Accelerating Early Lung Cancer Detection with Autonomous AI

Radboud University Medical Center (RUMC) is joining forces with ScreenPoint Medical and Thirona to develop generic AI software for the early diagnosis of breast and lung cancer. The software is designed to be used autonomously, improving and accelerating image interpretation without compromising transparency and control (the physician remains the decision-maker). This initiative addresses the growing pressure on physicians to assess complex patient data quickly, a task made more difficult by the fact that image interpretation especially for early-stage cancer often error-prone and labour intensive.

Project highlights

  • Project across three years from 2025 to 2028 with around 300 patients dataset analysed  
  • The lung cancer optimal biopsy routes are expected to be calculated automatically, aiming to save the time of physicians around 20 mins.  
  • The project aims to increase the diagnostic yield for lung cancer (through biopsies) with 20% on average 

Contribution by Thirona

Thirona’s primary objective is to automate bronchoscope route planning to assist physicians in both biopsy preparation and live navigation. This is achieved through the following process:

  • Algorithm Development: Thirona will first develop and improve segmentation algorithms for preoperative and intraoperative images.
  • Image Registration: Registration algorithms will be developed and refined to create a spatial link between images taken by different modalities and at different points in time.
  • Route Generation: By combining these algorithms, the most optimal navigation route to the airway of peripheral lesions will be automatically generated.
  • Visualization & Guidance: Finally, a 3D view of these routes is created to simulate the bronchoscope’s position. During the biopsy, these routes are displayed in real-time and projected onto intra-operative images, providing the physician with critical guidance to successfully reach the target nodule.

Relevant Publications

November 2025

Quality of CT based airway segmentation software for navigation bronchoscopy

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