Si Jiajun, Wang Haoru, Xie Mingye, Yang Yanlin, Li Jun, Wang Fang, Chen Xin, He Ling
Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China.
Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Quant Imaging Med Surg. 2024 Aug 1;14(8):5630-5641. doi: 10.21037/qims-24-122. Epub 2024 Jul 30.
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.
淋巴瘤是儿童常见的恶性肿瘤。淋巴瘤的病理亚型高度复杂，治疗方案各异。不同病理亚型的淋巴瘤在计算机断层扫描（CT）图像上无显著差异。由于淋巴瘤是一种血液系统疾病，淋巴瘤患者的脾脏常出现异常，因此本研究的目的是通过提取CT图像中脾脏的影像组学特征，构建区分伯基特淋巴瘤（BL）和淋巴母细胞淋巴瘤的模型。这可以提供一种有效、无创的方法，用于区分小儿淋巴瘤患者的常见病理亚型。
回顾性分析48例淋巴母细胞淋巴瘤患者和61例BL患者的临床资料和影像资料。通过完全随机化将数据集分为训练集（n = 76）和测试集（n = 33）。分别在非增强、动脉期和静脉期从CT图像中提取脾脏的影像组学特征。整合这些特定时期的特征以构建融合模型。采用二次判别分析（QDA）、逻辑回归（LR）和支持向量机（SVM）三种分类器构建模型。
融合模型表现出优于单个模型的性能。在训练集或测试集中，由QDA和LR构建的融合模型之间无显著差异。在使用SVM分类器构建的四个融合模型中，SVM_4是表现最佳的模型。SVM_4模型在训练集中的曲线下面积、灵敏度、特异性和F1分数分别为0.967 [95%置信区间（CI）：0.935 - 0.998]、0.86、0.97和0.913，在测试集中分别为0.754（95% CI：0.584 - 0.924）、0.611、0.867和0.71。
脾脏的影像组学特征显示出区分小儿患者中两种最常见淋巴瘤亚型的能力。这种无创方法有望实现高效、准确的鉴别。
