Details

Autor(en) / Beteiligte

• Wagner, Matthias W
• So, Delvin
• Guo, Ting
• Erdman, Lauren
• Sheng, Min
• Ufkes, S
• Grunau, Ruth E
• Synnes, Anne
• Branson, Helen M
• Chau, Vann
• Shroff, Manohar M
• Ertl-Wagner, Birgit B
• Miller, Steven P

Titel

MRI based radiomics enhances prediction of neurodevelopmental outcome in very preterm neonates

Ist Teil von

• Scientific reports, 2022-07, Vol.12 (1), p.11872-11872, Article 11872

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2022

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Abstract To predict adverse neurodevelopmental outcome of very preterm neonates. A total of 166 preterm neonates born between 24–32 weeks’ gestation underwent brain MRI early in life. Radiomics features were extracted from T1- and T2- weighted images. Motor, cognitive, and language outcomes were assessed at a corrected age of 18 and 33 months and 4.5 years. Elastic Net was implemented to select the clinical and radiomic features that best predicted outcome. The area under the receiver operating characteristic (AUROC) curve was used to determine the predictive ability of each feature set. Clinical variables predicted cognitive outcome at 18 months with AUROC 0.76 and motor outcome at 4.5 years with AUROC 0.78. T1-radiomics features showed better prediction than T2-radiomics on the total motor outcome at 18 months and gross motor outcome at 33 months (AUROC: 0.81 vs 0.66 and 0.77 vs 0.7). T2-radiomics features were superior in two 4.5-year motor outcomes (AUROC: 0.78 vs 0.64 and 0.8 vs 0.57). Combining clinical parameters and radiomics features improved model performance in motor outcome at 4.5 years (AUROC: 0.84 vs 0.8). Radiomic features outperformed clinical variables for the prediction of adverse motor outcomes. Adding clinical variables to the radiomics model enhanced predictive performance.