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, and six
months after surgery. The same anonymization protocol will be applied, ensuring that
all multimodal data is securely stored on the STRATUM server, while tabular data is
entered into the STRATUM eCRF.
Once data collection is completed for each patient, the Study Monitor, who is
independent of the research team, will review the database for errors and missing
data, ensuring data quality. If necessary, the Study Monitor will collaborate with
the site DC to resolve errors or discrepancies between the eCRF and the primary
source data (e.g., EHR and questionnaires) and make direct revisions in the eCRF. In
case of a participant ceases participation in the study or is lost to follow-up, the
anonymized data generated until that moment will be employed, if possible, for the
technical validation, but the observation will be excluded from the analyses where
the missing data is necessary to compute the outcomes in the subsequent clinical
trial.
STUDY MEASURE CATEGORIES. The collected measures will span from patient enrolment to the end of a 6-month
follow-up period after surgery and will be categorized in the following main
domains:
- - Patient characteristics: Tabular data from the patient, such as age, year of
birth, weight, sex, etc.
- Patient´s symptoms: Tabular data from the patient symptoms related to
neurological consequences of the tumour.
- - PROMs (Patient-Reported Outcome Measures): Scores obtained from the KPS
(Karnofsky Performance Status) scale, the ECOG (Eastern Cooperative Oncology
Group) performance status scale, the 5-level EQ-5D version (EQ-5D-5L)
instrument, the EORTC (European Organization for Research and Treatment of
Cancer) QLQ- BN20 brain tumour module and the EORTC QLQ Core Questionnaire
(EORTC QLQ-C).
- - Vasari guide variables: The variables related to the radiological
characteristics of the brain tumours follow the VASARI (Visually AcceSAble
Rembrandt Images) MRI visual feature guide.
Such variables will be measured
using the minimum requirements to allow lesion volumetry and diagnosis using a
1.5 T MRI.
- - Surgery details: These variables represent the information related to the
personnel, tools, materials involved in the surgery, as well as the duration of
the different parts and tasks of the surgery.
These variables will help to
quantify the cost and efficiency of the surgery.
- - Intraoperative pathology: Variables related to the intraoperative tissue
diagnosis provided after analysing frozen section samples for rapid diagnosis
during surgery according to the local routine practices at each site.
In case
more than one intraoperative tissue analysis is performed, these data will be
collected for each sample independently.
- - Definitive pathology: Variables related to the definitive tissue diagnosis
provided by histopathology.
The CAP (College of American Pathologists) protocol
for the examination of tumours of the brain and spinal cord (version 1.0.0.0,
September 2022) will be followed to diagnose intra-axial primary brain tumours.
Molecular information will be provided according to the 5th edition of the WHO
(World Health Organization) Classification of Tumours of the CNS (Central
Nervous System), where it is specified that molecular information should be
integrated into many of the tumour types, such as diffuse gliomas and embryonal
tumours.
- - Definitive pathology (STRATUM-related samples): Variables related to the
definitive tissue diagnosis provided by histopathology of the additional
resected suspected tumour samples for STRATUM technical validation.
This only
includes the specimen size and the tissue type (non-tumour or tumour). The same
standard procedures as presented before will be applied to this
histopathological analysis.
- - Exitus: Variables related to the patient's death in case it occurs.
- - Postoperative MRI outcomes: Variables related to the extent of resection and
volume of residual tumour on postoperative 48/72 h MRI using 1.5T MRI.
- - Complications: Set of variables representing the possible postoperative
complications related to brain tumour surgery, including the type of
complication, its treatment, and the diagnostic tests performed, if necessary.
- - Medication: Set of variables representing the group type of the medication
(painkillers, steroids, antiseizures, other) administered to the patient after
brain surgery and related to it.
- - Emergency and hospital readmissions: Variables representing the hospital
admission and discharge dates of the patient, including the visits to emergency
room, due to causes related to brain tumour surgery, but different to the
hospital stay due to the surgical operation.
- - Follow-up MRI: Variables indicating the date of the follow-up MRI, the reason
and whether or not progression has occurred.
