Supramarginal Resection in Glioblastoma Guided by Artificial Intelligence

NCT ID: NCT05735171

Last Updated: 2025-12-24

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 20 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-11-01
• Study Completion Date: 2025-12-01

Brief 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. 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.

Conditions

Glioblastoma

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

AI-guided resection

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.

Group Type: EXPERIMENTAL

AI-guided surgery

Intervention Type: PROCEDURE

Neuronavigated targeted biopsy sampling. Supramarginal resection including high-risk areas of recurrence defined by a radiomics-based model.

Interventions

AI-guided surgery

Neuronavigated targeted biopsy sampling. Supramarginal resection including high-risk areas of recurrence defined by a radiomics-based model.

Intervention Type: PROCEDURE

Eligibility Criteria

Inclusion Criteria

• 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

Exclusion Criteria

• 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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

UiT The Arctic University of Norway

OTHER

Sponsor Role: collaborator

University Hospital of North Norway

OTHER

Sponsor Role: collaborator

University of Valladolid

OTHER

Sponsor Role: collaborator

Hospital del Rio Hortega

OTHER

Sponsor Role: lead

Responsible Party

Santiago Cepeda

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Santiago Cepeda, PhD

Role: PRINCIPAL_INVESTIGATOR

Hospital Río Hortega

Locations

University Hospital Rio Hortega

Valladolid, Valladolid, Spain

Countries

Spain

References

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.

Reference Type: BACKGROUND

PMID: 36980783 (View on PubMed)

Other Identifiers

22-PI208

Identifier Type: -

Identifier Source: org_study_id