淋巴瘤是儿童常见的恶性肿瘤。淋巴瘤的病理亚型非常复杂，治疗方案也各不相同。淋巴瘤的不同病理亚型在计算机断层扫描（CT）图像上没有显着差异。由于淋巴瘤是一种血液系统疾病，因此淋巴瘤患者经常出现脾脏异常，因此本研究的目的是通过从 CT 中提取脾脏的放射组学特征来构建区分伯基特淋巴瘤（BL）与淋巴母细胞淋巴瘤的模型图像。这为区分小儿淋巴瘤常见病理亚型提供了一种高效、无创的方法。对48例淋巴母细胞淋巴瘤和61例BL患者的临床资料和影像学资料进行回顾性分析。通过完全随机化将数据集分为训练集（n=76）和测试集（n=33）。从非对比增强、动脉期和静脉期的 CT 图像中分别提取脾脏的放射组学特征。这些阶段特定的特征被集成以构建融合模型。采用二次判别分析（QDA）、逻辑回归（LR）和支持向量机（SVM）三种分类器来构建模型。与单个模型相比，融合模型表现出优越的性能。 QDA和LR构建的融合模型无论在训练集还是测试集都没有显着差异。在使用 SVM 分类器构建的四个融合模型中，SVM_4 成为性能最佳的模型。 SVM_4 模型的曲线下面积、灵敏度、特异性和 F1 分数在训练集中分别为 0.967 [95% 置信区间 (CI)：0.935-0.998]、0.86、0.97 和 0.913，以及 0.754（测试集中的 95% CI：0.584-0.924）、0.611、0.867 和 0.71。脾脏的放射组学特征表明能够区分儿科患者中两种最常见的淋巴瘤亚型。这种非侵入性方法有望实现高效、准确的辨别。2024 年医学和外科定量成像。版权所有。

Lymphoma is a common malignant tumor in children. The pathologic subtyping of lymphoma is high complex, and the treatment options vary. The different pathologic subtypes of lymphomas have no significant differences on computed tomography (CT) images. As it is a hematologic disease, patients with lymphoma often show abnormalities in the spleen, and so the aim of this study was to construct a model for differentiating Burkitt lymphoma (BL) from lymphoblastic lymphoma through the extraction of radiomic features of the spleen from CT images. This could provide an efficient, noninvasive method that can differentiate the common pathological subtypes in patients with pediatric lymphoma.The clinical data and imaging data of 48 patients with lymphoblastic lymphoma and 61 patients with BL were retrospectively analyzed. The dataset was divided into a training set (n=76) and a test set (n=33) through complete randomization. Radiomics features of the spleen were separately extracted from CT images in the noncontrast enhanced, arterial, and venous phases. These phase-specific features were integrated to construct fusion models. Three classifiers, quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM), were employed to build the models.The fusion model exhibited superior performance compared to individual models. There was no significant difference between the fusion models constructed by QDA and LR in either the training set or the test set. Among the four fusion models constructed with the SVM classifier, SVM_4 emerged as the best performing model. The area under the curve, sensitivity, specificity, and F1-score of the SVM_4 model were 0.967 [95% confidence interval (CI): 0.935-0.998], 0.86, 0.97, and 0.913 in the training set, respectively, and 0.754 (95% CI: 0.584-0.924), 0.611, 0.867, and 0.71 in the test set, respectively.The radiomics features of the spleen demonstrated the capability to distinguish between the two most common lymphoma subtypes in pediatric patients. This noninvasive approach holds promise for efficient and accurate discrimination.2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.