3D Decision Support Tool for Brain Tumour Surgery Development and Validation: Observational Study

CONTEXT OF EUROPEAN STRATUM PROJECT

Integrated digital diagnostics can support complex surgeries in many anatomies where brain tumour surgery is one of the most complex cases. Neurosurgeons face several challenges during brain tumour surgeries, such as critical tissue and brain tumour margins differentiation or the interpretation of large amount of data available provided by several independent devices.

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. The STRATUM tool will include hyperspectral (HS) imaging (HSI) as an emerging imaging modality in the medical field to enhance intraoperative guidance and diagnosis during the neurosurgical procedures.9 Previous works from several members of the STRATUM consortium in different research projects (HELICoiD, ITHaCA, and NEMESIS-3D-CM) have demonstrated, as a proof-of-concept, that this technology is suitable for the intraoperative identification and delineation of brain tumours in real-time. Additionally, the tool is expected to provide a real-time deformation of the magnetic resonance imaging (MRI) within the exposed brain surface for brain-shift compensation during surgery. This will be performed by using advanced mathematical models in combination with the intraoperative multimodal data (HSI, depth information and standard surgical microscope imaging) captured by the STRATUM tool.

The system will be developed and clinically evaluated in three main stages. In Stage 1, a customized multimodal data acquisition system was developed to be used in the observational study of Stage 2 (STRATUM-OS), which will focused on the multimodal data imaging collection to support the development and technical validation of the STRATUM tool. In Stage 3 the tool will undergo clinical validation through a subsequent, non-randomized, historically controlled, clinical trial (STRATUM-NRCCT). The historic control in STRATUM-NRCCT will be the subjects recruited in STRATUM-OS.

Overall, the STRATUM project aims to:

i) optimize the integration and processing of existing and emerging data sources, facilitating timely, efficient and accurate surgical decision-making; ii) maximize tumour resection while minimizing the risk of neurological deficits; iii) reduce anaesthesia duration and related risks; iv) decrease waste associated with repeated pathology analysis; and v) optimize healthcare resource utilization.

STRATUM OBSERVATIONAL STUDY

STRATUM-OS is an international multicentre, prospective, open, observational cohort study, with a follow-up duration of 6 months, in which the data generated from brain tumour surgeries, including a wide range of intra-axial tumour types, will be collected to meet the objectives of the study. In STRATUM-OS patients will receive standard care as per established clinical protocols, with no modification to their treatment. However, patients will be asked to grant access to their clinical information, complete questionnaires, and provide relevant pre, intra and postoperative information related to the surgical intervention. STRATUM-OS is planned for a duration of 28 months divided in: i) a pre-recruitment period of 2 months for the installation of and surgeon training on the acquisition system, ii) a recruitment period of 20 months, and iii) follow-up period of 6 months, including one month for the integration and technical validation of the fully-working STRATUM tool. We anticipate that 320 consecutive patients can be recruited during this study in the 3 clinical sites. The protocol has been drafted in accordance with the Standardised Protocol Items: Recommendations for Observational Studies (SPIROS) statement

The general objective of STRATUM-OS is to collect the necessary data from a cohort of patients affected by intra-axial brain tumours with the standard surgical procedure established in current clinical protocols. STRATUM-OS will pursue the following main objectives:

1. To collect pre-stored and in-situ multimodal data for the development of an intraoperative 3D decision support tool for brain surgery guidance and diagnostics in real-time leveraging AI-based multimodal data processing (STRATUM tool). 2. To technically validate the STRATUM tool, aiming for (1) the intraoperative distinction between tumour and non-tumour areas in the exposed brain surface and (2) the identification of contrast-enhancing tumour (CET) or non-contrast-enhancing tumour (nCET/FLAIR-positive) regions in MRI, through AI-driven processing. 3. To compile a historical control group dataset including patient clinical data, health outcomes, surgical and tumour characteristics, and hospital resource utilization and costs. This dataset will be used in the subsequent non-randomized controlled clinical trial (STRATUM-NRCCT), to assess the safety, effectiveness and cost-effectiveness of the STRATUM tool in brain tumours surgery.

SETTING AND RECRUITMENT

Adult participants (≥ 18 years) with an intra-axial brain tumour will be eligible for inclusion. Recruitment will follow a consecutive enrolment process, selecting subjects who meet all the inclusion criteria and none of the exclusion criteria at the 3 participating clinical institutions: Hospital Universitario de Gran Canaria Doctor Negrín (Las Palmas de Gran Canaria, Spain), Karolinska University Hospital (Solna, Sweden) and Hospital Universitario 12 de Octubre (Madrid, Spain). Patients will be invited to participate and will be required to sign a written, informed consent form prior to inclusion in the study. They will continue to receive care at their originally assigned medical centre, with no patient transfers between institutions. Members of the research team at each hospital site will introduce the study to subjects who will receive written information describing the study. Researchers will discuss the study details with participants ensuring they have a thorough understanding before making a decision. Participants will have the opportunity to engage in an informed discussion with their physician before consenting. Written informed consent will be obtained from participants or, when applicable, from their designed tutor or legal representatives.

