Risk of Peritumoral Invasion in Rat Glioblastoma: Nomogram-based Ultrasound Localization Microscopy

Background This study aims to develop and assess a nomogram based on multiparametric ultrasound localization microscopy to evaluate the risk of peritumoral invasion. Methods Thirty-six in situ rat glioblastoma models were created. After craniotomy, ultrasound localization microscopy was used to quantify microvascular morphology and hemodynamics, which were combined with multimodal magnetic resonance imaging to manually delineate the invasive and normal brain regions. The least absolute shrinkage and selection operator regression algorithm was applied to select ultrasound localization microscopy parameters, followed by multivariable logistic regression to identify significant variables. A nomogram to predict peritumoral invasion risk was constructed using R software, and its diagnostic performance was evaluated. Results Vascularity (p < 0.001), orientation variance (p = 0.013), and diameter (p = 0.002) were identified as independent predictors of peritumoral invasion. The prediction model demonstrated strong discriminatory power, with an area under the curve of 0.964 (0.933–0.994) for the training set and 0.995 (0.984–1.000) for the validation set. The goodness-of-fit Hosmer-Lemeshow test statistics were 5.135 (p = 0.702) and 3.163 (p = 0.237), indicating that the predicted invasion risk closely matched the actual risk. Decision curve analysis revealed that when the invasion incidence ranged from 1–99% in the training set and from 5–94% in the validation set, the nomogram provided clinical benefit, demonstrating good generalizability. Conclusions We developed and validated a nomogram to predict peritumoral invasion in glioblastoma, enabling clinicians to perform preoperative risk assessments and implement personalized surgical strategies to improve resection rates.