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| Application of Diffusion Weighted Imaging Imagomics in the Grading Diagnosis of Intracranial Tumors in Children |
| MA Qiuhong, YI Ting, LIU Yuqing, et al |
| Department of Radiology,The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University (Hunan children's hospital), Changsha Hunan 410007 |
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Abstract 【Objective】To explore the value of diffusion weighted imaging (DWI) based imaging-omics in the differential diagnosis of low-grade gliomas (LGG) and high-grade gliomas (HGG) in children. 【Methods】A total of 92 children with intracranial tumors who underwent surgical treatment in our hospital from January 2015 to April 2021 were retrospectively analyzed and divided into low-grade gliomas group (LGG group) (grade Ⅰ-Ⅱ, n=53) and high-grade gliomas group (HGG group) (grade Ⅲ-Ⅳ, n=39) according to tumor grade. DWI examination was performed on both groups to observe their image features. MRMicro GL software was used to extract lesion features, and ITK-SNAP software was used to extract and evaluate the image omics features, which had diagnostic value and were significantly correlated with tumor grade. Receiver operating characteristic (ROC) curve was used to evaluate the value of ADC, DWI alone and ADC+DWI in the diagnosis of intracranial tumors. 【Results】108 image features were selected from ADC and DWI images for dimensionality reduction screening and model evaluation. The ADC model area under the curve (AUC) was 0.7865, and the diagnostic sensitivity and specificity were 1.0000 and 0.5000, respectively. The AUC of DWI model was 0.7135, the diagnostic sensitivity and specificity were 0.4375 and 1.0000, respectively, and the correlation coefficients were generally consistent. The AUC of ADC+DWI model was 0.9479, the correlation coefficient was consistent well, and the diagnostic efficiency was the best (P<0.05). 【Conclusion】ADC, DWI and ADC+DWI models can identify intracranial tumors in children, and ADC+DWI models have high efficiency in differentiating high and low grade gliomas, which can improve the diagnostic performance of the models and help to guide clinicians to make surgery and treatment plans.
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Received: 11 August 2023
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2025, Vol. 42 
