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
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
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
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
Judy Gichoya, MD
Assistant Professor
Emory University School of Medicine
Measuring Learning Gains in Man-Machine Assemblage When Augmenting Radiology Work with Artificial Intelligence
Abstract
The work setting of the future presents an opportunity for human-technology partnerships, where a harmonious connection between human-technology produces unprecedented productivity gains. A conundrum at this human-technology frontier remains – will humans be augmented by technology or will technology be augmented by humans? We present our work on overcoming the conundrum of human and machine as separate entities and instead, treats them as an assemblage. As groundwork for the harmonious human-technology connection, this assemblage needs to learn to fit synergistically. This learning is called assemblage learning and it will be important for Artificial Intelligence (AI) applications in health care, where diagnostic and treatment decisions augmented by AI will have a direct and significant impact on patient care and outcomes. We describe how learning can be shared between assemblages, such that collective swarms of connected assemblages can be created. Our work is to demonstrate a symbiotic learning assemblage, such that envisioned productivity gains from AI can be achieved without loss of human jobs.
Specifically, we are evaluating the following research questions: Q1: How to develop assemblages, such that human-technology partnerships produce a “good fit” for visually based cognition-oriented tasks in radiology? Q2: What level of training should pre-exist in the individual human (radiologist) and independent machine learning model for human-technology partnerships to thrive? Q3: Which aspects and to what extent does an assemblage learning approach lead to reduced errors, improved accuracy, faster turn-around times, reduced fatigue, improved self-efficacy, and resilience?
Zoom: https://stanford.zoom.us/j/93580829522?pwd=ZVAxTCtEdkEzMWxjSEQwdlp0eThlUT09
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.
MIPS Seminar Series: Translational Opportunities in Glycoscience
Carolyn Bertozzi, PhD
Director, ChEM-H
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences
Professor, by courtesy, of Chemical and Systems Biology
Stanford University
Location: Zoom
Webinar URL: . https://stanford.zoom.us/j/94010708043
Dial: US: +1 650 724 9799 or +1 833 302 1536 (Toll Free)
Webinar ID: 940 1070 8043
Passcode: 659236
12:00pm – 12:45pm Seminar & Discussion
RSVP Here
ABSTRACT
Cell surface glycans constitute a rich biomolecular dataset that drives both normal and pathological processes. Their “readers” are glycan-binding receptors that can engage in cell-cell interactions and cell signaling. Our research focuses on mechanistic studies of glycan/receptor biology and applications of this knowledge to new therapeutic strategies. Our recent efforts center on pathogenic glycans in the tumor microenvironment and new therapeutic modalities based on the concept of targeted degradation.
ABOUT
Carolyn Bertozzi is the Baker Family Director of Stanford ChEM-H and the Anne T. and Robert M. Bass Professor of Humanities and Sciences in the Department of Chemistry at Stanford University. She is also an Investigator of the Howard Hughes Medical Institute. Her research focuses on profiling changes in cell surface glycosylation associated with cancer, inflammation and infection, and exploiting this information for development of diagnostic and therapeutic approaches, most recently in the area of immuno-oncology. She is an elected member of the National Academy of Medicine, the National Academy of Sciences, and the American Academy of Arts and Sciences. She also has been awarded the Lemelson-MIT Prize, a MacArthur Foundation Fellowship, the Chemistry for the Future Solvay Prize, among many others.
