The monthly NCI Imaging Informatics Webinar is organized by the Center for Biomedical Informatics and Information Technology (CBIIT) and the Cancer Imaging Program (CIP). It occurs on the first Monday of every month from 1:00 pm to 2:00 pm Eastern Time and features scientific presentations and project updates.
Join the Google Group for up-to-date information
|Meeting number (access code)||732 377 553|
|Join by phone|
1-650-479-3207 Call-in toll number (US/Canada)
Agenda of October 3, 2022 meeting
Justin Kirby (FNLCR)
Ulli Wagner (FNLCR)
|July 4, 2022||Independence Day - no webinar|
|August 1, 2022||Summer break - no webinar|
|September 5, 2022||Labor Day - no webinar|
|October 3, 2022|
|November 7, 2022|
|December 5, 2022|
Presentations and Recordings from Previous Calls
Presentations can be found at SlideShare
Bridging the gap between prostate radiology and pathology through machine learning
Mirabela Rusu, PhD, Stanford University
The subtle difference in MRI appearance of prostate cancer and benign prostate tissue renders the interpretation of prostate MRI challenging, causing many false positives, false negatives, and wide variations in interpretation. My laboratory focuses on improving the interpretation of prostate MRI by developing deep learning models that automatically localize indolent and aggressive prostate cancers on MRI scans. The novelty of our methods comes from using whole-mount pathology images to label MRI images and to create pathomic MRI biomarkers of aggressive and indolent cancers. Our approach achieved an area under the receiver operator characteristics curve of 0.93 evaluated on a per-lesion basis and outperformed existing deep learning models. In patients outside our training cohorts, such predictive models will outline the extent of cancer on radiology images in the absence of pathology images, thus helping guide the prostate biopsy and local treatment.
The talk will focus on discussing recent contributions from my lab on registering whole-mount pathology images with MRI, training deep learning models to extract pathomic MRI biomarkers and using them in training deep learning models to detect and distinguish indolent and aggressive prostate cancers on MRI, and showing the benefits of using labels from pathology in training deep learning models to distinguish indolent from aggressive prostate cancer on MRI.
|February 7, 2022|
|December 6, 2021|
Precision Medicine Approach to Breast Cancer Detection and Diagnosis
(Martin Yaffe, PhD, Sunnybrook Research Institute and The University of Toronto)
Dr. Yaffe will describe a multi-platform approach under investigation in his lab to improve the effectiveness of breast cancer detection and diagnosis. We are developing radiomic tools to guide the stratification of women for breast cancer screening that will be more accurate and efficient in detection than the “one size fits all” use of mammography whose accuracy suffers, particularly in dense breasts. We employ microsimulation modeling to guide that work. We are also exploring the integration of radiomic information from in vivo medical images with histopathology, single-cell multiplex biomarker analysis, and targeted molecular sequencing to better characterize breast and other cancers and their immune environment and to explore their spatial heterogeneity.
|November 1, 2021|
NCI CCR Artificial Intelligence Resource: Recent AI Applications in Cancer Imaging
Artificial Intelligence (AI) is becoming important for cancer research but is difficult to access for most labs. In 2020, the NCI Center for Cancer Research (CCR) created a new AI Resource (AI) to benefit researchers in the CCR. The group focuses on translational computer vision approaches to analyzing medical images, such as radiologic, digital pathology, video/endoscopy and optical imaging, among others. Examples of potential projects include developing better screening, detection methods or predictive markers, or improving procedures among many others.
With experts in pathology, medical imaging, and machine learning, AIR has taken on a diverse portfolio of research projects in their first year. In this seminar, senior members of the group will discuss its formation, collaboration experience, recent progress, and challenges for deploying developed models back to the hands of researchers across varying domains in NCI.
The presentation contained unpublished data.
As projects are finished and code is released, the AIR team will update the webpage.
