Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| UiT The Arctic University of Norway | OTHER |
| University Hospital of North Norway | OTHER |
| University of Valladolid | OTHER |
Not provided
Not provided
Not provided
Not provided
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.
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.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-guided surgery | Procedure | Neuronavigated targeted biopsy sampling. Supramarginal resection including high-risk areas of recurrence defined by a radiomics-based model. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Feasibility using eligibility | Among all screened patients, the proportion of patients who meet the eligibility criteria | Screening/Enrollment |
| Feasibility using the proportion of consent | Among all screened patients, the proportion of patients consenting to participate | Screening/Enrollment |
| Measure | Description | Time Frame |
|---|---|---|
| Efficacy using overall survival | Measured in days from surgery to the time of death | From date of surgery until the date of death from any cause, assessed up to 36 months |
| Efficacy using progression-free survival |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Santiago Cepeda, PhD | Hospital RÃo Hortega | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Hospital Rio Hortega | Valladolid | Valladolid | 47012 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36980783 | Background | 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. | |
| 42010946 |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D005909 | Glioblastoma |
| ID | Term |
|---|---|
| D001254 | Astrocytoma |
| D005910 | Glioma |
| D018302 | Neoplasms, Neuroepithelial |
| D017599 | Neuroectodermal Tumors |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Assessment of progression-free survival based on the Modified Criteria for Radiographic Response Assessment in Glioblastoma (mRANO) criteria.
| 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. | 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 | 30 days |
| Extent of resection | Volumetric measurement of contrast enhancement and T2-FLAIR signal alteration on MRI | < 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) | 30 days |
| Derived |
| Cepeda 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 Apr 20:noag088. doi: 10.1093/neuonc/noag088. Online ahead of print. |
| D009373 |
| Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009380 | Neoplasms, Nerve Tissue |