评估基于磁共振成像 (MRI) 的深度学习放射组学列线图 (DLRN) 在区分颅内孤立性纤维瘤 (ISFT) 与血管瘤性脑膜瘤 (AM) 以及预测 ISFT 患者总生存期 (OS) 方面的价值。首都医科大学附属北京天坛医院的 1090 名患者和兰州大学第二医院的 131 名患者分别被分为主要队列（PC）和外部验证队列（EVC）。在 PC 中开发了基于 MRI 的 DLRN，以区分 ISFT 和 AM。我们验证了 DLRN 并将其与 EVC 中的临床模型 (CM) 进行比较。总共有 149 名 ISFT 患者接受了随访。我们对 DLRN 评分、临床特征和组织学分层进行了 Cox 回归分析。此外，我们使用 Kaplan-Meier 曲线评估了独立危险因素与随访患者 OS 之间的关联。DLRN 在区分 ISFT 和 AM 方面优于 CM（曲线下面积 [95% 置信区间 (CI)]：0.86 [在 EVC 中，DLRN 为 0.84-0.88]，CM 为 0.70 [0.67-0.72]，p < 0.001)。 DLRN 评分高的患者[每增加 1 分；风险比 (HR) 1.079，95% CI：1.009-1.147，p = 0.019] 和次全切除 (STR) [每增加 1 次； HR 2.573，95% CI：1.337-4.932，p = 0.004]与较短的 OS 相关。高 DLRN 评分组和低 DLRN 评分组之间的 OS 存在统计学显着差异，截止值为 12.19 (p<0.001)。全切除 (GTR) 组和 STR 组之间的 OS 也存在差异 (p < 0.001)。所提出的 DLRN 在区分 ISFT 和 AM 方面优于 CM，并且可以预测 ISFT 患者的 OS。所提出的基于 MRI 的深度学习放射组学列线图在区分 ISFT 和 AM 方面优于临床模型，并且可以预测 ISFT 患者的 OS，这可以指导手术策略并预测患者的预后。根据传统放射学体征区分 ISFT 和 AM 具有挑战性。在我们的研究中，DLRN 的表现优于 CM。 DLRN 可以预测 ISFT 患者的 OS。© 2024。作者，获得欧洲放射学会的独家许可。

To evaluate the value of a magnetic resonance imaging (MRI)-based deep learning radiomic nomogram (DLRN) for distinguishing intracranial solitary fibrous tumors (ISFTs) from angiomatous meningioma (AMs) and predicting overall survival (OS) for ISFT patients.In total, 1090 patients from Beijing Tiantan Hospital, Capital Medical University, and 131 from Lanzhou University Second Hospital were categorized as primary cohort (PC) and external validation cohort (EVC), respectively. An MRI-based DLRN was developed in PC to distinguish ISFTs from AMs. We validated the DLRN and compared it with a clinical model (CM) in EVC. In total, 149 ISFT patients were followed up. We carried out Cox regression analysis on DLRN score, clinical characteristics, and histological stratification. Besides, we evaluated the association between independent risk factors and OS in the follow-up patients using Kaplan-Meier curves.The DLRN outperformed CM in distinguishing ISFTs from AMs (area under the curve [95% confidence interval (CI)]: 0.86 [0.84-0.88] for DLRN and 0.70 [0.67-0.72] for CM, p < 0.001) in EVC. Patients with high DLRN score [per 1 increase; hazard ratio (HR) 1.079, 95% CI: 1.009-1.147, p = 0.019] and subtotal resection (STR) [per 1 increase; HR 2.573, 95% CI: 1.337-4.932, p = 0.004] were associated with a shorter OS. A statistically significant difference in OS existed between the high and low DLRN score groups with a cutoff value of 12.19 (p < 0.001). There is also a difference in OS between total excision (GTR) and STR groups (p < 0.001).The proposed DLRN outperforms the CM in distinguishing ISFTs from AMs and can predict OS for ISFT patients.The proposed MRI-based deep learning radiomic nomogram outperforms the clinical model in distinguishing ISFTs from AMs and can predict OS of ISFT patients, which could guide the surgical strategy and predict prognosis for patients.Distinguishing ISFTs from AMs based on conventional radiological signs is challenging. The DLRN outperformed the CM in our study. The DLRN can predict OS for ISFT patients.© 2024. The Author(s), under exclusive licence to European Society of Radiology.