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How Do We Couple Quantitative Imaging and Models of Tumor Response to Improve Multimodality Therapy?

L Marks

V Moiseenko

R Stewart

R Jeraj

L Marks1*, V Moiseenko2*, R Stewart3*, R Jeraj4*, (1) University of North Carolina, Chapel Hill, NC, (2) University of California, San Diego, La Jolla, CA, (3) University of Washington, Seattle, WA, (4) University of Wisconsin, Madison, WI

TU-E-WAB-1 Tuesday 2:00PM - 3:50PM Room: Wabash Ballroom

Multimodal treatments of solid malignancies have grown in prominence as new trials seek to effectively combine radiation therapies and molecularly targeted therapies. However, inter-patient and intra-patient variations in therapeutic response have yielded inconclusive trial outcomes. Quantitative imaging has been suggested as a means of overcoming some of these variations through individualized target definition, dose escalation, and response assessment due to its predictive value for therapy outcomes. Nonetheless, current visualization techniques at limited spatial resolution provide a narrow view of the underlying molecular, cellular and multicellular processes that make up the comprehensive biological response to therapies. Additionally, temporal gaps among longitudinal imaging time points limit dynamic response assessment.

Biological imaging-based and multi-scale modeling of tumor response to combination therapies is a new area of clinical research that seeks to bridge these visualization and temporal gaps. This symposium will summarize recent clinical trials that are guided by quantitative imaging, address challenges of linking molecular imaging to heterogeneous biological properties, enumerate models of target definition and biologically effective response to therapy, and propose integrative methods for imaging-based models of multimodal therapy dynamics. Most importantly, the session will bring together clinicians and scientists alike specializing in imaging and dose-response modeling to partake in a panel discussion around the thematic question: how do we couple quantitative imaging and models of tumor response to improve multimodal therapy?

Learning objectives:

1. Understand the expanding role of quantitative imaging for therapy target definition, dose escalation, and response assessment.
2. Gain an improved understanding of how physical and biological mechanisms contribute to therapy response across relevant time and spatial scales.
3. Discuss strategies to integrate information from clinical trials with imaging and tumor modeling so as to personalize treatments and improve outcome assessment.

Funding Support, Disclosures, and Conflict of Interest: Supported in part by grant CA69579 (LBM)

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