Calendar

Apr
30
Fri
2021
Racial Equity Challenge: Race in society @ Zoom
Racial Equity Challenge: Race in society
Apr 30 @ 12:00 pm – 1:00 pm Zoom
Racial Equity Challenge: Race in society @ Zoom

Targeted violence continues against Black Americans, Asian Americans, and all people of color. The department of radiology diversity committee is running a racial equity challenge to raise awareness of systemic racism, implicit bias and related issues. Participants will be provided a list of resources on these topics such as articles, podcasts, videos, etc., from which they can choose, with the “challenge” of engaging with one to three media sources prior to our session (some videos are as short as a few minutes). Participants will meet in small-group breakout sessions to discuss what they’ve learned and share ideas.

Please reach out to Marta Flory, flory@stanford.edu with questions. For details about the session, including recommended resources and the Zoom link, please reach out to Meke Faaoso at mfaaoso@stanford.edu.

May
11
Tue
2021
Cancer Early Detection Seminar Series - Michael Berger, Ph.D. @ Zoom - See Description for Zoom Link
Cancer Early Detection Seminar Series – Michael Berger, Ph.D.
May 11 @ 11:00 am – 12:00 pm Zoom - See Description for Zoom Link
Cancer Early Detection Seminar Series - Michael Berger, Ph.D. @ Zoom - See Description for Zoom Link

CEDSS: “Building a Scalable Clinical Genomics Program: How tumor, normal, and plasma DNA sequencing are informing cancer care, cancer risk, and cancer detection”

 

Michael Berger, Ph.D.

Elizabeth and Felix Rohatyn Chair & Associate Director of the Marie-Josée and Henry R. Kravis Center for Molecular Oncology
Memorial Sloan Kettering Cancer Center

 

Zoom Details
Meeting URL: https://stanford.zoom.us/s/92559505314
Dial: US: +1 650 724 9799  or +1 833 302 1536 (Toll Free)
Meeting ID: 925 5950 5314
Passcode: 418727

11:00am – 12:00pm Seminar & Discussion
RSVP Here

 

ABSTRACT
Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of oncogenomic mutations that can be targeted therapeutically. To accelerate enrollment to clinical trials of molecularly targeted agents and guide treatment selection, we have established a center-wide, prospective clinical sequencing program at Memorial Sloan Kettering Cancer Center using a custom, paired tumor-blood normal sequencing assay (MSK-IMPACT), which we have used to profile more than 50,000 patients with solid tumors. Yet beyond just the characterization of tumor-specific alterations, the inclusion of blood DNA has readily enabled the identification of germline risk alleles and somatic mutations associated with clonal hematopoiesis. To complement this approach, we have also implemented a ‘liquid biopsy’ cfDNA panel (MSK-ACCESS) for cancer detection, surveillance, and treatment selection and monitoring. In my talk, I will describe the prevalence of somatic and germline genomic alterations in a real-world population, the clinical benefits of cfDNA assessment, and how clonal hematopoiesis can inform cancer risk and confound liquid biopsy approaches to cancer detection.

 

ABOUT
Michael Berger, PhD, holds the Elizabeth and Felix Rohatyn Chair and is Associate Director of the Marie-Josée and Henry R. Kravis Center for Molecular Oncology at Memorial Sloan Kettering Cancer Center, a multidisciplinary initiative to promote precision oncology through genomic analysis to guide the diagnosis and treatment of cancer patients. He is also an Associate Attending Geneticist in the Department of Pathology with expertise in cancer genomics, computational biology, and high-throughput DNA sequencing technology. His laboratory is developing experimental and computational methods to characterize the genetic makeup of individual cancers and identify genomic biomarkers of drug response and resistance. As Scientific Director of Clinical NGS in the Molecular Diagnostics Service, he oversees the development and bioinformatics associated with clinical sequencing assays, and he helped lead the development and implementation of MSK-IMPACT, a comprehensive FDA-authorized tumor sequencing panel that been used to profile more than 60,000 tumors from advanced cancer patients at MSK. The resulting data have enabled the characterization of somatic and germline biomarkers across many cancer types and the identification of mutations associated with clonal hematopoiesis. Dr. Berger also led the development of a clinically validated plasma cell-free DNA assay, MSK-ACCESS, which his laboratory is using to explore tumor evolution, acquired drug resistance, and occult metastatic disease. He received his Bachelor’s Degree in Physics from Princeton University and his Ph.D. in Biophysics from Harvard University.

 

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

Jun
3
Thu
2021
IMMERS - Stanford Medical Mixed Reality Panel Discussion Series @ Zoom
IMMERS – Stanford Medical Mixed Reality Panel Discussion Series
Jun 3 @ 9:00 am – 10:30 am Zoom
IMMERS - Stanford Medical Mixed Reality Panel Discussion Series @ Zoom

Join us for a panel on Behavioral XR on Thursday, June 3rd from 9:00 – 10:30 am PDT.  The event will start with a one-hour panel discussion featuring Dr. Elizabeth McMahon, a psychologist with a private practice in California; Sarah Hill of Healium, a company developing XR apps for mental fitness based in Missouri; Christian Angern of Sympatient, a company developing VR for anxiety therapy based in Germany; and Marguerite Manteau-Rao of Penumbra, a medical device company based in California.  This panel will be moderated by Dr. Walter Greenleaf of Stanford’s Virtual Human Interaction Lab (VHIL) and Dr. Christoph Leuze of the Stanford Medical Mixed Reality (SMMR) program.  Immediately following the panel discussion, you are also invited to a 30-minute interactive session with the panelists where questions and ideas can be explored in real time.

