The value of radiomics features of the spleen as surrogates for differentiating subtypes of common pediatric lymphomas

作者全名："Si, Jiajun; Wang, Haoru; Xie, Mingye; Yang, Yanlin; Li, Jun; Wang, Fang; Chen, Xin; He, Ling"

作者地址："[Si, Jiajun; Wang, Haoru; Xie, Mingye; Yang, Yanlin; Li, Jun; Chen, Xin; He, Ling] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Chongqing Engn Res Ctr Stem Cell Therapy, Minist Educ,Key Lab Child Dev & Disorders,Children, 1 Med Coll Rd, Chongqing 400016, Peoples R China; [Wang, Fang] Shanghai United Imaging Intelligence Co Ltd, Dept Res & Dev, Shanghai, Peoples R China"

通信作者："He, L (通讯作者)，Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Chongqing Engn Res Ctr Stem Cell Therapy, Minist Educ,Key Lab Child Dev & Disorders,Children, 1 Med Coll Rd, Chongqing 400016, Peoples R China."

来源：QUANTITATIVE IMAGING IN MEDICINE AND SURGERY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001312970900006

JCR分区：Q2

影响因子：2.8

年份：2024

卷号：14

期号：8

开始页：　

结束页：　

文献类型：Article

关键词：Lymphoma; machine learning; radiomics; spleen; children

摘要："Background: 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. Methods: 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. Results: 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. Conclusions: 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."

基金机构：National Natural Science Foundation of Chongqing [[CSTB]2023NSCQ-BHX0127]

基金资助正文：This study was supported by the National Natural Science Foundation of Chongqing (No. [CSTB] 2023NSCQ-BHX0127) .