GlioblastomaSupraGlio-AINCT05735171
Supramarginal Resection in Glioblastoma Guided by Artificial Intelligence
A study testing AI-guided surgery in people with Glioblastoma, run by Hospital del Rio Hortega at 1 site in Spain.
Trial summary
Glioblastomas are the most common and poorly prognostic primary brain neoplasms. Despite advances in surgical techniques and chemotherapy, the median survival time for these patients remains less than 15 months. This highlights the need for more effective treatments and improved prognostic tools. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Glioblastoma
- What is being tested?
- AI-guided surgery
- Type of treatment
- Surgery or procedure
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 20 participants
- Where
- 1 siteSpain
- Study dates
- 1 Nov 2022to 1 Dec 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- A suspected diagnosis of supratentorial glioblastoma by MRI.
- Tumor in non eloquent brain region according to the UCSF (University of California, San Francisco) classification, including the sensor motor areas (precentral and postcentral gyri), perisylvian language areas in the dominant hemisphere (superior temporal, inferior frontal, and inferior parietal gyri), basal ganglia, internal capsule, thalamus, and visual cortex around the calcarine sulcus
- Indication for surgical treatment and where supramarginal resection is considered possible according to the preoperative imaging. This consideration needs to be verified by two specialists in neurosurgery. This criterion needs to be verified by two senior neurosurgeons.
- Karnofsky Performance Score ≥ 70;
- Written informed consent
Who cannot join
- Tumors in eloquent areas.
- Recurrent gliomas (except biopsy)
- MR image data not usable due to artifacts during acquisition. Inability to give written informed consent
- KPS < 70
- Severe comorbidity.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Tailored Supramarginal Resection in Glioblastoma Guided by Artificial Intelligence-based Recurrence Probability Maps. A Non-randomized Pilot Study
Glioblastomas are the most common and poorly prognostic primary brain neoplasms. Despite advances in surgical techniques and chemotherapy, the median survival time for these patients remains less than 15 months. This highlights the need for more effective treatments and improved prognostic tools. The globally accepted surgical strategy currently consists of achieving the maximum safe resection of the enhancing tumor volume. However, the non-enhancing peritumoral region contains viable cells that cause the inevitable recurrence that these patients face. Clinicians currently lack an imaging tool or modality to differentiate neoplastic infiltration in the peritumoral region from vasogenic edema. In addition, it is not always feasible to include all the T2-FLAIR signal alterations surrounding the enhancing tumor in the surgical planning due to the proximity of eloquent areas and the higher risk of postoperative deficits.
However, the investigators have developed a model to predict regions of recurrence based on machine learning and MRI radiomic features that have been trained and evaluated in a multi-institutional cohort.
The investigators aim to analyze whether an adjusted supramarginal resection guided by these new recurrence probability maps improves survival in selected patients with glioblastoma.
Read the detailed description
The SupraGlio-AI study aims to test the feasibility of the proposed AI-guided tailored supratotal resection for glioblastomas. The study will provide preliminary data on the accuracy of the AI model in predicting recurrence and the impact of using this information in surgical planning. This information will be crucial in determining the potential for a larger, randomized controlled trial in the future. The pilot study will also allow for refinement of the study design, intervention, and data collection processes before a larger-scale study is conducted. In addition to testing the feasibility and efficacy of the AI-guided tailored supratotal resection, this pilot study also has two secondary objectives: 1) Survival Analysis: The survival analysis will provide insights into the impact of using the AI model on patient outcomes and help determine the potential benefits of this approach. 2) Histopathological and Transcriptomic Analysis: The study will also include a histopathological and transcriptomic analysis of the tissue samples obtained from the high-risk regions defined by the AI model. This analysis will provide information on the molecular and cellular changes occurring in these regions and may offer insights into the underlying biology of glioblastoma recurrence. These data will inform the development of future studies aimed at improving patient outcomes.
