Vincent Reid, MD, FACS, Chairman of Surgery and Medical Director at the Hall Perrine Cancer Center, Mercy Cedar Rapids, to share clinical experience and real-world case studies using TumorSight™ Viz
CHICAGO and EXTON, Pa., Dec. 2, 2025 /PRNewswire/ -- SimBioSys®, an AI-driven precision medicine company revolutionizing how cancer is understood and treated, and Ricoh 3D for Healthcare, LLC, a wholly owned subsidiary of Ricoh USA, Inc. focused on delivering FDA-cleared, patient-specific medical devices through centralized and point-of-care manufacturing, today announced an upcoming educational webinar highlighting real-world clinical use of TumorSight™ Viz, the FDA-cleared 3D surgical visualization platform transforming how surgeons evaluate breast-conserving options and engage patients in their care. The live virtual event, "AI-Powered 3D Visualization for Smarter Breast Surgery," will be held on Wednesday, December 10, 2025 at 7:00 p.m. ET / 4:00 p.m. PT. Attendees may register here, or by visiting Ricoh 3D for Healthcare in Booth #4047 at the Radiological Society of North America (RSNA) Annual Meeting in Chicago this week.
The webinar will feature Vincent Reid, MD, FACS, Chairman of Surgery and Medical Director of the Hall Perrine Cancer Center at Mercy Cedar Rapids, who will share his experience incorporating TumorSight Viz into surgical planning, including case examples recently featured by ABC News Digital and Good Morning America. Dr. Reid will be joined by Lexi Gormley, PhD, Senior Clinical Applications Specialist at Ricoh 3D for Healthcare, with moderation by Barry Rosen, MD, FACS, Chief Medical Officer of SimBioSys.
"With tens of thousands of women undergoing breast-conserving surgery each year in the United States, surgeons are seeking new tools that bring greater clarity, confidence, and personalization to the planning process," said Stacey Stevens, President and CEO of SimBioSys. "TumorSight Viz provides surgeons with an entirely new way to understand tumor size, shape, and spatial relationships within the breast—insight that is difficult to achieve with traditional imaging alone. We are pleased to partner with Ricoh to bring this educational opportunity to clinicians nationwide and to highlight how advanced, AI-powered 3D visualization can support more precise and informed surgical decisions."
TumorSight Viz converts standard breast MRI into intuitive, patient-specific 3D models that reveal tumor size, location, and proximity to surrounding tissues. The technology provides surgeons with a level of anatomical context that supports a deeper understanding of what is surgically feasible and enhances communication with patients about their treatment options. The platform, which recently received its third FDA clearance, has been validated across more than 1,600 retrospective cases from more than nine institutions.1,2
"TumorSight Viz brings a new degree of clarity to breast-conserving and oncoplastic surgical planning," said Vincent Reid, MD, FACS, Mercy Cedar Rapids. "Having a precise, intuitive view of the tumor and its relationship to the surrounding breast anatomy allows us to assess feasibility more clearly and consistently. It enhances the planning process and supports more meaningful discussions with patients about their options."
"At Ricoh, we are committed to equipping clinicians with technologies that strengthen surgical precision and elevate the standard of care," said Lexi Gormley, PhD, Senior Clinical Applications Specialist, Ricoh 3D for Healthcare. "This webinar offers breast surgeons a firsthand look at how TumorSight Viz fits into everyday practice—how it informs decision-making, supports oncoplastic planning, and enhances the overall patient experience. We're excited to partner with SimBioSys to bring these insights directly to the clinical community."
The upcoming webinar builds on the companies' recently announced strategic alliance aimed at expanding market access to TumorSight Viz nationwide. The program also follows strong momentum for SimBioSys, including national recognition of its technology by ABC News Digital and Good Morning America, where real-world use at Mercy Cedar Rapids was highlighted as part of Breast Cancer Awareness Month programming.
About SimBioSys
SimBioSys is an AI-driven precision medicine company revolutionizing how cancer is understood and treated. By converting standard medical imaging into AI-powered, decision-driving intelligence, SimBioSys empowers clinicians with clear, contextual, and actionable insights to individualize care. Its FDA-cleared TumorSight™ Viz platform and in-development TumorSight™ Risk solution combine AI and spatial biophysics to deliver greater confidence, precision, and personalization in cancer treatment. For more information, visit www.simbiosys.com.
About RICOH 3D for Healthcare
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. For more information, please visit ricoh-usa.com/3d-for-healthcare.
Contact
SimBioSys, Inc.
media@simbiosys.com
Ricoh USA, Inc.
mediarelations@ricoh-usa.com
Porter Novelli for Ricoh USA, Inc.
ricohna@porternovelli.com
References:
- 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
- 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
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SOURCE SimBioSys
