Magnetic resonance imaging-based radiomics for predicting infiltration levels of CD68+ tumor-associated macrophages in glioblastomas.

ABSTRACT

PURPOSE: Tumor-associated macrophages (TAMs) are important biomarkers of tumor invasion and prognosis in patients with glioblastoma. We combined the imaging and radiomics features of preoperative MRI to predict CD68+ macrophage infiltration. METHODS: Clinical, MRI image, and pathology data of 188 patients with glioblastoma were analyzed. Overall, 143 patients were included in the training (n = 101) and validation (n = 42) sets, whereas 45 patients were included in an independent test set. The optimal cut-off value (14.8%) was based on the minimum p-value formed by the Kaplan-Meier survival analysis and log-rank tests which divided patients into groups with high CD68+ TAMs (≥ 14.8%) and low CD68+ TAMs (< 14.8%). Regions of interest and radiomics features extraction were based on contrast-enhanced T1-weighted images (CE-T1WI) and T2WI. Multi-parameter stepwise regression was used to create the clinical, radiomics, and combined models, each evaluated using the receiver operating characteristic curve. Decision curve analysis was used to assess the clinical applicability of the nomogram. RESULTS: A clinical model based on the minimum apparent diffusion coefficient (ADCmin) revealed an area under the curve (AUC) of 0.768, 0.764, and 0.624 for the training set, validation set, and test set, respectively. The 2D radiomics model, based on two features, revealed an AUC of 0.783, 0.724, and 0.789 for the training, validation, and test sets, respectively. The 3D radiomics model, based on three features, revealed AUCs of 0.823, 0.811, and 0.787 for the training, validation, and test sets, respectively. The combined model, with ADCmin and radiomics features, showed the best performance, with AUCs of 0.865, 0.822, and 0.776 for the training, validation, and test sets, respectively. The calibration curve of the combined model nomogram showed good agreement between the estimated and actual probabilities. CONCLUSION: The combined model constructed using ADCmin, a quantitative imaging parameter, combined with five key radiomics features can be used to evaluate the extent of CD68+ macrophages before surgery.