By incorporating these secondary objectives, this pilot study will contribute to a more comprehensive understanding of the potential benefits of using AI in guiding tailored supratotal resection for glioblastomas. The results will inform future research and potentially lead to the development of improved treatment approaches for patients with this type of brain tumor.
- Conditions
- Glioblastoma
- Being tested
- AI-guided surgery
- Medical subject headings
- Glioblastoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-guided surgeryProcedure or surgery
Neuronavigated targeted biopsy sampling. Supramarginal resection including high-risk areas of recurrence defined by a radiomics-based model.
| Study group | Role | What they receive |
|---|---|---|
| AI-guided resection | Experimental | Tailored supramarginal surgery guided by AI-based recurrence probability maps. Aim of supramarginal resection, where the high-risk of recurrence areas identified by the AI-based model are subsidiary to be removed as safe locations for the patient. Receives: Procedure: AI-guided surgery |
What is being measured
The primary outcome is the main question the study is designed to answer.
Feasibility using eligibility
Among all screened patients, the proportion of patients who meet the eligibility criteria
Measured: Screening/Enrollment
Feasibility using the proportion of consent
Among all screened patients, the proportion of patients consenting to participate
Measured: Screening/Enrollment
Secondary outcomes (6)
Efficacy using overall survival
Measured in days from surgery to the time of death
Measured: From date of surgery until the date of death from any cause, assessed up to 36 months
Efficacy using progression-free survival
Assessment of progression-free survival based on the Modified Criteria for Radiographic Response Assessment in Glioblastoma (mRANO) criteria.
Measured: From date of surgery until the date of first documented progression, assessed up to 36 months
Safety using the neurological function
The National Institutes of Healt Stroke Scale (NIHSS) will be used to assess neurological function. The NIHSS is composed of 11 items, each of which scores a specific ability between a 0 and 4. For each item, a score of 0 typically indicates normal function in that specific ability, while a higher score is indicative of some level of impairment.
Measured: 30 days
Safety using global disability
The modified Rankin scale (mRS ) is a measure of global disability that has been widely used to assess outcome after stroke. The scale runs from 0-6, running from perfect health without symptoms to death
Measured: 30 days
Extent of resection
Volumetric measurement of contrast enhancement and T2-FLAIR signal alteration on MRI
Measured: < 72 hours after surgery
Postoperative complication
Relevant post surgical complication that requires a second surgery or prolong the length of hospitalization (i.e. hematoma, infection)
Measured: 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2023
- Study start1 Nov 2022
- Primary completion30 May 2025
- Study completion1 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for GlioblastomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Non-randomised
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 20 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hospital del Rio Hortega
- Sponsor type
- University, hospital or other
- Collaborators
- UiT The Arctic University of Norway, University Hospital of North Norway, University of Valladolid
- Responsible party
- Principal investigator, Santiago Cepeda, Hospital del Rio Hortega
- Investigators
- Santiago Cepeda, PhD, Principal investigator, Hospital Río Hortega
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05735171
- Sponsor's protocol ID
- 22-PI208
- Acronym
- SupraGlio-AI
- Status last verified
- Dec 2025
- First submitted
- 7 Feb 2023
- Last updated
- 24 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Cepeda S, Luppino LT, Perez-Nunez A, Solheim O, Garcia-Garcia S, Velasco-Casares M, Karlberg A, Eikenes L, Sarabia R, Arrese I, Zamora T, Gonzalez P, Jimenez-Roldan L, Kuttner S. Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers (Basel). 2023 Mar 22;15(6):1894. doi: 10.3390/cancers15061894.
PubMed 36980783Cepeda S, Hernando-Perez E, Perez-Riesgo E, Rodriguez-Valle I, Esteban-Sinovas O, Arrese I, Lucero-Salaverry MM, Zamora T, Torres-Nieto MA, Luppino LT, Kuttner S, Wodzinski M, Escudero T, Garzon J, Romero-Oraa R, Hornero R, Nunez L, Villalobos C, Sarabia R. Prospective biopsy-controlled validation of an AI model for predicting glioblastoma infiltration: Results from the SupraGlio trial. Neuro Oncol. 2026 Aug 1;28(8):1999-2013. doi: 10.1093/neuonc/noag088.
