Histogram Analysis of Ultrasound Localization Microscopy for Predicting Isocitrate Dehydrogenase Status in Rat...

Isocitrate dehydrogenase (IDH) status is a critical biomarker for guiding glioblastoma treatment. This study aims to evaluate the effectiveness of a nomogram derived from histogram analysis of ultrafast ultrasound localization microscopy (ULM) for predicting IDH status.

Thirty-seven in situ glioblastoma rat models were established. Following craniotomy, microvascular morphology and hemodynamics were quantified using ultrafast ULM, and tumor regions were manually delineated via multimodal magnetic resonance imaging fusion registration. Tumors were classified as IDH-mutant or IDH-wildtype based on immunohistochemistry. A novel analytical approach based on an intensity histogram of the localization-density map was proposed to capture microvascular features within tumor regions. Univariate and multivariate logistic regression analyses were conducted to construct the nomogram, which was evaluated using calibration curves, decision curves, and receiver operating characteristic analysis.

No significant differences were found in conventional ULM parameters between the IDH-mutant and IDH-wildtype groups (p = 0.162-0.915). Significant intergroup differences were observed in histogram features, including mean (p = 0.047), standard deviation (p = 0.004), skewness (p = 0.027), coefficient of variation (p = 0.011), and solidity (p = 0.007). Multivariate analysis identified minimum (p = 0.009), width (p = 0.025), skewness (p = 0.010), and coefficient of variation (p = 0.001) as independent predictors of IDH status. The model demonstrated strong predictive performance, with an area under the curve of 0.936 and accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.876, 0.667, 0.934, 0.700, and 0.910, respectively. The Hosmer-Lemeshow test (χ² = 5.254, p = 0.730) confirmed a good fit between predicted and observed IDH status. Decision curve analysis confirmed the nomogram's clinical utility across mutation incidences ranging from 6% to 91%, indicating strong generalizability.

ULM-derived histogram parameters provide a reliable method for predicting IDH status.