MRI Radiomics Signature of Pediatric Medulloblastoma Improves Risk Stratification Beyond Clinical and Conventional MR Imaging Features

• Published in: Journal of magnetic resonance imaging 2023-07, Vol.58 (1), p.236-246
• Main Authors: Zheng, Hui, Li, Jinning, Liu, Huanhuan, Ting, Gui, Yin, Qiufeng, Li, Rui, Liu, Ming, Zhang, Yuzhen, Duan, Shaofeng, Li, Yuhua, Wang, Dengbin
• Format: Article
• Language: English
• Subjects: Calibration; Cerebellar Neoplasms - diagnostic imaging; Child; Child, Preschool; Children; Cohort Studies; Correlation coefficient; Correlation coefficients; Customization; Decision analysis; Edema; Field strength; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medulloblastoma; Medulloblastoma - diagnostic imaging; overall survival; Pediatrics; Performance evaluation; Radiomics; Regression analysis; Regression models; Retrospective Studies; Risk; Risk Assessment; Statistical analysis; Statistical tests; Survival; Survival analysis
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.28537

Background Prognostic evaluation is important for personalized treatment in children with medulloblastoma (MB). Limited data are available for risk stratification using a radiomics‐based model. Purpose To evaluate the incremental value of an MRI radiomics signature in stratifying the risk of pediatric MB in terms of overall survival (OS). Study type Retrospective. Subjects A total of 111 children (mean age 5.82 years) with pathologically confirmed MB divided into training and validation cohorts (77 and 34 children, respectively). Field Strength/Sequence A 3 T, contrast‐enhanced T1‐weighted imaging with inversion recovery. Assessment The study endpoint was OS defined as the time between the preoperative MRI study and death or last follow‐up. The radiomics signature model and a clinical‐MRI model were developed for personalized OS prediction. An integrative model, which combined the radiomics signature and clinical‐MRI features, was also built using multivariable Cox regression model. The performance of the three models was evaluated with the C‐index. The performance of integrative model was assessed by calibration curve and decision curve analysis (DCA). Statistical Tests Independent T‐test, Mann–Whitney U test, Fisher's exact tests or chi‐square test, logistic regression analysis, Kaplan–Meier survival analysis, C‐index, intraclass correlation coefficients (ICC). P  0.05) and more benefit. Data Conclusions This study demonstrated that the integrative model, which combined radiomics signature, clinical, and conventional MRI features, showed best performance in OS evaluation for children with MB. The radiomics signature may confer incremental value over clinical‐MRI features. Evidence Level 3. Technical Efficacy Stage 2.