PubMed 42010946
Study at a glance
From the registry record, last updated by the sponsor on 24 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Tailored Supramarginal Resection in Glioblastoma Guided by Artificial Intelligence-based Recurrence Probability Maps. A Non-randomized Pilot Study
- ClinicalTrials.gov ID
- NCT05735171
- Acronym / sponsor ID
- SupraGlio-AI / 22-PI208
- Conditions
- Glioblastoma
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 20 participants (actual)
- Lead sponsor
- Hospital del Rio HortegaUniversity, hospital or other
- Collaborators
- UiT The Arctic University of Norway, University Hospital of North Norway, University of Valladolid
- Study start
- 1 Nov 2022 (actual)
- Primary completion
- 30 May 2025 (actual)
- Study completion
- 1 Dec 2025 (actual)
- First posted
- 21 Feb 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Glioblastomas are the most common and poorly prognostic primary brain neoplasms. Despite advances in surgical techniques and chemotherapy, the median survival time for these patients remains less than 15 months. This highlights the need for more effective treatments and improved prognostic tools. The globally accepted surgical strategy currently consists of achieving the maximum safe resection of the enhancing tumor volume. However, the non-enhancing peritumoral region contains viable cells that cause the inevitable recurrence that these patients face. Clinicians currently lack an imaging tool or modality to differentiate neoplastic infiltration in the peritumoral region from vasogenic edema. In addition, it is not always feasible to include all the T2-FLAIR signal alterations surrounding the enhancing tumor in the surgical planning due to the proximity of eloquent areas and the higher risk of postoperative deficits.
However, the investigators have developed a model to predict regions of recurrence based on machine learning and MRI radiomic features that have been trained and evaluated in a multi-institutional cohort.
The investigators aim to analyze whether an adjusted supramarginal resection guided by these new recurrence probability maps improves survival in selected patients with glioblastoma.
Detailed description
The SupraGlio-AI study aims to test the feasibility of the proposed AI-guided tailored supratotal resection for glioblastomas. The study will provide preliminary data on the accuracy of the AI model in predicting recurrence and the impact of using this information in surgical planning. This information will be crucial in determining the potential for a larger, randomized controlled trial in the future. The pilot study will also allow for refinement of the study design, intervention, and data collection processes before a larger-scale study is conducted. In addition to testing the feasibility and efficacy of the AI-guided tailored supratotal resection, this pilot study also has two secondary objectives: 1) Survival Analysis: The survival analysis will provide insights into the impact of using the AI model on patient outcomes and help determine the potential benefits of this approach. 2) Histopathological and Transcriptomic Analysis: The study will also include a histopathological and transcriptomic analysis of the tissue samples obtained from the high-risk regions defined by the AI model. This analysis will provide information on the molecular and cellular changes occurring in these regions and may offer insights into the underlying biology of glioblastoma recurrence. These data will inform the development of future studies aimed at improving patient outcomes.
By incorporating these secondary objectives, this pilot study will contribute to a more comprehensive understanding of the potential benefits of using AI in guiding tailored supratotal resection for glioblastomas. The results will inform future research and potentially lead to the development of improved treatment approaches for patients with this type of brain tumor.
Study design
- Primary purpose
- Treatment
- Allocation
- Non-randomised
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-guided resection | Experimental | Procedure: AI-guided surgeryTailored supramarginal surgery guided by AI-based recurrence probability maps. Aim of supramarginal resection, where the high-risk of recurrence areas identified by the AI-based model are subsidiary to be removed as safe locations for the patient. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- A suspected diagnosis of supratentorial glioblastoma by MRI.
