Calendar

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

Oct
14
Thu
2021
Alone in the Ring – presented by SMAC and Stanford Medicine and the Muse
Oct 14 @ 5:30 pm – 7:00 pm
Alone in the Ring - presented by SMAC and Stanford Medicine and the Muse

Alone in the Ring (a research-based theatre production about inclusive healthcare workplaces) is coming to campus during the Annual Stanford School of Medicine Diversity Week and National Disability Employment Awareness Month, SMAC and Stanford Medicine and the Muse hope to continue the discussion on how to spark and sustain change towards inclusive workspaces. Alone in the Ring is followed by a discussion between the team and audience members. During the presentation, audience members are encouraged to reflect: How inclusive is your workspace? How could you make it more accessible?

Register for this event 

Oct
22
Fri
2021
CME Grand Rounds – Jocelyn D. Chertoff, MD, MS @ Zoom - See Description for Zoom Link
Oct 22 @ 12:00 pm – 1:00 pm
CME Grand Rounds - Jocelyn D. Chertoff, MD, MS @ Zoom - See Description for Zoom Link

CME Grand Rounds – Topic: TBD

Jocelyn D. Chertoff, MD, MS
Professor
Radiology, Obstetrics & Gynecology
Chair, Radiology
Dartmouth Hitchcock Medical Center

 

Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/600003703?pwd=RjcwS2MvOG1qVkxyL3U0RmNtUDVWdz09
Meeting ID: 600 003 703
Password: 566048
Or iPhone one-tap (US Toll): +18333021536,,600003703# or +16507249799,,600003703#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
International numbers available: https://stanford.zoom.us/u/acuqphnvqT

 

ABSTRACT
Coming soon!

 

BIO
Coming soon!

Oct
26
Tue
2021
Health Equity Action Leadership (HEAL Network) Fireside Chat
Oct 26 @ 12:00 pm – 1:00 pm
Health Equity Action Leadership (HEAL Network) Fireside Chat

Office of Faculty Development and Diversity and SMAC.

The OFDD team welcomes all Stanford community members to join our inaugural Health Equity Action Leadership (HEAL Network) event, Health Equity Research in the Latinx Community, where faculty who do this work will share their experiences in a fireside chat panel.

Moderator: Lisa Goldman-Rosas

Speakers: Dr. Ken Sutha, Dr. Peter Poullos, Dr. Holly Tabor

Nov
4
Thu
2021
CME Grand Rounds Etta K. Moskowitz Lectureship – Elizabeth Krupinski, PhD @ Zoom - See Description for Zoom Link
Nov 4 @ 5:30 pm – 6:30 pm
CME Grand Rounds Etta K. Moskowitz Lectureship - Elizabeth Krupinski, PhD @ Zoom - See Description for Zoom Link

CME Grand Rounds Etta K. Moskowitz Lectureship – Topic: TBD

Elizabeth Krupinski, PhD
Professor & Vice Chair for Research
Radiology & Imaging Sciences
Emory University School of Medicine

 

Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/600003703?pwd=RjcwS2MvOG1qVkxyL3U0RmNtUDVWdz09
Meeting ID: 600 003 703
Password: 566048
Or iPhone one-tap (US Toll): +18333021536,,600003703# or +16507249799,,600003703#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
International numbers available: https://stanford.zoom.us/u/acuqphnvqT

 

ABSTRACT
Coming soon!

 

BIO
Coming soon!

Nov
12
Fri
2021
CME Grand Rounds – Michael Gisondi, MD @ Zoom - See Description for Zoom Link
Nov 12 @ 12:00 pm – 1:00 pm
CME Grand Rounds - Michael Gisondi, MD @ Zoom - See Description for Zoom Link

CME Grand Rounds – “Promote Your Academic Career Using Social Media”

Michael Gisondi, MD
Associate Professor & Vice Chair of Education
Emergency Medicine
Stanford University

 

Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/600003703?pwd=RjcwS2MvOG1qVkxyL3U0RmNtUDVWdz09
Meeting ID: 600 003 703
Password: 566048
Or iPhone one-tap (US Toll): +18333021536,,600003703# or +16507249799,,600003703#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
International numbers available: https://stanford.zoom.us/u/acuqphnvqT

 

ABSTRACT
Coming soon!

 

BIO
Coming soon!

Apr
17
Wed
2024
IBIIS & AIMI Seminar: Building Fair and Trustworthy AI for Healthcare @ Clark Center S360 - Zoom Details on IBIIS website
Apr 17 @ 12:00 pm – 1:00 pm

Roxana Daneshjou, MD, PhD
Assistant Professor, Biomedical Data Science & Dermatology
Assistant Director, Center of Excellence for Precision Heath & Pharmacogenomics
Director of Informatics, Stanford Skin Innovation and Interventional Research Group
Stanford University

Title: Building Fair and Trustworthy AI for Healthcare

Abstract: AI for healthcare has the potential to revolutionize how we practice medicine. However, to do this in a fair and trustworthy manner requires special attention to how AI models work and their potential biases. In this talk, I will cover the considerations for building AI systems that improve healthcare.

