The Knowledge Hub for Medical Imaging Professionals: Transforming Radiology with Structured Reporting, Data-Driven Approaches and Multicentric Research

Access breakthrough research, innovative case studies and collaborative projects advancing radiology worldwide. Dive into our activities and product updates, and learn who we are as a company and as a team.

Two female medical researchers looking at computer screen

RACOON-RESCUE is Advancing Pediatric Non-Hodgkin Lymphoma Care

Pediatric Non-Hodgkin Lymphoma (NHL) is the fourth most common tumor in children and adolescents, yet its radiological methods lack standardization, leaving room for significant improvement.

The RACOON-RESCUE project addresses this gap by uniting pediatric radiologists and oncologists from all German university hospitals to systematically analyze imaging data, aiming to enhance diagnosis, treatment response assessment, and follow-up care for pediatric NHL.

The findings from RACOON-RESCUE aim to support the development of optimized and personalized diagnostic and risk-adapted therapeutic approaches.

Read the full interview with the project leads, Prof. Dr. Diane Renz and Prof. Dr. Wilhelm Wößmann, to learn more about the initiative.

Related Resources

Related Resources

Medical personnel looking at a technical device to discuss diagnostic guidelines

2,237 Patients, 11 Hospitals, four HCC Criteria: A Comparison Study

A recent study, conducted across 11 South Korean hospitals, compared the diagnostic performance of four hepatocellular carcinoma (HCC) diagnostic…

mint Lesion screenshot with HCC diagnosis according to APASL, AASLD, LI-RADS, KLCA-NCC, and EASL guidelines

Multicentric Study: Comparison of Diagnostic Guidelines for Hepatocellular Carcinoma

Recent advancements in MRI techniques and tumor biology have led to updated hepatocellular carcinoma (HCC) diagnostic guidelines from various liver…

Radiologist using for medical image analysis

Advancing Real-World Federated Learning in Radiology

Federated Learning (FL) enables collaborative model training without data centralization – a crucial aspect for radiological image analysis where…