![3rd Annual Diversity & Inclusion Forum @ Virtual Event - See Description to Register](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/09/Screen-Shot-2020-09-18-at-1.54.02-PM-300x255.png)
Join us for the 3rd Annual Diversity and Inclusion Forum on Friday, October 9, 2020 on Zoom! This virtual event will highlight innovative workshops developed by our residents and fellows with their educational mentors who have participated in the 2019-2020 cohort of the Leadership Education in Advancing Diversity Program.
The event will be an enriching opportunity for all faculty, residents, fellows, postdocs, students, staff, and community members to learn tools and strategies to enable them to become effective change agents for diversity, equity, and inclusion in medical education.
All are welcome to participate and we look forward to seeing you on Friday, October 9!
Register here:
https://mailchi.mp/046c21726371/diversityforum2020-1632872?e=4a913cab2d
![StanfordMed LIVE - Disability Town Hall @ Virtual Event - See Description for Livestream Link](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/09/smac-logo2-300x300.png)
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
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.
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.
![Stanford School of Medicine's 2nd Annual Conference on Disability in Healthcare and Medicine](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/10/Screen-Shot-2021-03-04-at-10.39.22-AM-e1614883544333-300x300.jpeg)
Date: April 10, 2021 (8 AM-6PM)
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- 8 AM-8:20 AM opening remarks Zainub and Pete
- 8:20 AM-9:20 AM Talk 1 “I fought the law and no one won”
- 10 minute Break
- 9:30 AM-10:30 AM talk 2 students and doctors with disabilities panel
- 20 minute break
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- 10:50 AM-11:50 AM Breakout
- One hour lunch (TBD)
- 12:50 PM-1:50 PM Talk 3 the frontiers of disability research
- Lisa Meeks is moderating
- Bonnie Swenor invited
- 10 minute break
- 2:00 PM-3:00 PM breakout 2
- 10 minute break
- 3:10 PM-4:10 PM talk 4 do-it-yourself disability advocacy (Poullos/Tolchin with students)
- 4:10 PM-4:30 PM closing remarks
- 4:30 PM-6 PM virtual happy hour
![Alone in the Ring - presented by SMAC and Stanford Medicine and the Muse](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/09/Screen-Shot-2021-09-16-at-11.12.58-AM-300x193.png)
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?
![Health Equity Action Leadership (HEAL Network) Fireside Chat](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/09/Screen-Shot-2021-09-16-at-11.21.47-AM-300x191.png)
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
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.
![IBIIS & AIMI Seminar: Facilitating Patient and Clinician Value Considerations into AI for Precision Medicine @ Clark Center S360 - Zoom Details on IBIIS website](http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2024/05/mildred-cho_profilephoto-300x300.jpg)
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.
![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](http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2024/06/Ifeoma-Okoye-MBBS-FWACS-FMCR-1-300x300.jpg)
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.