 

Register here to save your place now!  After registering, you will receive a confirmation email containing information about joining the meeting.

 

Please visit this page to subscribe to our events mailing list.

 

Sponsored by Stanford Medical Mixed Reality (SMMR)

Jul
16
Fri
2021
Radiology-Wide Research Conference @ Zoom – Details can be found here: https://radresearch.stanford.edu
Radiology-Wide Research Conference
Jul 16 @ 12:00 pm – 1:00 pm Zoom – Details can be found here: https://radresearch.stanford.edu
Radiology-Wide Research Conference @ Zoom – Details can be found here: https://radresearch.stanford.edu

Radiology Department-Wide Research Meeting

• Research Announcements
• Mirabela Rusu, PhD – Learning MRI Signatures of Aggressive Prostate Cancer: Bridging the Gap between Digital Pathologists and Digital Radiologists
• Akshay Chaudhari, PhD – Data-Efficient Machine Learning for Medical Imaging

Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.

 

Hosted by: Kawin Setsompop, PhD
Sponsored by: the the Department of Radiology

Aug
3
Tue
2021
2021 AIMI Symposium + BOLD-AIR Summit @ Virtual Livestream
2021 AIMI Symposium + BOLD-AIR Summit
Aug 3 @ 8:00 am – Aug 4 @ 3:00 pm Virtual Livestream
2021 AIMI Symposium + BOLD-AIR Summit @ Virtual Livestream

Stanford AIMI Director Curt Langlotz and Co-Directors Matt Lungren and Nigam Shah invite you to join us on August 3 for the 2021 Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) Symposium. The virtual symposium will focus on the latest, best research on the role of AI in diagnostic excellence across medicine, current areas of impact, fairness and societal impact, and translation and clinical implementation. The program includes talks, interactive panel discussions, and breakout sessions. Registration is free and open to all.

 

Also, the 2nd Annual BiOethics, the Law, and Data-sharing: AI in Radiology (BOLD-AIR) Summit will be held on August 4, in conjunction with the AIMI Symposium. The summit will convene a broad range of speakers in bioethics, law, regulation, industry groups, and patient safety and data privacy, to address the latest ethical, regulatory, and legal challenges regarding AI in radiology.

 

REGISTER HERE

Sep
22
Wed
2021
IBIIS & AIMI Seminar: Seeing the Future from Images: ML-Based Models for Cancer Risk Assessment @ Zoom: https://stanford.zoom.us/j/99474772502?pwd=NEQrQUQ0MzdtRjFiYU42TCs2bFZsUT09
IBIIS & AIMI Seminar: Seeing the Future from Images: ML-Based Models for Cancer Risk Assessment
Sep 22 @ 11:00 am – 12:00 pm Zoom: https://stanford.zoom.us/j/99474772502?pwd=NEQrQUQ0MzdtRjFiYU42TCs2bFZsUT09

 

Regina Barzilay, PhD
School of Engineering Distinguished Professor for AI and Health
Electrical Engineering and Computer Science Department
AI Faculty Lead at Jameel Clinic for Machine Learning in Health
Computer Science and Artificial Intelligence Lab
Massachusetts Institute of Technology

Abstract:
In this talk, I will present methods for future cancer risk from medical images. The discussion will explore alternative ways to formulate the risk assessment task and focus on algorithmic issues in developing such models. I will also discuss our experience in translating these algorithms into clinical practice in hospitals around the world.

Sep
27
Mon
2021
2021 IBIIS & AIMI Virtual Retreat
Sep 27 @ 1:00 pm – 4:30 pm https://ibiis.stanford.edu/events/retreat/2021Hybrid.html

Keynote:

Self-Supervision for Learning from the Bottom Up

Why do self-supervised learning? A common answer is: “because data labeling is expensive.” In this talk, I will argue that there are other, perhaps more fundamental reasons for working on self-supervision. First, it should allow us to get away from the tyranny of top-down semantic categorization and force meaningful associations to emerge naturally from the raw sensor data in a bottom-up fashion. Second, it should allow us to ditch fixed datasets and enable continuous, online learning, which is a much more natural setting for real-world agents. Third, and most intriguingly, there is hope that it might be possible to force a self-supervised task curriculum to emerge from first principles, even in the absence of a pre-defined downstream task or goal, similar to evolution. In this talk, I will touch upon these themes to argue that, far from running its course, research in self-supervised learning is only just beginning.

Oct
6
Wed
2021
Early Detection of Cancer Conference @ Virtual Event
Early Detection of Cancer Conference
Oct 6 – Oct 8 all-day Virtual Event
Early Detection of Cancer Conference @ Virtual Event

Cancer Research UK, OHSU Knight Cancer Institute and the Canary Center at Stanford, present the Early Detection of Cancer Conference series. The annual Conference brings together experts in early detection from multiple disciplines to share ground breaking research and progress in the field.

