Abstract

Glioma surgery requires a delicate balance between maximising extent of tumour resection and minimising post-operative neurological deficits to optimise both survival and functional outcomes. Despite advanced peri-operative neuroimaging and intra-operative brain mapping, outcome prediction is not always accurate.

A digital twin is a virtual replica of a physical entity. In healthcare settings, digital twins simulate disease progression and treatment outcomes by integrating multi-modal data with continuous updates. This approach aims to improve the accuracy of patient-specific prognosis and allow clinicians to test their treatment plans on the digital twin. While it is gaining traction in fields like cardiology, its application in neurosurgical oncology is still largely theoretical.

This seminar will explore the potential of digital twin technology in glioma treatment. Current literature on AI-driven brain modelling, patient outcome prediction, and surgical planning will be reviewed, highlighting the need for a more individualized, integrated, and interpretable digital twin system to support personalised neurosurgical decision making for patients with gliomas. Key challenges and opportunities of developing such a system will also be discussed.