Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters

作者全名："Wang, Yuanlin; Wang, Longlun; Qin, Bin; Hu, Xihong; Xiao, Wenjiao; Tong, Zanyong; Li, Shuang; Jing, Yang; Li, Lusheng; Zhang, Yuting"

作者地址："[Wang, Yuanlin; Wang, Longlun; Qin, Bin; Xiao, Wenjiao; Tong, Zanyong; Li, Shuang; Zhang, Yuting] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol, Minist Educ,Key Lab Child Dev & Disorders,Children, Chongqing, Peoples R China; [Hu, Xihong] Fudan Univ, Dept Radiol, Childrens Hosp, Shanghai, Peoples R China; [Jing, Yang] Huiying Med Technol Co Ltd, Beijing, Peoples R China; [Li, Lusheng] Chongqing Med Univ, Childrens Hosp, Dept Neurosurg, Minist Educ,Key Lab Child Dev & Disorders,Chongqin, Chongqing, Peoples R China"

通信作者："Zhang, YT (通讯作者)，Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol, Minist Educ,Key Lab Child Dev & Disorders,Children, Chongqing, Peoples R China.; Li, LS (通讯作者)，Chongqing Med Univ, Childrens Hosp, Dept Neurosurg, Minist Educ,Key Lab Child Dev & Disorders,Chongqin, Chongqing, Peoples R China."

来源：FRONTIERS IN NEUROSCIENCE

ESI学科分类：NEUROSCIENCE & BEHAVIOR

WOS号：WOS:000975082800001

JCR分区：Q2

影响因子：3.2

年份：2023

卷号：17

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：medulloblastoma; radiomics; molecular subtypes; machine learning; prediction models

摘要："PurposeTo construct a machine learning model based on radiomics of multiparametric magnetic resonance imaging (MRI) combined with clinical parameters for predicting Sonic Hedgehog (SHH) and Group 4 (G4) molecular subtypes of pediatric medulloblastoma (MB). MethodsThe preoperative MRI images and clinical data of 95 patients with MB were retrospectively analyzed, including 47 cases of SHH subtype and 48 cases of G4 subtype. Radiomic features were extracted from T1-weighted imaging (T1), contrast-enhanced T1 weighted imaging (T1c), T2-weighted imaging (T2), T2 fluid-attenuated inversion recovery imaging (T2FLAIR), and apparent diffusion coefficient (ADC) maps, using variance thresholding, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The optimal features were filtered using LASSO regression, and a logistic regression (LR) algorithm was used to build a machine learning model. The receiver operator characteristic (ROC) curve was plotted to evaluate the prediction accuracy, and verified by its calibration, decision and nomogram. The Delong test was used to compare the differences between different models. ResultsA total of 17 optimal features, with non-redundancy and high correlation, were selected from 7,045 radiomics features, and used to build an LR model. The model showed a classification accuracy with an under the curve (AUC) of 0.960 (95% CI: 0.871-1.000) in the training cohort and 0.751 (95% CI: 0.587-0.915) in the testing cohort, respectively. The location of the tumor, pathological type, and hydrocephalus status of the two subtypes of patients differed significantly (p < 0.05). When combining radiomics features and clinical parameters to construct the combined prediction model, the AUC improved to 0.965 (95% CI: 0.898-1.000) in the training cohort and 0.849 (95% CI: 0.695-1.000) in the testing cohort, respectively. There was a significant difference in the prediction accuracy, as measured by AUC, between the testing cohorts of the two prediction models, which was confirmed by Delong's test (p = 0.0144). Decision curves and nomogram further validate that the combined model can achieve net benefits in clinical work. ConclusionThe combined prediction model, constructed based on radiomics of multiparametric MRI and clinical parameters can potentially provide a non-invasive clinical approach to predict SHH and G4 molecular subtypes of MB preoperatively."

基金机构：Chongqing Medical Scientific Research Project (Joint Project of Chongqing Health Commission and Science and Technology Bureau) [2023ZDXM023]

基金资助正文：This study was supported by the grants from Chongqing Medical Scientific Research Project (Joint Project of Chongqing Health Commission and Science and Technology Bureau) (No. 2023ZDXM023).