- - Treatments: This group of variables represents the class of postoperative
treatment (chemotherapy or radiotherapy) applied to the patient related to the
brain surgery, including the types and subtypes of treatment, the number of
cycles or sessions and the dose (in case of chemotherapy).
- - Hospital stay: Dates of admission and discharge measured from the moment the
patient enters (prior surgery) and leaves (after surgery) the hospital,
including those related to the time spent at neurosurgical care ward.
- - Follow-up visits to professionals: Variables representing the number of
follow-up visits to different healthcare professionals in relation to the brain
tumour surgery, including the professional type and the date of the visit.
- - Follow-up tests: Variables representing the number of follow-up tests performed
to the patient in relation to the management of the brain tumour, including the
type of test and date performed.
SAMPLE SIZE. In total, it is estimated that 26 patients will undergo brain surgery per month
across the three clinical sites. Of these, approximately 70% of patients (~18
patients/month) are expected to meet the inclusion criteria, satisfy none of the
exclusion criteria, and provide the informed consent for study participation. A 90%
success rate in obtaining usable samples is anticipated (~16 patients/month).
Consequently, STRATUM-OS is expected to collect data from 320 consecutive patients
over 20 months of recruitment, followed by a 6-month follow-up period (total
duration: 28 months). Given an average of 4.5 samples per patient (accounting for
potential sample loss during pathological analysis), a total of approximately 1,440
tissue samples are expected to be collected.
These estimations have been obtained based on previous experiences from the project
partners. Particularly, from the HELICoiD and ITHaCA projects in which the same HS
acquisition system was employed, a total of 85 HS images were obtained from 41
different subjects captured in three data acquisition campaigns at the Hospital
Universitario de Gran Canaria Dr. Negrín, covering a 24-months period in total. From
this dataset, 28% of HS images were excluded (17% of subjects), resulting in 61 HS
images from 34 eligible subjects. Additionally, the study conducted at the Hospital
Universitario 12 de Octubre within the NEMESIS-3D-CM project was able to obtain a
multimodal dataset composed by HS images from 193 different subjects, also in a
24-months period.
Although patient-level data are important for clinical context and for using them as
historical controls in the subsequent STRATUM-NRCCT study, in this study the primary
unit of analysis for the main outcome measure is the individual tissue sample, which
will be histologically classified as "tumour" or "non-tumour" and serve as the
reference standard for technical validation. Therefore, the effective sample size
for the statistical analysis is determined by the number of validated tissue
samples. This volume of data is expected to provide sufficient statistical power to
estimate key diagnostic performance metrics of the STRATUM Tool-such as sensitivity,
specificity, and predictive values-with acceptable precision.
A preliminary evaluation of inclusion rates will be conducted after the first 3
months of recruitment, to identify potential barriers to enrolment. If necessary,
corrective measures will be implemented to ensure that the study reaches its target
sample size.
DATA PARTITION FOR TECHNICAL VALIDATION. In AI-based applications, data partitioning is the process of dividing a dataset
into several subsets (e.g., training, validation and test sets). This process is
crucial to evaluate and validate the performance of developed AI models, ensuring
that models are validated and tested using unseen data for model training. This is
highly important especially in medical applications, where data from different
subjects must be in independent sets. This allows a more accurate assessment of the
model performance, avoiding overfitting and obtaining more generalized models for
unseen data/subjects.
In this study, we plan to utilize the initial 70% of recruited patients to train the
AI algorithm (n=224 patients). The subsequent 10% will be used for cross-validation
(n=32), and the final 20% will serve to test the model (n=64). While data
partitioning is performed at the patient level to avoid information leakage, the
actual analysis will be conducted at the tissue sample level, using labels validated
by histopathology to assess diagnostic performance at the tissue sample level.
STATISTICAL METHODS. To ensure a robust and unbiased evaluation of the STRATUM Tool, the dataset will be
partitioned at the patient level into three non-overlapping subsets: 70% for model
training, 10% for internal validation, and 20% for final testing. This partitioning
strategy is intended to prevent data leakage across subsets, ensuring that all
tissue samples from the same patient are assigned to the same group. Observations
from participants with missing data may be excluded from analyses.
