本研究的目的是开发一种医学成像和基于综合堆叠学习的方法来预测高风险和低风险胸腺瘤。回顾性纳入我院收治的126例胸腺瘤患者和5例胸腺癌患者，其中低危患者65例，高危患者66例。其中，78名患者构成训练队列，其余53名患者构成验证队列。我们从患者的动脉、静脉和平相图像中分别提取了 1702 个特征。这些特征的成对相减分别产生 1702 个动脉-静脉、动脉-平原和静脉-平原差异特征。采用 Mann-Whitney U 测试、最小绝对收缩和选择算子 (LASSO) 以及 SelectKBest 方法从训练集中选择最佳特征。使用堆叠学习算法构建了六个模型。通过应用堆叠集成学习，XGBoost 结合三种机器学习算法（XGBoost、多层感知器（MLP）和随机森林）来产生六种基本成像模型。然后，将XGBoost算法应用于六种基本成像模型，构建组合放射组学模型。最后，将放射组学模型与临床信息相结合，创建列线图，可以轻松地用于临床实践来预测胸腺瘤风险类别。训练和验证队列中组合放射组学模型的曲线下面积 (AUC) 分别为 0.999 (95% CI 0.988-1.000) 和 0.967 (95% CI 0.916-1.000)，而列线图的曲线下面积 (AUC) 分别为 0.999 (95% CI 0.988-1.000) 和 0.967 (95% CI 0.916-1.000)。 95% CI 0.996-1.000) 和 0.983 (95% CI 0.990-1.000)。本研究描述了基于 CT 的放射组学在胸腺瘤患者中的应用，并提出了用于预测该疾病风险类别的列线图，这可能有利于受影响患者的临床决策。© 2024。作者。

The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A total of 126 patients with thymomas and 5 patients with thymic carcinoma treated at our institution, including 65 low-risk patients and 66 high-risk patients, were retrospectively recruited. Among them, 78 patients composed the training cohort, while the remaining 53 patients formed the validation cohort. We extracted 1702 features each from the patients' arterial-, venous-, and plain-phase images. Pairwise subtraction of these features yielded 1702 arterial-venous, arterial-plain, and venous-plain difference features each. The Mann‒Whitney U test and least absolute shrinkage and selection operator (LASSO) and SelectKBest methods were employed to select the best features from the training set. Six models were built with a stacked learning algorithm. By applying stacked ensemble learning, three machine learning algorithms (XGBoost, multilayer perceptron (MLP), and random forest) were combined by XGBoost to produce the the six basic imaging models. Then, the XGBoost algorithm was applied to the six basic imaging models to construct a combined radiomic model. Finally, the radiomic model was combined with clinical information to create a nomogram that could easily be used in clinical practice to predict the thymoma risk category. The areas under the curve (AUCs) of the combined radiomic model in the training and validation cohorts were 0.999 (95% CI 0.988-1.000) and 0.967 (95% CI 0.916-1.000), respectively, while those of the nomogram were 0.999 (95% CI 0.996-1.000) and 0.983 (95% CI 0.990-1.000). This study describes the application of CT-based radiomics in thymoma patients and proposes a nomogram for predicting the risk category for this disease, which could be advantageous for clinical decision-making for affected patients.© 2024. The Author(s).