Neuro-Oncology, Neurosurgery, Neurovascular
Conditions
Keywords
Hyperspectral imaging, Tissue perfusion, Tissue oxygenation
Brief summary
Brain surgery operations include brain tumour removal and blood vessel procedures. Each year in the UK, approximately 70,500 patients are diagnosed with a brain tumour, 5,000 of whom undergo surgery. Approximately 1,000 patients undergo blood vessel brain surgery. Brain tumour surgery involves removing as much of the tumour as safely as possible. If all tumour is removed, patients have significantly better outcomes and live longer. However, even with the best hands and the most modern technology currently available, it is often not possible to reliably identify tumour during surgery. Moreover, nerves and blood vessels cannot be reliably identified either during surgery. Yet, they need to be preserved to avoid brain damage. Due to this uncertainty and the need to balance risks, tumour is often left behind. Today, close to 30% of brain tumour patients require repeat surgery owing to tumour left behind during their first surgery. Further surgeries are more difficult, pose additional patient risks and lead to increased healthcare costs with often poor patient outcomes. Newly developed camera systems have the potential to enhance the surgeon's vision to reliably identify tumour and healthy brain structures. Hyperspectral imaging (HSI) is one of the most promising of such technologies. Its core ability is to provide very detailed and rich information that is invisible to the human eye. HSI has demonstrated the potential to provide crucial, but currently unavailable, information about tumour and critical brain structures during surgery. However, HSI data is very complex and requires advanced computer-processing for its interpretation. In this project, we will use a HSI imaging system to record data in 81 patient undergoing brain including 63 patients with brain tumours and 18 patients suffering from brain vessel abnormalities. Using this data we will develop key computer-processing features to enable real-time image interpretation.
Interventions
None listed
Sponsors
King's College London
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adult patients aged 18 years and over * Patients with a diagnosis of a brain tumour (any type), AVM or aneurysm who are scheduled for elective surgery * Patients able to provide written informed consent
Exclusion criteria
* Patients under 18 years of age * Patients who have previously had brain surgery
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation of HSI data with histological analysis of the corresponding biopsied pathological tissue | 4-6 months | To correlate HSI data with histological analysis of the corresponding biopsied pathological tissue and to correlate tissue perfusion and tissue oxygenation maps generated from the HSI data with the surgical timeline in patients undergoing neurovascular surgery. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Tissue perfusion and tissue oxygenation | 36 months | Correlation of tissue perfusion and tissue oxygenation maps generated from the HSI data with the surgical timeline in patients undergoing neurovascular surgery |
| Safety of iHSI in Neurosurgery | 36 months | Record of adverse events |
| AI algorithm specificity | 36 months | Specificity of the AI algorithm to determine the histological nature of imaged tissue |
| Qualitative assessment | 36 months | The impact that using the system has on surgical workflow (qualitative assessment) |
Countries
United Kingdom
Contacts
Primary ContactJonathan Shapey
Outcome results
None listed