Youwon Shin1, Yoonho Nam2,3, Taehoon Shin1,4, Jin Wook Choi5, Jang Hoon Lee6, Da Eun Jung6, Jiseon Lim3, Hyun Gi Kim7,8. 1. Division of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul, Republic of Korea. 2. Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. 3. Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Republic of Korea. 4. Department of Medicine, Case Western Reserve University, Cleveland, OH, USA. 5. Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, Suwon, Republic of Korea. 6. Department of Pediatrics, Ajou University School of Medicine, Ajou University Medical Center, Suwon, Republic of Korea. 7. Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. catharina@catholic.ac.kr. 8. Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, Suwon, Republic of Korea. catharina@catholic.ac.kr.
Abstract
OBJECTIVES: This study aimed to apply a radiomics approach to predict poor psychomotor development in preterm neonates using brain MRI. METHODS: Prospectively enrolled preterm neonates underwent brain MRI near or at term-equivalent age and neurodevelopment was assessed at a corrected age of 12 months. Two radiologists visually assessed the degree of white matter injury. The radiomics analysis on white matter was performed using T1-weighted images (T1WI) and T2-weighted images (T2WI). A total of 1906 features were extracted from the images and the minimum redundancy maximum relevance algorithm was used to select features. A prediction model for the binary classification of the psychomotor developmental index was developed and eightfold cross-validation was performed. The diagnostic performance of the model was evaluated using the AUC with and without including significant clinical and DTI parameters. RESULTS: A total of 46 preterm neonates (median gestational age, 29 weeks; 26 males) underwent brain MRI (median corrected gestational age, 37 weeks). Thirteen of 46 (28.3%) neonates showed poor psychomotor outcomes. There was one neonate among 46 with moderate to severe white matter injury on visual assessment. For the radiomics analysis, twenty features were selected for each analysis. The AUCs of prediction models based on T1WI, T2WI, and both T1WI and T2WI were 0.925, 0.834, and 0.902. Including gestational age or DTI parameters did not improve the prediction performance of T1WI. CONCLUSIONS: A radiomics analysis of white matter using early T1WI or T2WI could predict poor psychomotor outcomes in preterm neonates. KEY POINTS: • Radiomics analysis on T1-weighted images of preterm neonates showed the highest diagnostic performance (AUC, 0.925) for predicting poor psychomotor outcomes. • In spite of 45 of 46 neonates having no significant white matter injury on visual assessment, the radiomics analysis of early brain MRI showed good diagnostic performance (sensitivity, 84.6%; specificity, 78.8%) for predicting poor psychomotor outcomes. • Radiomics analysis on early brain MRI can help to predict poor neurodevelopmental outcomes in preterm neonates.
OBJECTIVES: This study aimed to apply a radiomics approach to predict poor psychomotor development in preterm neonates using brain MRI. METHODS: Prospectively enrolled preterm neonates underwent brain MRI near or at term-equivalent age and neurodevelopment was assessed at a corrected age of 12 months. Two radiologists visually assessed the degree of white matter injury. The radiomics analysis on white matter was performed using T1-weighted images (T1WI) and T2-weighted images (T2WI). A total of 1906 features were extracted from the images and the minimum redundancy maximum relevance algorithm was used to select features. A prediction model for the binary classification of the psychomotor developmental index was developed and eightfold cross-validation was performed. The diagnostic performance of the model was evaluated using the AUC with and without including significant clinical and DTI parameters. RESULTS: A total of 46 preterm neonates (median gestational age, 29 weeks; 26 males) underwent brain MRI (median corrected gestational age, 37 weeks). Thirteen of 46 (28.3%) neonates showed poor psychomotor outcomes. There was one neonate among 46 with moderate to severe white matter injury on visual assessment. For the radiomics analysis, twenty features were selected for each analysis. The AUCs of prediction models based on T1WI, T2WI, and both T1WI and T2WI were 0.925, 0.834, and 0.902. Including gestational age or DTI parameters did not improve the prediction performance of T1WI. CONCLUSIONS: A radiomics analysis of white matter using early T1WI or T2WI could predict poor psychomotor outcomes in preterm neonates. KEY POINTS: • Radiomics analysis on T1-weighted images of preterm neonates showed the highest diagnostic performance (AUC, 0.925) for predicting poor psychomotor outcomes. • In spite of 45 of 46 neonates having no significant white matter injury on visual assessment, the radiomics analysis of early brain MRI showed good diagnostic performance (sensitivity, 84.6%; specificity, 78.8%) for predicting poor psychomotor outcomes. • Radiomics analysis on early brain MRI can help to predict poor neurodevelopmental outcomes in preterm neonates.
Entities:
Keywords:
Infant; Magnetic resonance imaging; Neurodevelopmental disorder; Premature birth; Radiomics
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