Brain and central nervous system cancer was the 12th most common of cancer-related mortality worldwide in 2020 affecting both sexes and all age groups. Notably it was the 2nd leading cause of cancer mortality in young populations (<34 years). Neurosurgery remains one of the primary treatment modalities for brain tumours, where surgeons aim to achieve a gross total resection of the tumour with the aid of neuronavigation, intraoperative ultrasound, and/or fluorescence guided techniques. Despite current advances, neurosurgeons continue to face several challenges during brain tumour surgeries, such as:
i) the difficulty in differentiating tumour margins, since brain tumours (especially gliomas) diffusely infiltrate the surrounding brain tissue;
ii) the brain-shift phenomenon caused by the movement of the brain after performing the craniotomy and starting the resection that affects the accuracy of neuronavigation;
iii) long waiting times for intraoperative pathology consultation that can take up to 45 min;
iv) the lack of tools to ensure complete low-grade tumour resection.
To address these challenges, STRATUM aims to develop a 3D decision support tool for brain surgery guidance and diagnostics integrating augmented reality and multimodal data processing powered by artificial intelligence (AI) algorithms. This tool will function as a point-of-care computing system and will be developed using a co-creation methodology that actively involves end-users and other stakeholders.
STRATUM will pursue the following objectives:
1) To foster advances in personalized medicine based on multimodal data (including the emerging hyperspectral imaging modality) and AI.
2) To increase the intraoperative diagnostic accuracy of brain tumours, improving surgical outcomes and patients’ quality of life.
3) To reduce surgery time with respect to current neurosurgical operation durations.
4) To improve current cost- and energy-efficiency of neurosurgical workflows.
5) To demonstrate the prototype in a two-year clinical study in 3 clinical sites, including an early health technology assessment.
6) To prepare the preliminary business plan and the TRL9 roadmap after the project ending.
An optimized integration and processing of available and new emerging data sources would aid surgeons in timely efficient and correct decision-making in tissue removal. This would maximize the degree of resection while simultaneously minimize the risk of neurological deficits. Moreover, time efficient surgical procedures not only benefit the patients directly by minimizing anaesthesia time and risks of e.g. postoperative infections, but also indirectly by optimizing resources of the health care system.
