• Multiplexed Tissue Imaging to Study Cancer
  (Sandro Santagata, MD PhD)

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.

NCI CCR Artificial Intelligence Resource: Recent AI Applications in Cancer Imaging

Presenters:
G. Thomas Brown, MD PhD, Staff Clinician, NCI/CCR
Stephanie Harmon, PhD, Staff Scientist, NCI/CCR
Nathan Lay, PhD, Staff Scientist, NCI/CCR

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.

• IDC Update
  (Ulli Wagner)

• Imaging Data Commons Production Release Update
  (Andrey Fedorov, PhD)

• TCIA Update
  (Justin Kirby, John Freymann)

• Announcements