Ultrasound echo envelope statistics to differentiate glioblastoma multiforme from normal brain tissue ex vivo: a proof-of-concept study

Objectives Glioblastoma multiforme (GBM) is one of the most aggressive brain tumors, characterized by high heterogeneity and vascular proliferation. These features complicate its differentiation from normal brain tissue during surgical resection. Conventional ultrasound imaging provides limited quantitative insight into underlying tissue microstructure. This study investigates whether multiparametric quantitative ultrasound (QUS) features derived from envelope statistics can differentiate GBM from normal tissue. Specifically, the ability of the Homodyned-K (HK), Nakagami, and Burr distributions to capture microstructural differences is evaluated. The study also examines whether combining these features within a multiparametric framework and applying machine-learning-based classifications improves diagnostic performance. Methods Ex vivo formalin-fixed human normal brain tissue and GBM samples (n = 20 per group) were embedded in agarose phantoms. Ultrasound data were acquired using a Verasonics Vantage 128 system with a 7.6 MHz linear array. Envelope data were analyzed within regions of interest (ROIs) selected within the imaging depth of field. HK, Nakagami, and Burr distributions were fitted to the envelope data to extract statistical parameters. These features were used for classification with logistic regression, support vector machines, random forests, and multilayer perceptron models. Model performance was assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Results Parameters derived from the HK, Nakagami, and Burr distributions were significantly different in GBM than in normal brain tissue, indicative of differences in tissue microstructure and scattering behavior. Among the individual statistical models, HK-derived features achieved the highest classification performance, with 90.0% accuracy. Multiparametric classification further improved performance, achieving 80.5% per-plane accuracy (AUC = 0.854) and 97.5% per-sample accuracy (AUC = 0.985). A reduced four-feature subset achieved comparable performance. Conclusion These findings demonstrate that envelope statistics capture complementary aspects of GBM microstructure, enabling effective differentiation from normal brain tissue. The multiparametric framework improves characterization by integrating information across statistical models, while machine learning-based classification highlights its potential for tissue differentiation. These findings provide proof of concept for the use of multiparametric ultrasound envelope statistics in GBM tissue characterization. Further studies in freshly excised tissues and intraoperative settings are required to evaluate the translational potential of this approach.