The training set will be used to build the AI model by learning patterns from
intraoperative imaging data paired with corresponding histopathological or
radiological labels. The validation set will be used during model development to
fine-tune hyperparameters and optimize training procedures (e.g., early stopping,
learning rate adjustments, regularization). No metrics will be formally reported
from the validation set.
The test set will be completely isolated during model development and used
exclusively to compute final performance metrics. These will include accuracy,
sensitivity, specificity, precision, F1-score, ROC curve analysis, the Jaccard
Index, and the Dice-Sørensen coefficient, as appropriate to the outcome type
(classification or segmentation). Diagnostic performance will be assessed for two
main tasks:
- (1) the distinction between tumour and non-tumour tissue samples, using
definitive histopathological analysis as the reference standard; and (2) the
identification of CET and nCET (FLAIR-positive) regions in MRI, using
histopathological and radiological reports as the reference.
Subgroup analyses will be performed for the most common histological tumour types,
defined as those with at least 25 patients included in the test set. All subgroup
evaluations will be conducted exclusively within the test set to ensure unbiased
estimation. Due to the subsample nature of the investigation, performance estimates
may have wide confidence intervals, particularly for less frequent tumour subtypes.
ETHICS AND DISSEMINATION. The study will adhere to the ethical principles for medical research involving human
subjects established in the Declaration of Helsinki and the Good Clinical Practice
Guidelines. According to our previous experience, the use of HSI has not
demonstrated any safety or tolerability concerns in surgical procedures.
Multimodal data will be captured by expert neurosurgeons using the STRATUM
acquisition system designed, produced, and installed at each clinical site (at the
time of surgery, no real-time results on tissue classification will be displayed to
physicians, except for the standard frozen section histopathological diagnostic
information they usually receive). The STRATUM acquisition system will not alter the
surgical procedure, apart for the data collection process (estimated to be ~10 min
during the entire surgery with no expected negative effects for the patient).
Captured data will not influence or modify the neurosurgical plan. As part of the
standard procedure, tumour tissue samples and adjacent tissue samples (suspected to
be tumour) will be collected for pathological analysis. These samples will serve as
golden standard for the algorithm development. This sample collection will not
interfere with the intervention, histopathological analysis, or the intraoperative
decision-making. Pathologists will have no access to the STRATUM tool results prior
to their independent analysis, even during validation and testing phases after
initial training.
Patient confidentiality and data security will be managed in compliance with General
Data Protection Regulations and relevant national and European legislations as per
local and national ethical approvals. All study-related information will be securely
stored at the clinical sites. All local databases will be protected by password
restricted access systems. Forms, lists, logbooks, appointment books, and any other
listings that link participant ID numbers to other identifying information will be
stored in a separated, locked restricted-access areas. Only authorized personnel,
including researchers involved in the STRATUM project, the sponsor or designated
representatives, the Ethics Committee, and relevant health authorities will have
access to this data.
All data and biological samples collected during STRATUM-OS will be used exclusively
for the development and technical validation of the STRATUM tool, as well as for the
creation of a historical control group for the subsequent non-randomized controlled
clinical trial (STRATUM-NRCCT). This purpose is explicitly stated among the primary
objectives of the present protocol. Therefore, no additional informed consent will
be required for this use, as participants will be fully informed and provide consent
to both components-technical validation and historical control generation-at the
time of inclusion. Any future use of data or samples beyond the scope of this
protocol will require prior approval from the relevant Ethics Committees and, where
applicable, new participant consent. Participants will have the right to access,
rectify, delete, limit the processing, portability and opposition of their data by
contacting the principal investigator of the project in each clinical site.
The results of this study will be published in open access journals, regardless of
whether the findings are positive or negative. The study results will be shared with
the participating physicians, referring clinicians, patients, and the broader
medical and scientific community. Data (properly anonymized) will be stored in the
secure STRATUM repository at the project coordination institution and, upon project
completion, will be archived in trusted repositories, having their respective
digital object identifiers (DOIs).
