Application of CT image omics model in the differential diagnosis of ganglioneuroblastoma and neuroblastoma in childhood
10.3760/cma.j.cn115355-20240527-00272
- VernacularTitle:CT影像组学模型在儿童节细胞神经母细胞瘤和神经母细胞瘤鉴别诊断中的价值
- Author:
Haiyan LI
1
;
Zhiqiang LI
;
Wei ZHAO
;
Shuai QUAN
;
Siqi ZHANG
;
Shuming XU
Author Information
1. 山西省儿童医院（山西省妇幼保健院）CT室，太原 030013
- Keywords:
Imaging omics;
Child;
Ganglioneuroblastoma;
Neuroblastoma;
Diagnosis, differential
- From:
Cancer Research and Clinic
2024;36(11):858-862
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the application of CT image omics model in the differential diagnosis of ganglioneuroblastoma (GNB) and neuroblastoma (NB) in childhood.Methods:A retrospective case series study was performed. The clinical and imaging data of 23 NB and 23 GNB pediatric patients confirmed by surgery and pathology in Shanxi Children's Hospital from January 2013 to December 2013 were collected. The original CT images in the normal scan phase, arterial phase and venous phase of all the children before operation were extracted from the PACS system in DICOM format. ITK-SNAP (ver.3.4.0) software was applied to manually outline and extract the image omics features layer by layer of the lesions in the normal scan phase, arterial phase and venous phase of each patient before surgery. The minimum absolute contraction selection operator and stepwise multi-factor logistic regression method were used to screen out effective features in different scan phases. The corresponding phase image omics model was established by using logistic model. The diagnostic efficiency of each phase of the image omics model was evaluated by using the receiver operating characteristic curve, calibration curve and decision curve.Results:A total of 1 361 image omics features were extracted from the original CT images in the 3 phases. The model was established by using multi-factor logistic regression to extract 4 features in the normal scan phase, 2 features in the arterial phase, 3 features in the venous phase and 7 features in the combination of the 3 phases. The area under the curve (AUC) of the model in the normal scan phase was 0.940, the accuracy was 89.1%, the sensitivity was 91.3% and the specificity was 87.0%; the AUC of the model in the arterial phase was 0.923, the accuracy was 84.8%, the sensitivity was 82.6%, and the specificity was 87.0%; the AUC of the model in the venous phase was 0.949, the accuracy was 87.8%, the sensitivity was 83.3%, and the specificity was 91.3%; the AUC of 3 phases combined model was 0.964, the accuracy was 95.1%, the sensitivity was 94.7%, and the specificity was 95.5%. The results showed that the single-phase image omics model was effective in the differential diagnosis of NB and GNB in childhood; the AUC, accuracy, sensitivity and specificity of the 3 phases combined imaging model were higher than those of the single-phase imaging model. The calibration curve and decision curve showed that the probability of differential diagnosis of NB and GNB in childhood by the 3 phases combined model had a high consistency with the observed value, and a good net benefit could be achieved.Conclusions:CT-based image omics model has a high clinical value in the differential diagnosis of NB and GNB in childhood.
