Apparent Diffusion Coefficient (ADC) and Magnetic Resonance Imaging (MRI) Nomogram for Differentiating a Solitary Fibrous Tumor (World Health Organization Grade II) From an Angiomatous Meningioma.

INTRODUCTION

Accurate preoperative differentiation between intracranial solitary fibrous tumor (SFT, World Health Organization grade II) and angiomatous meningioma (AM) is crucial for surgical planning and prognosis prediction. While conventional magnetic resonance imaging (MRI) is widely used, distinguishing these tumors based on imaging alone remains challenging. This study aimed to evaluate clinical and MRI features to improve diagnostic accuracy between SFT and AM, focusing on the apparent diffusion coefficient (ADC) and conventional MRI parameters.

METHODS

A retrospective analysis was conducted on 51 patients (23 with SFT and 28 with AM) confirmed by pathology. Clinical and MRI characteristics were assessed using t-tests and chi-square tests. Logistic regression analysis was performed to identify independent predictors, and receiver operating characteristic (ROC) curve analysis evaluated diagnostic performance. A nomogram integrating ADC values with conventional MRI features was developed and validated using calibration curves.

RESULTS

Significant differences in tumor shape, cystic necrosis, T1-weighted imaging and T2-weighted imaging signal intensities, and ADC values were observed between SFT and AM (p < 0.05). Logistic regression analysis confirmed these factors as independent predictors, with ADC demonstrating the highest diagnostic performance at an optimal cutoff value of 1.08 × 10³ mm²/second. The ROC analysis showed that combining ADC with conventional MRI features improved diagnostic accuracy. The calibration curve demonstrated strong agreement between nomogram predictions and actual outcomes.

CONCLUSION

Integrating ADC values with clinical and MRI features provides a reliable method for differentiating intracranial SFT from AM. This approach enhances diagnostic precision, aiding in optimized clinical decision-making and surgical planning.