IMAGIO: Imaging and Advanced Guidance for Workflow Optimization in Interventional Oncology

Project summary

Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020, or nearly one in six deaths. Lung and liver cancers were among the top three leading causes of cancer death in 2020 with 1.8 million and 830.000 deaths, respectively. On the other hand, soft tissue sarcomas are relatively uncommon cancers diagnosed in about 1% of all adults, but much more common in children and young adults, accounting for 7–10% of paediatric malignancies; they are an important cause of death in the 14–29 years age group.

Interventional Oncology (IO) involves miniaturized instruments (biopsy needles, ablation electrodes, intravascular catheters) and minimally-invasive access, guided by imaging techniques (X-ray, ultrasound, computed tomography, magnetic resonance imaging) – to target cancer with ablative or localized drug delivery strategies. IO can be used as a stand-alone approach, or in combination with the other approaches (‘pillars’) to enhance treatment efficacy.

While cancer survival has significantly improved over time through innovations in each individual pillar, our current understanding of cancer now leads us to an intertwining of pillars and multimodal care pathways: IO is uniquely suited to leverage and enhance the effects of the conventional therapy pillars, while reducing the burden on the healthcare system.

IMAGIO will leverage IO in the clinical setting to improve the cancer survival outcomes, through minimally invasive, efficient, and affordable care pathways for three disease states: liver cancer, lung cancer and sarcoma. In IMAGIO, top innovators in MedTech and Pharma will mature the next-generation IO imaging across the full spectrum, from pre-clinical developments to impact validation in clinical trials. IO and immunotherapy expertise will be leveraged from top MedTech and Pharma partners covering the full value chain of oncological care – to provide cancer patients access to safe, fast, and effective care.

Our contribution

We will contribute to the project by developing AI/ML-based early detection, patient stratification and individualized treatment planning algorithms.

Funding

This research project is funded by the Innovative Health Initiative.

People

Roel Verhoeven

Roel Verhoeven

Erik van der Heijden

Erik van der Heijden

Pulmonologist

Radboudumc

 Tim Govers

Tim Govers

Colin Jacobs

Colin Jacobs

Assistant Professor