The Conference is part of a long-term commitment to invest in early detection research, to understand the biology behind early stage cancers, find new detection and screening methods, and enhance uptake and accuracy of screening.

The 2021 conference will take place October 6-8 virtuallyFor more information visit the website: http://earlydetectionresearch.com/

Oct
12
Tue
2021
Cancer Early Detection Seminar Series - Azra Raza, MD @ Venue coming soon!
Cancer Early Detection Seminar Series – Azra Raza, MD
Oct 12 @ 11:00 am – 12:00 pm Venue coming soon!
Cancer Early Detection Seminar Series - Azra Raza, MD @ Venue coming soon!

CEDSS: The First Cell: A new model for cancer research and treatment

Azra Raza, M.D.
Chan Soon-Shiong Professor of Medicine
Director, Myelodysplastic Syndrome Center
Columbia University Medical Center

 

Location: Zoom
Meeting URL: https://stanford.zoom.us/s/99340345860
Dial: US: +1 650 724 9799  or +1 833 302 1536 (Toll Free)
Meeting ID: 993 4034 5860
Passcode: 711508

RSVP Here

 

ABSTRACT

Cancer research continues to be predicated on a 1970’s model of research and treatment. Despite half a century of intense research, we are failing spectacularly to improve the outcome for patients with advanced disease. Those who are cured continue to be treated mostly with the older strategies (surgery-chemo-radiation). Our contention is that the real solution to the cancer problem is to diagnose cancer early, at the stage of The First Cell. The rapidly evolving technologies are doing much in this area but need to be expanded. We study a pre-leukemic condition called myelodysplastic syndrome (MDS) with the hope that we can detect the first leukemia cells as the disease transforms to acute myeloid leukemia (AML). Towards this end, we have collected blood and bone marrow samples on MDS and AML patients since 1984. Today, our Tissue Repository has more than 60,000 samples. We propose novel methods to identify surrogate markers that can identify the First Cell through studying the serial samples of patients who evolve from MDS to AML.

 

ABOUT

Dr. Raza is a Professor of Medicine and Director of the MDS Center at Columbia University in New York, NY.She started her research in Myelodisplastic Syndromes (MDS) in 1982 and moved to Rush University, Chicago, Illinois in 1992, where she was the Charles Arthur Weaver Professor in Oncology and Director, Division of Myeloid Diseases. The MDS Program, along with a Tissue Repository containing more than 50,000 samples from MDS and acute leukemia patients was successfully relocated to the University of Massachusetts in 2004 and to Columbia University in 2010.

Before moving to New York, Dr. Raza was the Chief of Hematology Oncology and the Gladys Smith Martin Professor of Oncology at the University of Massachussetts in Worcester. She has published the results of her laboratory research and clinical trials in prestigious, peer reviewed journals such as The New England Journal of Medicine, Nature, Blood, Cancer, Cancer Research, British Journal of Hematology, Leukemia, and Leukemia Research. Dr. Raza serves on numerous national and international panels as a reviewer, consultant and advisor and is the recipient of a number of awards.

 

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

Nov
17
Wed
2021
IBIIS & AIMI Seminar: Deep Learning for Histology Images Analysis @ Zoom: https://stanford.zoom.us/j/91788140120?pwd=K2NvMHZ2SUFVWjc1d2xJUndjTG9lQT09
IBIIS & AIMI Seminar: Deep Learning for Histology Images Analysis
Nov 17 @ 12:00 pm – 1:00 pm Zoom: https://stanford.zoom.us/j/91788140120?pwd=K2NvMHZ2SUFVWjc1d2xJUndjTG9lQT09

Saeed Hassanpour, PhD
Associate Professor of Biomedical Data Science
Associate Professor of Epidemiology
Associate Professor of Computer Science
Dartmouth Geisel School of Medicine

Deep Learning for Histology Images Analysis

Abstract:
With the recent expansions of whole-slide digital scanning, archiving, and high-throughput tissue banks, the field of digital pathology is primed to benefit significantly from deep learning technology. This talk will cover several applications of deep learning for characterizing histopathological patterns on high-resolution microscopy images for cancerous and precancerous lesions. Furthermore, the current challenges for building deep learning models for pathology image analysis will be discussed and new methodological advances to address these bottlenecks will be presented.

About:

Dr. Saeed Hassanpour is an Associate Professor in the Departments of Biomedical Data Science, Computer Science, and Epidemiology at Dartmouth College. His research is focused on machine learning and multimodal data analysis for precision health. Dr. Hassanpour has led multiple NIH-funded research projects, which resulted in novel machine learning and deep learning models for medical image analysis and clinical text mining to improve diagnosis, prognosis, and personalized therapies. Before joining Dartmouth, he worked as a Research Engineer at Microsoft. Dr. Hassanpour received his Ph.D. in Electrical Engineering with a minor in Biomedical Informatics from Stanford University and completed his postdoctoral training at Stanford Center for Artificial Intelligence in Medicine & Imaging.