SimBioSys, Ricoh 3D Team Up on AI-Powered Breast Cancer Visualization Tool

SimBioSys and Ricoh 3D for Healthcare LLC have reached a strategic, non-exclusive distribution agreement to broaden market access to TumorSight Viz—SimBioSys’ U.S. Food and Drug Administration (FDA)-cleared 3D surgical visualization platform for breast cancer.

TumorSight Viz converts standard breast MRI into intuitive, 3D visualizations that reveal tumor size, location, and spatial orientation relative to surrounding anatomy—supporting more informed and individualized surgical decision-making. TumorSight Viz 1.3 offers industry-leading segmentation accuracy, delivers case results in minutes, and integrates with hospital PACS to streamline planning and coordination.¹

This partnership follows the recent FDA 510(k) clearance of TumorSight Viz 1.3 and reflects a shared commitment to bringing next-generation digital health technologies to market at scale. By combining SimBioSys’ software with Ricoh’s deep expertise in healthcare services, surgical oncology clinician relationships, and nationwide reach, the companies aim to accelerate adoption of AI-powered surgical visualization tools in breast cancer care.

“Breast surgeons are tasked with planning procedures that not only remove tumors effectively but also preserve quality of life for their patients,” said Derek Mathers, director of Clinical Applications, Ricoh 3D for Healthcare. “At Ricoh, we are committed to advancing patient-specific solutions that support physicians in this balance. Through our collaboration with SimBioSys, TumorSight Viz brings new clarity to surgical planning—empowering oncoplastic techniques that expand breast- conserving options and help deliver more consistent outcomes for patients.”

Clinical studies demonstrate strong concordance with radiologist annotations, accurate delineation of tumor size relative to breast volume, and consistent performance across more than 1,600 retrospective cases from more than nine institutions—reinforcing TumorSight Viz’s reliability and readiness for broader adoption.^2,3 Ricoh 3D for Healthcare is bringing TumorSight Viz to market as part of its mission to deliver innovative, patient-specific solutions.

“At SimBioSys, we’re focused on delivering innovation that empowers clinicians to make more precise, personalized surgical decisions,” SimBioSys President/CEO Stacey Stevens stated. “Ricoh’s reach, reputation, and commitment to advancing surgical innovation make them an ideal partner to help bring TumorSight Viz to more clinical teams and patients. This partnership enables us to meet the increasing demand for tools that help improve outcomes, reduce variability, and elevate the surgical experience for both providers and patients.”

SimBioSys is a clinical artificial intelligence company leveraging spatial biophysics to drive precision in breast cancer treatment. By revolutionizing how cancer is understood and treated, SimBioSys is empowering clinicians with greater clarity, consistency, and confidence to deliver care aligned with each patient’s unique needs.

Ricoh 3D for Healthcare LLC is a wholly owned subsidiary of Ricoh USA Inc., focused on delivering FDA-cleared, patient-specific medical devices through centralized and point-of-care manufacturing. The company supports hospitals and health systems with clinical-grade anatomic models, surgical planning tools, and scalable production partnerships. In addition to hospital- based services, Ricoh 3D for Healthcare collaborates with medtech innovators to manufacture and distribute next-generation personalized devices across the United States.

References
¹ SimBioSys data on file.
² Weitz M, Pfeiffer JR, Patel S, Biancalana M, et al. (2024). Performance of an AI-powered visualization software platform for precision surgery in breast cancer patients. npj Breast Cancer. https://doi.org/10.1038/s41523-024- 00696-6.
³ Pekis A, Kannan V, Kaklamanos E, Antony A, Patel S, Earnest T. (2024). Seeing beyond cancer: Multi- institutional validation of object localization and 3D semantic segmentation using deep learning for breast MRI. In: Medical Imaging 2024: Image Processing (Vol. 12927, 129270H). SPIE. https://doi.org/10.1117/12.3009341.