You can also email the AIR team with any questions you might have (email@example.com)
|October 4, 2021|
Our team at the Harvard Medical School’s Lab of Systems Pharmacology has generated reagents, workflows, and data analysis/visualization approaches for multiplexed tissue imaging. We developed tissue-based cyclic immunofluorescence (t-CyCIF) for subcellular imaging of formalin-fixed and paraffin-embedded (FFPE) and frozen tissues across 20-60 different proteins markers from a single tissue section. To support the use of multiplexed tissue imaging in the NCI Human Tumor Atlas Network (HTAN), we have developed algorithms and workflows to analyze these complex images, digital docents for their narrated viewing, and reporting standards for public data sharing. The information from these imaging methods complement data acquired by microregion spatial transcriptomics technologies. We have also used high-resolution imaging of tissues to identify functional interactions (e.g., immune synapses) in cancer tissues and have created multiplexed 3D cancer atlases to more completely characterize the architecture of the tumor-immune landscape in colon cancer and in melanomas, from pre-cancer lesions through metastasis.
The National Cancer Institute (NCI) Cancer Research DataCommons (CRDC) aims to establish a national cloud-based data science infrastructure. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with non-imaging data. We achieve this goal by co-locating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020. In this presentation, we will give a brief overview of the capabilities of the production release of the IDC platform, and discuss the next steps for the development.
|June 7, 2021|
|May 3, 2021|
Establishing next generation dynamic susceptibility contrast MRI based biomarkers for neuro-oncologic applications
Dr. Chad Quarles, PhD
Dynamic susceptibility contrast (DSC) MRI is one of the most widely used physiologic imaging techniques in neuro-oncology, enabling the differentiation of glioma grades, identification of tumor components in non-enhancing glioma, reliable detection of recurrence, and early detection of therapy response. This presentation will highlight how an improved understanding of the biophysics of the contrast mechanisms underlying DSC-MRI enabled the recent standardization of acquisition protocols for multi-site clinical trials, is leading to the field’s first benchmark for software validation, and is informing the development of advanced pulse sequences and analysis strategies tailored to specific clinical challenges faced in the management of brain cancer patients.
|April 5, 2021|
|March 1, 2021|
Cancer is nearly always a surgically treated disease. Almost 80% of patients with early stage solid tumors undergo surgery at some point within their treatment course. A major gap in quality of care remains the high rate of tumor-positive margins in head and neck cancer (HNC) following surgical resections. Positive margin rates are directly correlated with lower survival but have remained unchanged at 25% for the last two decades! Primary factors that have impeded improving the rate of tumor-positive margins include subjective surgeon assessment as well as the limited amount of the tissue that can be sampled for intraoperative frozen-section analysis. We have demonstrated that use of intraoperative molecular imaging (IMI) can objectively identify the area on the tumor specimen most likely to contain a tumor-positive margin (“sentinel margin”). In a prospective evaluation, a fluorescently-labeled tumor-specific contrast agent is administered intravenously to the patient several days prior to surgery. After the surgical resection, the specimen is evaluated with IMI, in which near infrared imaging is used to identify the location of the sentinel margin on the surgical specimen. This evaluation is compared to subjective assessments of the deep tumor margin by palpation, considered the standard of care. It is expected that IMI imaging will be more accurate in identifying the sentinel margin, and will shorten the time to histological diagnosis while maintaining tissue orientation and high histological image quality. The translation of these new technologies has the potential to double the five-year survival rate of patients with HNC as well offer the potential to improve care for other cancer types as well.
|February 1, 2021|
Orchestration of distributed image archives
|December 7, 2020||NIAID TB Bioportal |
The NIAID Office of Cyber Infrastructure and Computational Biology will share their TB Portals Program, including their efforts at imaging data collection, data dissemination, tool development, and data science research. https://tbportals.niaid.nih.gov
|November 16, 2020|
|November 2, 2020|
|October 5, 2020|
|September 14, 2020|
|July 6, 2020||MP4 file|
|June 1, 2020||MP4 file|
|April 6, 2020||MP4 file|
|January 6, 2020||MP4 file|
|December 2, 2019||Call was canceled due to RSNA|
|November 4, 2019||Call was canceled due to conflicting meetings|
|October 7, 2019||MP4 file|
|September 9, 2019||MP4 file|
|August 5, 2019||MP4 file|
|July 1, 2019|
Joint Session with the CPTAC Special Interest Group