May
22
Wed
2024
IBIIS & AIMI Seminar: Facilitating Patient and Clinician Value Considerations into AI for Precision Medicine @ Clark Center S360 - Zoom Details on IBIIS website
May 22 @ 11:00 am – 12:00 pm

Mildred Cho, PhD
Professor of Pediatrics, Center of Biomedical Ethics
Professor of Medicine, Primary Care and Population Health
Stanford University

Title: Facilitating Patient and Clinician Value Considerations into AI for Precision Medicine

Abstract:
For the development of ethical machine learning (ML) for precision medicine, it is essential to understand how values play into the decision-making process of developers. We conducted five group design exercises with four developer participants each (N=20) who were asked to discuss and record their design considerations in a series of three hypothetical scenarios involving the design of a tool to predict progression to diabetes. In each group, the scenario was first presented as a research project, then as development of a clinical tool for a health care system, and finally as development of a clinical tool for their own health care system. Throughout, developers documented their process considerations using a virtual collaborative whiteboard platform. Our results suggest that developers more often considered client or user perspectives after changing the context of the scenario from research to a tool for a large healthcare setting. Furthermore, developers were more likely to express concerns arising from the patient perspective and societal and ethical issues such as protection of privacy after imagining themselves as patients in the health care system. Qualitative and quantitative data analysis also revealed that developers made reflective/reflexive statements more often in the third round of the design activity (44 times) than in the first (2) or second (6) rounds. These statements included statements on how the activity connected to their real-life work, what they could take away from the exercises and integrate into actual practice, and commentary on being patients within a health care system using AI. These findings suggest that ML developers can be encouraged to link the consequences of their actions to design choices by encouraging “empathy work” that directs them to take perspectives of specific stakeholder groups. This research could inform the creation of educational resources and exercises for developers to better align daily practices with stakeholder values and ethical ML design.

Jun
24
Mon
2024
IBIIS & AIMI Seminar: Deepening Collaboration with Stanford & Pennsylvania, Toward Developing Joint Strategies to Close the ‘Cancer Care’ & ‘Clinical Trial Volume’ Gap in LMICs @ Clark Center S360 - Zoom Details on IBIIS website
Jun 24 @ 12:30 pm – 1:30 pm

Ifeoma Okoye MBBS, FWACS, FMCR 
Professor of Radiology and Director
University of Nigeria Centre for Clinical Trials
College of Medicine, University of Nigeria

Title: Deepening Collaboration with Stanford & Pennsylvania, Toward Developing Joint Strategies to Close the ‘Cancer Care’ & ‘Clinical Trial Volume’ Gap in LMICs

Abstract
In this seminar I will be addressing the dire cancer survival outcomes in low- and middle-income countries (LMICs), with a particular focus on Sub-Saharan Africa. Cancer survival rates in Sub-Saharan Africa are alarmingly low. According to the World Health Organization, cancer deaths in LMICs account for approximately 70% of global cancer fatalities. In Nigeria, the five-year survival rate for breast cancer, one of the most common cancers, stands at a disheartening 10-30%, compared to over 80% in high-income countries. This stark disparity highlights the urgent need for sustained comprehensive cancer interventions in our region.

Here, I will discuss the pivotal role in the cancer control sphere, of a new software, ONCOSEEK, capable of early detecting 11 types of Cancers! It’s particular emphasis on the Patient Perspective, which aligns with our ethos of need for holistic patient care. In addition I will discuss recent developments on collaborative effort with the Gevaert lab at Stanford University and the University of Pennsylvania.

Sep
18
Wed
2024
IBIIS & AIMI Seminar – “GREEN: Generative Radiology Report Evaluation and Error Notation” & ” Leveraging Patch-Level Representation Learning with Vision Transformer for Prostate Cancer Foundation Models” @ Clark Center S360 - Zoom Details on IBIIS website
Sep 18 @ 12:00 pm – 1:00 pm
Sophie Ostmeier

Sophie Ostmeier, MD
Postdoctoral Scholar
Department of Radiology
Stanford School of Medicine

Title: GREEN: Generative Radiology Report Evaluation and Error Notation

Abstract
Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to the need for accurate medical communication about medical images. Existing automatic evaluation metrics either suffer from failing to consider factual correctness (e.g., BLEU and ROUGE) or are limited in their interpretability (e.g., F1CheXpert and F1RadGraph). In this paper, we introduce GREEN (Generative Radiology Report Evaluation and Error Notation), a radiology report generation metric that leverages the natural language understanding of language models to identify and explain clinically significant errors in candidate reports, both quantitatively and qualitatively. Compared to current metrics, GREEN offers: 1) a score aligned with expert preferences, 2) human interpretable explanations of clinically significant errors, enabling feedback loops with end-users, and 3) a lightweight open-source method that reaches the performance of commercial counterparts. We validate our GREEN metric by comparing it to GPT-4, as well as to error counts of 6 experts and preferences of 2 experts. Our method demonstrates not only higher correlation with expert error counts, but simultaneously higher alignment with expert preferences when compared to previous approaches.

Jeong Hoon Lee

Jeong Hoon Lee, PhD
Postdoctoral Researcher
Department of Radiology
Stanford School of Medicine

Title: Leveraging Patch-Level Representation Learning with Vision Transformer for Prostate Cancer Foundation Models

Abstract:
Recent advancements in self-supervised learning (SSL), emerging as an effective approach for imaging foundation models, enable the effective pretraining of AI models across multiple domains without the need for labels. Despite the rapid advancements, their application in medical imaging remains challenging due to the subtle difference between cancer and normal tissue. To address this limitation, in this study, we propose an AI architecture ProViCNet that employs the vision transformer (ViT) based segmentation architecture with patch-level contrastive learning for better feature representation. We validated our model in prostate cancer detection tasks using three types of magnetic resonance imaging (MRI) across multiple centers. To evaluate the performance of feature representation in this model, we performed downstream tasks with respect to Gleason grade score and race prediction. Our model demonstrated significant performance improvements compared to the state-of-the-art segmentation architectures. This study proposes a novel approach to developing foundation models for prostate cancer imaging overcoming SSL limitations.