• TCIA Update

• Imaging Data Commons Update

• Precision Surgery: Intraoperative molecular imaging to improve margin detection

  Dr. Eben Rosenthal, Professor of Otolaryngology, Head & Neck Surgery and Radiology, Stanford University
  

Abstract:

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. 

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Orchestration of distributed image archives
(Jonas Almeida and Praphulla Bhawsar)

• Summary: Recent advances in the use of HTTP range requests to traverse bioformats*, coupled with a general move to “zero footprint” image informatics solutions, enable the creation of image archives as an exercise in governance. A particular feature of this configuration is that the images do not have to be copied or moved from their primary location. This has two interesting effects: a) the image owner remains in control of its governance, and b) training of AI classifiers can be federated across image sets that are not even shared.

  * Bremer E, Saltz J, Almeida JS. ImageBox 2 – Efficient and rapid access of image tiles from whole-slide images using serverless HTTP range requests. J Pathol Inform 2020;11:29

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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

• Program introduction – Alex Rosenthal
• Demo #1 – Alyssa Long
• Demo #2 – Andrei Gabrielian
• Imaging data science research – Ziv Yaniv & Gabriel Rosenfeld

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• Imaging Data Commons (Andrey Fedorov, Dennis Bontempi)

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• MONAI (Stephen Aylward, Prerna Dogra, Jorge Cardoso)

  • Introduction to MONAI and the MONAI Community - Stephen Aylward

  • MONAI medical deep learning capabilities and roadmap - Jorge Cardoso 

  • MONAI and clinical workflows: Clara and federated learning - Brad Genereaux

• TCIA Update (Justin Kirby)

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• Kheops (Joël Spaltenstein, Osman Ratib)
• TCIA Update (Justin Kirby)

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• Computational Imaging for Precision Medicine: A quest for generalizable AI models  (Satish Viswanath)

• TCIA Update (John Freymann)

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• Distributed Learning of Deep Learning in Medical Imaging (Daniel Rubin)
• MedICI website (Benjamin Bearce)
• TCIA update (John Freymann)

• ACR's AI-LAB (Laura Coombs, Chris Treml)
• TCIA update (Justin Kirby)

• PathPresenter - a web-based digital pathology and image viewer (Rajendra Singh, Matthew Hanna)
• TCIA update (Justin Kirby)

• Medical Segmentation Decathlon: Generalizable 3D Semantic Segmentation (Amber Simpson)
• Imaging Data Commons Update (Todd Pihl)
• TCIA Update (Justin Kirby)

• Data Commons Overview (Todd Pihl)
• The Imaging Data Commons (Andrey Fedorov)
• TCIA Update (Justin Kirby)
• NBIA Update (Scott Gustafson)

• HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides (Andrew Janowczyk)
• RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning (Kenneth Philbrick)
• TCIA Update (Justin Kirby)

• Advanced Methods in Tissue Cytometry (Rupert Ecker)
• Presentation by the 4D Necleome Imaging Working Group (David Grünwald)
• TCIA Update (Justin Kirby)

Joint Session with the CPTAC Special Interest Group

• CPTAC Project Overview (Chris Kinsinger)
• CPTAC Image Data at TCIA (Justin Kirby)
• CPTAC Proteomics Data at the Proteomics Data Commons (R. Rajesh Thangudu)
• CPTAC Genomic Data at the Genomics Data Commons (Ana Robles)
• Using the CPTAC Data Portal (R. Rajesh Thangudu)