Instructors 

 

MIPS Roundtables are every other Thursday from 1:30-2:30pm showcasing various topics and are open to all interested. Note we will take a break through late July and August. 

 

1:30-2:00 PM | Dr. Ahmed El Kaffas, Ph.D.
Translational Ultrasound for Tissue Characterization and Stimulation
Instructor, Radiology
Stanford University

 

2:00-2:30 PM | Dr. Brett Fite, Ph.D.
Combining Focal and Immunotherapies
Instructor, Radiology
Stanford University

 

Please note Zoom information does change week to week.

7/9 Webinar URL: https://stanford.zoom.us/j/91909413178
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 919 0941 3178
Password: 572746

Jul
16
Thu
2020
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link
Jul 16 @ 1:30 pm – 2:30 pm
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link

Thursday MIPS Roundtable: Meet our MIPS Instructors 

 

MIPS Roundtables are Thursdays from 1:30-2:30pm showcasing various topics and are open to all interested. Note this will be our last summer Roundtable and we will take a break through late July and August. 

 

1:30-2:00 PM | Dr. Josquin Foiret, Ph.D.
High throughput ultrasound imaging for improved diagnosis
Instructor, Radiology
Stanford University

 

2:00-2:30 PM | Dr. Jinghang Xie, Ph.D.
TESLA probes for imaging T cell-mediated cytotoxic response to immunotherapy
Instructor, Radiology
Stanford University

 

Please note Zoom information does change week to week.

7/16 Webinar URL: https://stanford.zoom.us/j/94952044130
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 949 5204 4130
Password: 963699

Jul
21
Tue
2020
PHIND Seminar – Vishnu Shankar @ Zoom - See Description for Zoom Link
Jul 21 @ 11:00 am – 12:00 pm
PHIND Seminar - Vishnu Shankar @ Zoom - See Description for Zoom Link

PHIND Seminar Series: What is in your sweat and what can it mean for health and disease?
11:00 AM – 12:00 PM: Seminar & Discussion
RSVP: https://www.onlineregistrationcenter.com/VShankar

 

Presenter:
Vishnu Shankar, M.S.
Department of Chemistry
Stanford University

 

Principal Investigators:
Michael Snyder, Ph.D.
Stanford W. Ascherman, MD, FACS Professor in Genetics
Stanford University

Robert Tibshirani, Ph.D.
Professor of Biomedical Data Science and of Statistics
Stanford University

Richard Zare, Ph.D.
Marguerite Blake Wilbur Professor in Natural Science and Professor, by courtesy, of Physics
Stanford University

Location
Webinar URL: https://stanford.zoom.us/s/99817512229?pwd=QitCTjRXMEdBTWZyd29MTHYyNU5Xdz09
Dial: +1 650 724 9799  or +1 833 302 1536
Webinar ID: 998 1751 2229
Password: 489011

 

ABSTRACT

Sweat is a complex fluid known to be rich in electrolytes, small molecules, and fatty acids. Although adults can sweat up to 10 liters per day, little is still known about the chemical composition of sweat, how this changes, and what are its implications for health and disease. We demonstrate a powerful approach to help elucidate this link, where collecting samples simply requires swabbing a glass slide across one’s forehead in less than 30 seconds. Using the combination of desorption electrospray ionization mass spectrometry and statistical machine learning, our approach can successfully detect over 10,000 metabolites in sweat and identify metabolic changes in the sweat profile related to gender, age, and disease. As an example, we demonstrate in a cohort of 65 subjects the possibility of using just a few metabolites detected in sweat to successfully identify patients with renal disease. More generally, our approach suggests the possibility of using the sweat profile to non-invasively assess individual risk for metabolic diseases in the theme of “Precision Medicine.”

 

ABOUT VISHNU SHANKAR

Vishnu Shankar recently graduated with his master’s degree in computer science, with a specialization in artificial intelligence from Stanford University. He completed his bachelor’s degree with honors in mathematical and computational sciences in 2018, also at Stanford, with his senior thesis on Bayesian networks for incorporating effect modifiers in meta-analysis.  In addition, his background spans biology, mathematics, chemistry, statistics, operations research, physics, and computing. Vishnu has published 6 papers and 3 articles in fields including protein structural prediction, comparison of clinical guidelines cost-effectiveness in type 2 diabetes, development of programs to combat mental illness, cancer diagnosis with analytical chemistry and machine learning, and related areas.   He is also the founder of the CARES organization to support peer student wellness at college campuses, for which he won the Asoka Youth Changemaker award sponsored by Boehringer Ingelheim.  Vishnu has enthusiastically pursued science since his middle school days and has worked on demonstrating the possibilities of DNA computing, simulating protein folding, studying the genetic modifications in fruits and vegetables, and more. He has been recognized for his scientific research as an Intel Science Talent finalist, Google Science Fair Regional finalist, recipient of the American Institute of Aeronautics and Astronautics Excellence award for his work on epidemiology modeling.  Vishnu has interned at Caltech and Genentech, where he applied experimental techniques to purify and study protein behavior including dialysis, titration, chromatography for early stage drug development.  He was also selected as one of the two high school students to represent the western US Confucius Institute in Student Leaders Exchange Program in China as a non-native Mandarin speaker.

 

Hosted by: Sanjiv Sam Gambhir, M.D., Ph.D.
Sponsored by the PHIND Center and the Department of Radiology

Jul
22
Wed
2020
SCIT Quarterly Seminar @ via ZOOM: https://stanford.zoom.us/j/99587932751?pwd=L0VOWWJJKytzSkVTT2w1N2FzUzdjUT09
Jul 22 @ 10:00 am – 11:00 am

“Kernel Locally Sensitive Hashing for the Content Based Image Retrieval”
Masoud Badiei Khuzani, PhD

ABSTRACT: Due to the development of the Internet at large scale and the availability of various image capturing devices such as digital cameras, smart mobile phones, image scanners, digital image databases are expanding very rapidly. With the popularity of the computer based smart system, content based image retrieval (CBIR) has grown in different areas to research. Efficient image browsing, image retrieval and searching tools are needed to users in various domains including histopathology image datasets. To achieve image retrieval, many retrieval systems have been developed. Two main frameworks are the content based retrieval and text based retrieval. Text based frameworks were introduced in 1970s. In this approach, text descriptors are used to annotate the image. However, annotating a large data-base is laborious. In CBIR, contents of image are used to annotate to perform retrieval in an unsupervised manner. In this talk, we propose a novel framework for the CBIR using kernel locally sensitive hash functions. In particular, we propose a novel framework for constructing a set of new hash functions based on radial kernels to speed up the image retrieval process.  As a preliminary result, we validate our proposed retrieval system on the MNIST data-set.

“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

ABSTRACT: Diffuse Large B-Cell lymphoma (DLBCL) is the most common type of lymphoma, accounting for a third of cases worldwide. Despite established prognostic scores and advancements in treatment, the five-year percent survival rate for this patient population nears sixty percent, possibly related to the known heterogeneity of DLBCL. PET imaging features may characterize this diverseness and help predict clinical outcomes even before treatment initiation.  The approach we have been employing to address this need is the creation of a prognostic model from pretreatment clinical and imaging data of DLBCL patients seen at Stanford Hospital and Clinics (SHC). In this presentation, we will provide an update on the radiomics component of our model. Preliminary results, efforts towards standardization, and future directions will be covered. Ultimately, we hope our efforts will lead to the development of a prognostic model that can be utilized to guide treatment selection in high risk DLBCL patients in an attempt to circumvent relapse or refractory disease.