小血管前纵隔结节（SPMN）的术前诊断是一个挑战，常常导致不必要的手术干预。我们的目标是根据术前 CT 放射组学特征开发列线图，作为 SPMN 的无创诊断工具。对 2018 年 1 月至 2022 年 12 月期间来自两个医疗中心的手术切除 SPMN 的患者进行回顾性分析。从术前 CT 图像中提取并筛选放射组学特征。采用逻辑回归建立临床、放射组学和混合模型，用于区分胸腺上皮肿瘤（TET）和囊肿。这些模型的性能在内部和外部测试集中通过受试者工作特征曲线 (AUC) 下的面积进行了验证，同时还将其诊断能力与人类专家进行了比较。该研究总共招募了 363 名患者（中位年龄为 53 岁） [IQR：45-59 岁]；175 [48.2%] 男性）用于模型开发和验证，包括 136 个 TET 和 227 个囊肿。病变的强化状态、形状、钙化和拉德评分被确定为独立的区分因素。与其他模型和人类专家相比，混合模型表现出卓越的诊断性能，AUC 分别为 0.95（95% CI：0.92-0.98）、0.94（95% CI：0.89-0.99）和 0.93（95% CI：0.83- 1.00）分别在训练集、内部测试集和外部测试集中。该模型的校准曲线显示出良好的拟合度，而决策曲线分析则强调了其临床价值。基于放射组学的列线图有效地区分了最常见的 SPMN 类型，即 TET 和囊肿，从而为治疗指导提供了一种有前景的工具。版权所有 © 2024 年大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

The preoperative diagnosis of small prevascular mediastinal nodules (SPMNs) presents a challenge, often leading to unnecessary surgical interventions. Our objective was to develop a nomogram based on preoperative CT-radiomics features, serving as a non-invasive diagnostic tool for SPMNs.Patients with surgically resected SPMNs from two medical centers between January 2018 and December 2022 were retrospectively reviewed. Radiomics features were extracted and screened from preoperative CT images. Logistic regression was employed to establish clinical, radiomics, and hybrid models for differentiating thymic epithelial tumors (TETs) from cysts. The performance of these models was validated in both internal and external test sets by area under the receiver operating characteristic curve (AUC), while also comparing their diagnostic capability with human experts.The study enrolled a total of 363 patients (median age, 53 years [IQR:45-59 years]; 175 [48.2%] males) for model development and validation, including 136 TETs and 227 cysts. Lesions' enhancement status, shape, calcification, and rad-score were identified as independent factors for distinction. The hybrid model demonstrated superior diagnostic performance compared to other models and human experts, with an AUC of 0.95 (95% CI:0.92-0.98), 0.94 (95% CI:0.89-0.99), and 0.93 (95% CI:0.83-1.00) in the training set, internal test set, and external test set respectively. The calibration curve of the model demonstrated excellent fit, while decision curve analysis underscored its clinical value.The radiomics-based nomogram effectively discriminates between the most prevalent types of SPMNs, namely TETs and cysts, thus presenting a promising tool for treatment guidance.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.