兰州大学机构库
Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging
Han, Tao1,2,3,4; Liu, Xianwang1,2,3,4; Long, Changyou5; Xu, Zhendong6; Geng, Yayuan6; Zhang, Bin1,2,3,4; Deng, Liangna1,2,3,4; Jing, Mengyuan1,2,3,4; Zhou, Junlin1,3,4,7
2023-12
Online publication date2023-09
Source PublicationMAGNETIC RESONANCE IMAGING   Impact Factor & Quartile
ISSN0730-725X ; 1873-5894
EISSN1873-5894
Volume104Pages:16-22
page numbers7
AbstractPurpose: To explore the clinical value of a clinical radiomics model nomogram based on magnetic resonance imaging (MRI) for preoperative meningioma grading. Materials and methods: We collected retrospectively 544 patients with pathological diagnosis of meningiomas were categorized into training (n = 380) and validation (n = 164) groups at the ratio of 7 : 3. There were 3,376 radiomics features extracted from T2WI and T1C by shukun technology platform after manual segmentation using an independent blind method by two radiologists. The Selectpercentile and Lasso are used to filter the most strongly correlated features. Random forest (RF) radiomics model and clinical radiomics model nomogram were constructed respectively. The calibration, discrimination, and clinical validity were evaluated by using the calibration curve and decision analysis curve (DCA). Results: The RF radiomics model based on T1C and T2WI was the most effective to predict meningioma grade before surgery among the six different classifiers. The predictive ability of clinical radiomics model was slightly higher than that of RF model alone. The AUC, SEN, SPE, and ACC of the training set were 0.949, 0.976, 0.785, and 0.826, and the AUC, SEN, SPE, and ACC of the validation set were 0.838, 0.829, 0.783, and 0.793, respectively. The calibration curve and Hosmer-Lemeshow test showed the predictive probability of the fusion model was similar to the actual differentiated LGM and HGM. The analysis of the decision curve showed that the clinical radiomics model could obtain the best clinical net profit. Conclusions: The clinical radiomics model nomogram based on T1C and T2WI has high accuracy and sensitivity for predicting meningioma grade.
KeywordRadiomics Meningioma grade Magnetic resonance imaging Nomogram
PublisherELSEVIER SCIENCE INC
DOI10.1016/j.mri.2023.09.002
Indexed BySCIE
Language英语
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:001081551700001
Original Document TypeArticle
PMID 37734573
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/568855
Collection兰州大学
Corresponding AuthorZhou, Junlin
Affiliation
1.Lanzhou Univ, Dept Radiol, Hosp 2, Lanzhou 730030, Peoples R China;
2.Lanzhou Univ, Clin Sch 2, Lanzhou 730030, Peoples R China;
3.Key Lab Med Imaging Gansu Prov, Lanzhou 730030, Peoples R China;
4.Gansu Int Sci & Technol Cooperat Base Med Imaging, Lanzhou 730030, Peoples R China;
5.Qinghai Univ, Affiliated Hosp, Image Ctr, Xining, Peoples R China;
6.Shukun Beijing Technol Co Ltd, Jinhui Bd,Qiyang Rd, Beijing 100102, Peoples R China;
7.Lanzhou Univ, Dept Radiol, Hosp 2, Lanzhou, Peoples R China
Recommended Citation
GB/T 7714
Han, Tao,Liu, Xianwang,Long, Changyou,et al. Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging[J]. MAGNETIC RESONANCE IMAGING,2023,104:16-22.
APA Han, Tao.,Liu, Xianwang.,Long, Changyou.,Xu, Zhendong.,Geng, Yayuan.,...&Zhou, Junlin.(2023).Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging.MAGNETIC RESONANCE IMAGING,104,16-22.
MLA Han, Tao,et al."Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging".MAGNETIC RESONANCE IMAGING 104(2023):16-22.
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