Ying Yu, Yahya Noorazrul, Abdul Manan Hanani
Department of Interventional Radiology, University Kebangsaan Malaysia Medical Centre, Kuala Lumpur, MYS.
Department of Diagnostic Imaging and Radiotherapy, Centre for Diagnostic, Therapeutic and Investigative Sciences, Faculty of Health Sciences, National University of Malaysia, Kuala Lumpur, MYS.
Cureus. 2025 Feb 22;17(2):e79470. doi: 10.7759/cureus.79470. eCollection 2025 Feb.
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.
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.
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.
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.
准确术前鉴别颅内孤立性纤维瘤（SFT，世界卫生组织二级）和血管母细胞瘤性脑膜瘤（AM）对于手术规划和预后预测至关重要。虽然传统磁共振成像（MRI）被广泛应用，但仅基于影像学来区分这些肿瘤仍然具有挑战性。本研究旨在评估临床和MRI特征，以提高SFT和AM之间的诊断准确性，重点关注表观扩散系数（ADC）和传统MRI参数。
对51例经病理确诊的患者（23例SFT和28例AM）进行回顾性分析。使用t检验和卡方检验评估临床和MRI特征。进行逻辑回归分析以确定独立预测因素，并通过受试者工作特征（ROC）曲线分析评估诊断性能。开发了一个将ADC值与传统MRI特征相结合的列线图，并使用校准曲线进行验证。
在SFT和AM之间观察到肿瘤形状、囊性坏死、T1加权成像和T2加权成像信号强度以及ADC值存在显著差异（p < 0.05）。逻辑回归分析确认这些因素为独立预测因素，ADC在最佳截断值为1.08×10³mm²/秒时显示出最高的诊断性能。ROC分析表明，将ADC与传统MRI特征相结合可提高诊断准确性。校准曲线显示列线图预测与实际结果之间具有高度一致性。
将ADC值与临床和MRI特征相结合为区分颅内SFT和AM提供了一种可靠的方法。这种方法提高了诊断精度，有助于优化临床决策和手术规划。
