Spatial radiomics model for identifying supratentorial pilocytic astrocytoma and ganglioglioma based on MRI
10.3760/cma.j.cn112149-20240524-00289
- VernacularTitle:基于MRI的空间影像组学模型鉴别幕上毛细胞型星形细胞瘤与节细胞胶质瘤的价值
- Author:
Tianliang ZHAN
1
;
Jianrui LI
;
Qiang XU
;
Zhizheng ZHUO
;
Junjie LI
;
Haohui CHEN
;
Ya'ou LIU
;
Zhiqiang ZHANG
Author Information
1. 南京中医药大学金陵临床医学院放射诊断科，南京 210002
- Keywords:
Astrocytoma;
Ganglioglioma;
Magnetic resonance imaging;
Location;
Radiomics
- From:
Chinese Journal of Radiology
2024;58(12):1381-1387
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a spatial radiomics model based on the spatial distribution characteristics of supratentorial pilocytic astrocytoma (PA) and ganglioglioma (GG) and to evaluate its differential diagnosis efficiency.Methods:The study was a cross-sectional study. A retrospective collection of 244 patients with episodic PA and GG who attended Beijing Tiantan Hospital of Capital Medical University (Center 1) from June 2016 to June 2022 and 116 patients with episodic PA and GG who attended General Hospital of Eastern Theater Command (Center 2) from March 2019 to October 2022 was performed. The patients in Center 1 were divided into a training set (171 patients) and a validation set (73 patients) in a 7∶3 ratio according to the random number table method, and the patients in Center 2 as a whole were regarded as test sets. All patients underwent MRI. Segmentation of tumor based on enhanced T 1WI and T 2WI images, alignment to standard space to generate a statistical parametric mapping of tumor locations and intergroup comparison was conducted. The Johns Hopkins University template was used to extract 189 tumor location features to construct a spatial model of tumor location; PyRadiomic 3.0.1 software was used to extract tumor radiomics features to construct a radiomics model; and the two models were fused to construct a spatial radiomics model. The efficacy of spatial radiomics model, spatial model, and radiomics model to discriminate PA from GG was analyzed using receiver operating characteristic curves and area under the curve (AUC). The generalization ability of the model was assessed by the difference in accuracy between the test sets and the validation sets (ΔACC). The clinical utility of the model was compared using clinical decision curves and calibration curves. Results:The statistical parametric mapping of lesions showed that supratentorial PA was vulnerable to medial structure areas such as suprasellar region, thalamus, basal ganglia and frontal lobe, temporal lobe, parietal lobe. GG was mainly distributed in bilateral temporal lobes, as well as frontal lobe, occipital lobe and parietal lobe. The AUCs of spatial radiomics model, radiomics model and spatial model to identify PA and GG in the test set were 0.876, 0.785, and 0.819, with accuracies of 77.59%, 72.41%, and 77.14%, respectively, and ΔACCs in the test set and validation set were 11.6%, 15.43%, and 6.94%, respectively. The clinical decision curves showed an overall greater clinical benefit of the spatial radiomics model compared with the conventional radiomics model and spatial model.Conclusion:Spatial radiomics model containing spatial information on lesion location can improve the diagnostic efficacy of supratentorial PA and GG, and enhance the generalization of the prediction model.
