The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma

被引:2
作者
Chen, Xin [1 ]
Wang, Haoru [1 ]
Xia, Yuwei [2 ]
Shi, Feng [2 ]
He, Ling [1 ]
Liu, Enmei [3 ]
机构
[1] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol,Childrens Hosp,Chongqing Key Lab Child, Minist Educ Key Lab Child Dev & Disorders, Chongqing 400014, Peoples R China
[2] Shanghai United Imaging Intelligence Co Ltd, Shanghai 200030, Peoples R China
[3] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Resp Med,Childrens Hosp,Chongqing Key Lab Chi, Minist Educ Key Lab Child Dev & Disorders, Chongqing 400014, Peoples R China
关键词
Neuroblastoma; Computed tomography; Radiomics; Mitosis karyorrhexis index; PATHOLOGY CLASSIFICATION;
D O I
10.1007/s12672-024-01067-0
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Objective Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma.Materials and methods 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status.Results The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively.Conclusions CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.
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页数:13
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