XEOS has completed a €14 million financing round backed by a syndicate of Belgian entrepreneurs investing in healthcare technology. The proceeds will be used to accelerate commercial expansion in Europe and the United States and to strengthen the clinical and health-economic evidence supporting the company’s flagship platform, the AURA 10 PET-CT—the first intraoperative specimen PET-CT system.

Despite major advances in cancer treatment, surgeons often perform tumor resections without immediate confirmation that all malignant tissue has been removed. Final margin status is typically determined days or weeks later through pathology, creating uncertainty that can lead to additional
treatments, repeat surgeries, and increased burden for patients and healthcare systems. XEOS addresses this long-standing challenge by bringing real-time molecular insight directly into the operating room.

The AURA 10 PET-CT enables surgical teams to acquire a high-resolution, three-dimensional molecular image of resected tissue within minutes, while the patient remains under anesthesia. By providing immediate feedback at the point of surgery, the system supports more informed intraoperative decisions and introduces a new paradigm for assessing completeness of resection without disrupting surgical workflow.

The system is currently installed in the United States and seven European countries and has been used in more than 500 patient procedures across oncologic indications. As adoption expands, AURA 10 PET-CT is being integrated into routine surgical workflows to help reduce uncertainty, limit re-interventions, and support more precise and efficient cancer surgery.

“Surgical decisions are often made under intense time pressure and with incomplete information,” said Vincent Keereman, Founder and CEO of XEOS. “This financing marks an important transition for XEOS, from early clinical adoption to building the foundation for scale. Our priority is to expand access to real-time molecular imaging, strengthen the clinical and health-economic evidence base, and support surgical teams with tools that enable more confident, data-driven decisions in the operating room.”

Traditionally, surgeons have relied on visual inspection, tactile feedback, and delayed pathology results to assess whether a tumor has been fully removed. The AURA 10 PET-CT delivers an immediate molecular map of the resected specimen, helping surgical teams to assess whether
surgery was successful without delay and supporting more informed intraoperative judgment.

At AZ Maria Middelaresin Ghent, Belgium, an early adopter of intraoperative specimen imaging, the AURA 10 PET-CT is now used across multiple procedures, most frequently in prostate, breast, and thyroid cancer surgery.

“Surgery is full of high-stakes decisions made with limited information,” said Dr. Filip Ameye, urologist and chief innovation officer at AZ Maria Middelares. “The AURA 10 PET-CT brings immediate clarity directly into the operating room. It fundamentally changes how surgeons assess their work by allowing us to see what was previously invisible.”

Beyond real-time imaging, the AURA 10 PET-CT generates a growing dataset that enables the development of AI-based image analysis tools. XEOS researchers have recently published peer reviewed results demonstrating that deep-learning models can automatically evaluate breast tumor images with accuracy comparable to expert assessment.¹ As clinical adoption expands, the company plans to further develop data-driven tools that support consistency, efficiency, and surgical confidence.

Proceeds from the financing will support three strategic priorities: expanding commercial presence in Europe and the United States; building a network of reference centers to strengthen clinical collaboration and visibility; and expanding clinical and health-economic evidence to support broader adoption and reimbursement.

Reference
¹ Maris L. et al. Supporting intraoperative margin assessment using deep learning for automatic tumour segmentation in breast lumpectomy micro-PET-CT. NPJ Breast Cancer. 2025 Aug 9;11(1):88.