Hosted by: Katherine Ferrara, PhD
Sponsored by: Molecular Imaging Program at Stanford & the Department of Radiology
MIPS Seminar Series: “Convergent, translational research to improve human health”
Joseph M. DeSimone, PhD
Sanjiv Sam Gambhir Professor of Translational Medicine and Chemical Engineering
Departments of Radiology and Chemical Engineering
Graduate School of Business (by Courtesy)
Stanford University
Location: Zoom
Webinar URL: https://stanford.zoom.us/s/98460805010
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 984 6080 5010
Passcode: 809226
12:00pm – 12:45pm Seminar & Discussion
RSVP Here
ABSTRACT
In many ways, manufacturing processes define what’s possible in society. Central to our interests in the DeSimone laboratory are opportunities to make things using cutting-edge fabrication technologies that can improve human health. This lecture will describe advances in nano- / micro-fabrication and 3D printing technologies that we have made and employed toward this end. Using novel perfluoropolyether materials synthesized in our lab in 2004, we invented the Particle Replication in Non-wetting Templates (PRINT) technology, a high-resolution imprint lithography-based process to fabricate nano- and micro-particles with precise and independent control over particle parameters (e.g. size, shape, modulus, composition, charge, surface chemistry). PRINT brought the precision and uniformity associated with computer industry manufacturing technologies to medicine, resulting in the launch of Liquidia Technologies (NASDAQ: LQDA) and opening new research paths, including to elucidate the influence of specific particle parameters in biological systems (Proc. Natl. Acad. Sci. USA 2008), and to reveal insights to inform the design of vaccines (J. Control. Release 2018), targeted therapeutics (Nano Letters 2015), and even synthetic blood (PNAS 2011). In 2015, we reported the invention of the Continuous Liquid Interface Production (CLIP) 3D printing technology (Science 2015), which overcame major fundamental limitations in polymer 3D printing—slowness, a very limited range of materials, and an inability to create parts with the mechanical and thermal properties needed for widespread, durable utility. By rethinking the physics and chemistry of 3D printing, we created CLIP to eliminate layer-by-layer fabrication altogether. A rapid, continuous process, CLIP generates production-grade parts and is now transforming how products are manufactured in industries including automotive, footwear, and medicine. For example, to help address shortages, CLIP recently enabled a new nasopharyngeal swab for COVID-19 diagnostic testing to go from concept to market in just 20 days, followed by a 400-patient clinical trial at Stanford. Academic laboratories are also using CLIP to pursue new medical device possibilities, including geometrically complex IVRs to optimize drug delivery and implantable chemotherapy absorbers to limit toxic side effects. Vast opportunities exist to use CLIP to pursue next-generation medical devices and prostheses. Moreover, CLIP can improve current approaches; for example, the fabrication of an iontophoretic device we invented several years ago (Sci. Transl. Med. 2015) to drive chemotherapeutics directly into hard-to-reach solid tumors is now being optimized for clinical trials with CLIP. New design opportunities also exist in early detection, for example to improve specimen collection, device performance (e.g. microfluidics, cell sorting, supporting growth and studies with human organoids), and imaging (e.g. PET detectors, ultrasound transducers). Here at Stanford, we are pursuing new 3D printing advances, including software treatment planning for digital therapeutic devices in pediatric medicine, as well as the design of a high-resolution printer capable of single-digit micron resolution to advance microneedle designs as a potent delivery platform for vaccines. The impact of our work on human health ultimately relies on our ability to enable a convergent research program to take shape that allows for new connections to be made among traditionally disparate disciplines and concepts, and to ensure that we maintain a consistent focus on the translational potential of our discoveries and advances.
ABOUT
Joseph M. DeSimone is the Sanjiv Sam Gambhir Professor of Translational Medicine and Chemical Engineering at Stanford University. He holds appointments in the Departments of Radiology and Chemical Engineering with a courtesy appointment in Stanford’s Graduate School of Business. Previously, DeSimone was a professor of chemistry at the University of North Carolina at Chapel Hill and of chemical engineering at North Carolina State University. He is also Co-founder, Board Chair, and former CEO (2014 – 2019) of the additive manufacturing company, Carbon.
DeSimone is responsible for numerous breakthroughs in his career in areas including green chemistry, medical devices, nanomedicine, and 3D printing, also co-founding several companies based on his research. He has published over 350 scientific articles and is a named inventor on over 200 issued patents. Additionally, he has mentored 80 students through Ph.D. completion in his career, half of whom are women and members of underrepresented groups in STEM. In 2016 DeSimone was recognized by President Barack Obama with the National Medal of Technology and Innovation, the highest U.S. honor for achievement and leadership in advancing technological progress. He is also one of only 25 individuals elected to all three branches of the U.S. National Academies (Sciences, Medicine, Engineering). DeSimone received his B.S. in Chemistry in 1986 from Ursinus College and his Ph.D. in Chemistry in 1990 from Virginia Tech.
