Abstract PO2-03-01: Personalized 3D tumor models drive shared decision-making in early breast cancer
Authors: Collyar Deborah , Biancalana Matthew , Antony Anuja , Liadis Nicole , Palaniappan Jyoti , Seago-Coyle Rebecca 
Published: May 2024

Abstract
Background:
Decision-making for early (Stages 1-3) breast cancer (eBC) care is complex and highly personal, requiring discussions between patients and multiple physicians. Shared decision-making (SDM) is an accepted process that combines input from both patients and physicians, although its overall use is low due to a lack of time and incentives, complicated application techniques, and few communication tools. Reviews show that SDM results in “improved affective-cognitive outcomes” but evidence linking to patient outcomes is sparse. Consequently, patients often receive mixed messages and feel anxious when making treatment decisions.
SimBioSys (SBS) developed the TumorSight(TM) platform for individualized treatment planning, coordinated care, and SDM. The first application on this platform, TumorSight Viz, rapidly builds 3D computational models from standard breast MRI data, and further integrates deep learning, genomic/genetic testing, metabolomics, and clinical and demographic data to tailor these models for each individual diagnosed with eBC. The platform can also transform acquired data (e.g., prone) to surgical position (e.g., supine) to create realistic images for precision surgical planning and tools for better SDM with eBC patients. We are assessing how surgeons and patient advocates (PA) perceive TumorSight, and how it may support better SDM and treatment implementation. Methods:
SBS conducted an exploratory survey with 31 eBC surgeons to assess the utility of TumorSight Viz visualizations across a range of didactic and planning applications and to query preferences for future components like predicted response to chemotherapy and immunotherapy. Questions were developed with input from breast surgeon advisors.
More exploration on TumorSight Viz utility is planned with semi-structured qualitative interviews to assess patient advocates’ perceptions of 3D visualizations, surgical planning, and ability to support decision making. PAs will represent geographic, experience, and race/ethnicity diversity. Questions to elicit patient needs, perception of SBS tools, and quality-of-life are being finalized with academic advisors. These will also be used to develop a future eBC patient survey. Results:
The SBS surgeon survey revealed that 96% of breast surgeons find visualization important to surgical planning, and these depictions were considered to be critical by a third of breast surgeons surveyed. 96% also listed the conversion of prone to supine visualizations as important (45% critical, 35% valuable, 16% nice to have, 0% not important) for surgical planning value. Surgeons also assigned strong utility for:
Graphical 3D rotation to determine tumor shape, size, location, positioning, and landmarks relative to other breast structures – 100%
Ability to instantly provide tumor-to-breast volume determination with high accuracy – 96%
Ability to instantly provide anticipated tumor extirpation volume to breast volume with high accuracy – 96%
Automatic tumor-to-landmark structures measurements (nipple, chest, skin) – 96%
Visualization of tumor in the context of other anatomical structures (e.g., skin, blood vessels, chest, heart) – 87%
Initial PA discussions (n=3) listed the importance of individualized treatment based on evidence instead of hunches; reasons to avoid prophylactic mastectomies; and ways to lower patient anxiety. Conclusions:
Survey-based feedback shows value to eBC surgeons and patients to coordinate care and personalize approaches for optimal outcomes. Computational tools can facilitate patient understanding and patient-tailored treatment. Additional surveys are planned with patients and eBC patient advocacy groups to assess the utility and potential expansion of patient-specific 3D tumor visualizations, both of which will be further discussed.
Citation Format: Deborah Collyar, Matthew Biancalana, Anuja Antony, Nicole Liadis, Jyoti Palaniappan, Rebecca Seago-Coyle. Personalized 3D tumor models drive shared decision-making in early breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-03-01.