Intratumoral and Peritumoral Multiparametric MRI-Based Radiomics Signature for Preoperative Prediction of Ki-67 Proliferation Status in Glioblastoma: A Two-Center Study

Rationale and Objectives: To assess the predictive ability of intratumoral and peritumoral multiparametric magnetic resonance imaging (MRI)-based radiomics signature (RS) for preoperative prediction of Ki-67 proliferation status in glioblastoma. Materials and Methods: A total of 205 patients with glioblastoma at two institutions were retrospectively analyzed. Data from institution 1 ( n = 158) were used to develop the predictive model, and as an internal test dataset, data from institution 2 ( n = 47) constitute the external test dataset. Feature selection was performed using spearman correlation coefficient, univariate ranking method, and the least absolute shrinkage and selection operator algorithm. RSs were established using a logistic regression algorithm. The predictive performance of the RSs was assessed using calibration curve, decision curve analysis (DCA), and area under the curve (AUC). Results: In the RSs based on single-parametric (contrast-enhanced T1-weighted image, T2-weighted image, or apparent diffusion coefficient maps), the AUCs of intratumoral, peritumoral, and combined area (intratumoral and peritumoral) were 0.60-0.67, with no significant difference among them. The RSs that using multiparametric features (integrating the previously mentioned three sequences) showed improved AUC compared to the single-parametric RSs; AUC reached 0.75-0.89. Among them, the multiparametric RS based on radiomics features of the combined area (Multi-Com) exhibited the highest performance, with an internal test dataset AUC of 0.89 (95% confidence interval (CI) 0.75-1.00) and an external test dataset AUC of 0.88 (95% CI 0.78-0.97). The calibration curve and DCA display RS (Multi-Com) have good calibration ability and clinical applicability. Conclusion: The multiparametric MRI-based RS combining intratumoral and peritumoral features can serve as a noninvasive and effective tool for preoperative assessment of Ki-67 proliferation status in glioblastoma.