DATA COLLECTION

The data collection procedure will include data extracted from the EHR of the patient, self-reported questionnaires, information collected from the different professionals involved in the neurosurgical workflow, recorded through an electronic Case Report Form (eCRF), and data collected intraoperatively using the STRATUM acquisition system along with detailed information about the surgery (using the eCRF). The STRATUM eCRF is built on the REDCap (Research Electronic Data Capture) platform and securely stored in an anonymized and standardized format within a secure repository at the Institute for Applied Microelectronics (IUMA) of the University of Las Palmas de Gran Canaria (ULPGC). All patient data will be assigned by a unique coded ID \[identification\] number linked to the subject to ensure pseudo-anonymization. Only the local clinical team will be aware of each participant's identity. A locally and securely managed document will link each study ID with the corresponding participant. The data collection procedure will be divided into three main phases.

Preoperative phase:

Patients who meet the inclusion criteria and none of the exclusion criteria and after giving consent to participate in the study will be identified by the Data Collector (DC) at each clinical site. The DC will extract preliminary information from the EHR and confirm the eligibility with the principal investigator at the site before surgery. Preoperative data, including tabular patient information and various preoperative imaging modalities, will be collected, anonymized and transcribed by the DC from several sources (EHR, self-reported questionnaires and interviews/questionnaires/reports from healthcare professionals involved in the neurosurgical workflows). These data will be entered into the STRATUM eCRF.

Intraoperative phase:

During surgery, the operating surgeon will be assisted by the DC in carrying out the following tasks: * Collection of intraoperative data: The STRATUM acquisition system will be used to capture in-situ HS and standard RGB (Red-Green-Blue) images, depth data, and other relevant intraoperative information of the exposed brain surface. * Tumour identification and resection: The operating surgeon will identify and resect suspicious tumour tissue based on neuronavigation guidance and their surgical judgement according to the standard procedure. At least one tissue sample should be resected from the centre of the tumour site. If possible, the operating surgeon will decide to excise and separately store one to seven additional suspicious tissue samples for definitive pathological diagnosis as part of their routine clinical practice, identifying them within both the STRATUM and the neuronavigation systems for subsequent correlation analysis. Resected tissue samples will be processed according to local protocols at each clinical site. A specific coding system linking the project, clinical site, patient and tissue sample will be used to enter the data in the eCRF. These suspected tumour samples will subsequently undergo histological analysis. Additional samples will be taken from surgical margins where tumour presence is suspected, aligning with the standard surgical procedure. Therefore, no extra sampling of tissue not suspected to be tumorous will be performed. * Neuronavigation procedure documentation: The entire neuronavigation process will be recorded, including multiple positioning points of the neuronavigator marker in relation to the navigable MRI at key stages of the surgical procedure. At least, the neuronavigator marker should be documented within the neuronavigation system (and captured by the STRATUM acquisition system) at the exact tumour site location where the tissue sample will be resected for pathological diagnosis. At least, three HS images will be captured during surgery: 1) A full capture of the exposed brain surface following craniotomy and durotomy; 2) A capture taken at an intermediate stage of the surgery where the tumour tissue is clearly exposed; 3) A final capture after completing the tumour resection. Prior to each HS image, the surgeon will ensure that the exposed area is carefully cleaned to prevent artifacts in the imaging data. When feasible, additional images corresponding to point 2) will be captured throughout the surgery to obtain the clearest possible representation of the brain tumour area. * Identification and recording of non-tumour brain tissue data: At least one highly reliable non-tumour brain tissue area will also be identified and documented based on neuronavigation and the operating surgeon's judgement. These positions should be recorded within both the neuronavigation system and the STRATUM acquisition system for further correlation analysis, but no tissue sample will be resected. The same coding protocol will be used in the eCRF to label the captured images of non-tumour brain tissue. * Intraoperative data storage: Immediately after surgery, the DC will download the captured data from the STRATUM acquisition system and the neuronavigator and store them in an external, encrypted, hard drive for subsequent pseudo-anonymization. Once pseudo-anonymized, all multimodal data (HS and RGB images, depth data, different MRI modalities, etc.) will be uploaded to the secure, dedicated STRATUM server at IUMA-ULPGC.

Postoperative phase:

Post-operative data will be collected by the DC from the EHR at one, three,

• STRATUM Tool intraoperative identification of Contrast Enhancing Tumour (CET) or non-Contrast Enhancing Tumour (nCET) (FLAIR positive) [Time frame: Between 3- and 4-weeks post-surgery for the last patient recruited, following completion and assessment of the post-operative MRI.]