Supramarginal Resection in Glioblastoma Guided by Artificial Intelligence

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.

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 sulcusIndication 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 ≥ 60;Written informed consentExclusion Criteria:Tumors in eloquent areas.Recurrent gliomas (except biopsy)MR image data not usable due to artifacts during acquisition. Inability to give written informed consentKPS < 70Severe comorbidity.