- Tumor in non eloquent brain region according to the UCSF (University of California, San Francisco) classification, including the sensor motor areas (precentral and postcentral gyri), perisylvian language areas in the dominant hemisphere (superior temporal, inferior frontal, and inferior parietal gyri), basal ganglia, internal capsule, thalamus, and visual cortex around the calcarine sulcus
- Indication for surgical treatment and where supramarginal resection is considered possible according to the preoperative imaging. This consideration needs to be verified by two specialists in neurosurgery. This criterion needs to be verified by two senior neurosurgeons.
- Karnofsky Performance Score ≥ 70;
- Written informed consent
Key exclusion criteria (5 listed)
- Tumors in eloquent areas.
- Recurrent gliomas (except biopsy)
- MR image data not usable due to artifacts during acquisition. Inability to give written informed consent
- KPS < 70
- Severe comorbidity.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-guided surgeryNeuronavigated targeted biopsy sampling. Supramarginal resection including high-risk areas of recurrence defined by a radiomics-based model. | Procedure or surgery | AI-guided resection |
Primary outcome
Feasibility using eligibility
Time frame: Screening/Enrollment
Among all screened patients, the proportion of patients who meet the eligibility criteria
Feasibility using the proportion of consent
Time frame: Screening/Enrollment
Among all screened patients, the proportion of patients consenting to participate
6 secondary outcomes
Efficacy using overall survival
Time frame: From date of surgery until the date of death from any cause, assessed up to 36 months
Efficacy using progression-free survival
Time frame: From date of surgery until the date of first documented progression, assessed up to 36 months
Safety using the neurological function
Time frame: 30 days
Safety using global disability
Time frame: 30 days
Extent of resection
Time frame: < 72 hours after surgery
Postoperative complication
Time frame: 30 days
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Dec 2025.
- Background: PubMed 36980783Cepeda S, Luppino LT, Perez-Nunez A, Solheim O, Garcia-Garcia S, Velasco-Casares M, Karlberg A, Eikenes L, Sarabia R, Arrese I, Zamora T, Gonzalez P, Jimenez-Roldan L, Kuttner S. Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers (Basel). 2023 Mar 22;15(6):1894. doi: 10.3390/cancers15061894.
- Derived from this study: PubMed 42010946Cepeda S, Hernando-Perez E, Perez-Riesgo E, Rodriguez-Valle I, Esteban-Sinovas O, Arrese I, Lucero-Salaverry MM, Zamora T, Torres-Nieto MA, Luppino LT, Kuttner S, Wodzinski M, Escudero T, Garzon J, Romero-Oraa R, Hornero R, Nunez L, Villalobos C, Sarabia R. Prospective biopsy-controlled validation of an AI model for predicting glioblastoma infiltration: Results from the SupraGlio trial. Neuro Oncol. 2026 Aug 1;28(8):1999-2013. doi: 10.1093/neuonc/noag088.
- Individual participant dataSharing planned: Undecided
Related trials for Glioblastoma
RecruitingObservational study
Collection of Blood and Urine Samples in Patients Receiving Radiation Therapy for Glioblastoma Multiforme
Glioblastoma Multiforme
Active, not recruitingPhase 3
Sonocloud-9 in Association With Carboplatin Versus Standard-of-Care Chemotherapies (CCNU or TMZ) in Recurrent GBM
Glioblastoma, Recurrent Glioblastoma
RecruitingPhase 1
Anti-GD2 ADC M3554 in Advanced Solid Tumors
Advanced Solid Tumor
Active, not recruitingPhase 1
Genetically Modified T-cells in Treating Patients With Recurrent or Refractory Malignant Glioma
Recurrent Glioblastoma, Recurrent Malignant Glioma
RecruitingPhase 2
Tamoxifen Versus Etoposide After First Recurrence in GBM Patients
Glioblastoma Multiforme
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Santiago Cepeda, PhD principal investigator
Hospital Río Hortega
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-guided surgery in people with Glioblastoma, run by Hospital del Rio Hortega at 1 site in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 24 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Feasibility using eligibility (time frame: Screening/Enrollment). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05735171, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Dec 2025; AzTrials last refreshed its copy on 6 Nov 2026.