The SAS Virtual Health Artificial Intelligence (AI) Summit on Cancer Research1 was held this year to share best practices, ongoing challenges, and future opportunities for advancing cancer treatment through analytics. Innovations in applying computer vision to medical images and using machine learning (ML) to build predictive models may help clinicians assess therapeutic results more efficiently, thereby enhancing personalized approaches to cancer treatment.
AI is the application of digital devices and computers to enhance human intelligence.2 In this article, we focus on the use of AI to develop ML and deep learning (DL) models. Whereas ML is the subfield of AI using mathematical and statistical approaches to derive models from data, DL is a specific class of ML that leverages complex networks in its learning process (Figure 1).3
Four Key Themes from the Summit
1. Applying Response Evaluation Criteria for Solid Tumors (RECIST 1.1) criteria to solid tumors involves measuring the largest diameter of a tumor, but tumor volume and morphology give a more comprehensive assessment of treatment response,4,5 so there is an opportunity to improve RECIST 1.1 with AI, ML, and DL.6 AI can determine volumetric changes in the three-dimensional morphology of cancers that are not simple spherical or elliptical structures, while eliminating subjectivity and observer variability7-10 and reducing time assessing tumor response. The combination of clinical data features, such as AI-assisted interpretation of test reports and longitudinal patient level data, can train DL and ML models to improve the diagnostic accuracy of radiographic studies.11,12
AI may also be used to give objective histopathological results, as in a recent study of patients with pancreatic cancer. Digitized, segmented images of tumors were used to segment residual tumor burden after