Prognostic Modeling of Overall Survival in Glioblastoma Using Radiomic Features Derived from Intraoperative Ultrasound: A Multi-Institutional Study.

Background: Accurate prognostic models are essential for optimizing treatment strategies for glioblastoma, the most aggressive primary brain tumor. While other neuroimaging modalities have demonstrated utility in predicting overall survival (OS), intraoperative ultrasound (iUS) remains underexplored for this purpose. This study aimed to evaluate the prognostic potential of iUS radiomics in glioblastoma patients in a multi-institutional cohort.

Methods: This retrospective study included patients diagnosed with glioblastoma from the multicenter Brain Tumor Intraoperative (BraTioUS) database. A single 2D iUS slice, showing the largest tumor diameter, was selected for each patient. Radiomic features were extracted and subjected to feature selection, and clinical data were collected. Using a fivefold cross-validation strategy, Cox proportional hazards models were built using radiomic features alone, clinical data alone, and their combination. Model performance was assessed via the concordance index (C-index).

Results: A total of 114 patients met the inclusion criteria, with a mean age of 56.88 years, a median OS of 382 days, and a median preoperative tumor volume of 32.69 cm. Complete tumor resection was achieved in 51.8% of the patients. In the testing cohort, the combined model achieved a mean C-index of 0.87 (95% CI: 0.76-0.98), outperforming the radiomic model (C-index: 0.72, 95% CI: 0.57-0.86) and the clinical model (C-index: 0.73, 95% CI: 0.60-0.87).

Conclusions: Intraoperative ultrasound relies on acoustic properties for tissue characterization, capturing unique features of glioblastomas. This study demonstrated that radiomic features derived from this imaging modality have the potential to support the development of survival models.