Hosted by: Katherine Ferrara, PhD
Sponsored by: Molecular Imaging Program at Stanford & the Department of Radiology
MIPS Seminar Series: “Circulating Tumor DNA Biomarkers for Therapy Monitoring and Early Detection”
Shan X. Wang, PhD
Leland T. Edwards Professor in the School of Engineering
Professor of Materials Science & Engineering, jointly of Electrical Engineering, and by courtesy of Radiology (Stanford School of Medicine)
Director, Stanford Center for Magnetic Nanotechnology
Stanford University
Location: Zoom
Webinar URL: https://stanford.zoom.us/s/93202777468
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 932 0277 7468
Passcode: 851144
12:00pm – 12:45pm Seminar & Discussion
RSVP Here
ABSTRACT
Inspired by Dr Sam Gambhir, MIPS, Canary Center, and Stanford CCNE have pursued in vivo imaging and in vitro diagnostic tests for cancer therapeutic response or early detection, respectively, over the last 15+ years. Here I present two successful examples based on circulating tumor DNA (ctDNA) targets in plasma, complementary to imaging modalities such as CT and Ultrasound.
We have developed a simple yet highly sensitive assay for the detection of actionable mutational targets such as Epidermal Growth Factor Receptor (EGFR) and Kirsten rat sarcoma oncogene (KRAS) mutations in the plasma ctDNA from non-small cell lung cancer (NSCLC) patients using giant magnetoresistive (GMR) nanosensors. Our assay achieves lower limits of detection compared to standard fluorescent PCR based assays, and comparable performance to digital PCR methods. In 30 patients with metastatic disease and known EGFR mutation status at diagnosis, our assay achieved 87.5% sensitivity for Exon19 deletion and 90% sensitivity for L858R mutation while retaining 100% specificity; additionally, our assay detected secondary T790M mutation resistance with 96.3% specificity while retaining 100% sensitivity. We re-sampled 13 patients undergoing tyrosine kinase inhibitor (TKI) therapy 2 weeks after initiation to assess response, our GMR assay was 100% accurate in correlation with longitudinal clinical outcome, and the responders identified by the GMR assay had significantly improved progression free survival (PFS) compared to the non-responders. The GMR assay is low cost, rapid, and portable, making it ideal for detecting actionable mutations at diagnosis and non-invasively monitoring treatment response in the clinic.
On another front, we have also developed a highly sensitive and multiplexed assay for the detection of methylated ctDNA targets in plasma samples. Current diagnostic tests for liver cancer in at-risk patients are cumbersome, costly and inaccurate, resulting in a need for accurate blood-based tests. By devising a Layered Analysis of Methylated Biomarkers (LAMB) from the relevant big data, we have discovered a set of DNA targets in the blood that accurately detects liver cancer in these at-risk patients. This set of methylated targets was found by analyzing the genetic information of 3411 liver cancer patients and 1722 healthy people. Our results could lead to clinical adoption of liquid biopsy tests for liver cancer surveillance in high-risk populations and the development of blood tests for other cancers.
ABOUT
Prof. Wang directs the Center for Magnetic Nanotechnology and is a leading expert in biosensors, information storage and spintronics. His research and inventions span across a variety of areas including magnetic biochips, in vitro diagnostics, cancer biomarkers, magnetic nanoparticles, magnetic sensors, magnetoresistive random access memory, and magnetic integrated inductors. He has over 300 publications, and holds 65 issued or pending patents in these and interdisciplinary areas. He was named an inaugural Fred Terman Fellow, and was elected a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of American Physical Society (APS) for his seminal contributions to magnetic materials and nanosensors. His team won the Grand Challenge Exploration Award from Gates Foundation (2010), the XCHALLENGE Distinguished Award (2014), and the Bold Epic Innovator Award from the XPRIZE Foundation (2017).
Dr. Wang cofounded three high-tech startups in Silicon Valley, including MagArray, Inc. and Flux Biosciences, Inc. In 2018 MagArray launched a first of its kind lung cancer early diagnostic assay based on protein cancer biomarkers and support vector machine (SVM). In 2019, Flux Biosciences launched a human trial to offer at-home testing of fertility based on hormones and magneto-nanosensors. Through his participation in the Center for Cancer Nanotechnology Excellence (as co-PI of the CCNE) and the Joint University Microelectronics Program (JUMP), he is actively engaged in the transformative research of healthcare and is developing emerging memories for energy efficient computing.
Hosted by: Katherine Ferrara, PhD
Sponsored by: Molecular Imaging Program at Stanford & the Department of Radiology