Division of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul, Republic of Korea.
Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Eur Radiol. 2021 Aug;31(8):6147-6155. doi: 10.1007/s00330-021-07836-7. Epub 2021 Mar 24.
This study aimed to apply a radiomics approach to predict poor psychomotor development in preterm neonates using brain MRI.
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.
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.
A radiomics analysis of white matter using early T1WI or T2WI could predict poor psychomotor outcomes in preterm neonates.
• 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.
本研究旨在应用放射组学方法，通过脑 MRI 预测早产儿的精神运动发育不良。
前瞻性纳入近足月或足月龄的早产儿，在矫正月龄 12 个月时进行神经发育评估。两名放射科医生对脑白质损伤程度进行视觉评估。对白质进行放射组学分析时，采用 T1 加权成像（T1WI）和 T2 加权成像（T2WI）。从图像中提取 1906 个特征，并采用最小冗余最大相关性算法选择特征。建立用于精神运动发育指数二分类的预测模型，并进行 8 折交叉验证。评估不包括显著临床和 DTI 参数的情况下模型的 AUC 来评估模型的诊断性能。
共纳入 46 例早产儿（中位胎龄为 29 周；男婴 26 例）进行脑 MRI（中位矫正胎龄为 37 周）。46 例中 13 例（28.3%）患儿精神运动发育不良。46 例中，1 例患儿在视觉评估中存在中重度脑白质损伤。对于放射组学分析，两种成像方法的每个分析都选择了 20 个特征。基于 T1WI、T2WI 及两者的预测模型 AUC 分别为 0.925、0.834 和 0.902。纳入胎龄或 DTI 参数并未改善 T1WI 的预测性能。
使用早期 T1WI 或 T2WI 的脑白质放射组学分析可预测早产儿的精神运动发育不良结局。
早产儿 T1WI 的放射组学分析在预测精神运动发育不良结局方面表现出最高的诊断性能（AUC，0.925）。
尽管 46 例患儿中有 45 例在视觉评估中无明显脑白质损伤，但早期脑 MRI 的放射组学分析对预测精神运动发育不良结局仍具有良好的诊断性能（灵敏度为 84.6%，特异性为 78.8%）。
早期脑 MRI 的放射组学分析有助于预测早产儿的神经发育不良结局。
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