Symposium VIII: Artificial Intelligence in Tumor Imaging: Needs, Opportunities & Challenges

Goals and Objectives

Five years ago the CEO Roundtable on Cancer's Project Data Sphere® (PDS), in partnership with the FDA, founded a series of symposia devoted to areas fertile for fresh attention in the realm of cancer clinical trials. Fostered by Rick Pazdur, Rob Califf, Scott Gottlieb and now Ned Sharpless, this series has consistently elevated the conversation, often deriving major new thrusts from academia, industry as well as from our FDA.

The primary mission of FDA-PDS Symposium VIII is to discuss the development of machine learning algorithms, based on computerized tomography (CT) images, to improve the efficiency and consistency of CT tumor measurements and to augment/eliminate the adjudication of CT readings. CT scans offer a consistent methodology of use across research institutions to develop a common AI solution for big data, applicable for 90% of solid tumors.

Hosted by Stanford University School of Medicine, the Symposium will focus on PDS' Images and Algorithms Program devoted to machine learning image-recognition algorithms. These algorithms may be used for continuous and categorical measurement of cancer tumor dynamics.

Date and Location

Monday, November 11, 2019
Stanford University School of Medicine

Post-Event Summary

Video List
Executive Summary