Lewis John D, Miran Atiyeh A, Stoopler Michelle, Branson Helen M, Danguecan Ashley, Raghu Krishna, Ly Linh G, Cizmeci Mehmet N, Kalish Brian T
Program in Neuroscience and Mental Health, SickKids Research Institute, Toronto, Canada.
Division of Neonatology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
Ann Neurol. 2025 Apr;97(4):791-802. doi: 10.1002/ana.27154. Epub 2024 Dec 10.
Neonatal hypoxic-ischemic encephalopathy is a serious neurologic condition associated with death or neurodevelopmental impairments. Magnetic resonance imaging (MRI) is routinely used for neuroprognostication, but there is substantial subjectivity and uncertainty about neurodevelopmental outcome prediction. We sought to develop an objective and automated approach for the analysis of newborn brain MRI to improve the accuracy of prognostication.
We created an anatomic MRI template from a sample of 286 infants treated with therapeutic hypothermia, and labeled the deep gray-matter structures. We extracted quantitative information, including shape-related information, and information represented by complex patterns (radiomic measures), from each of these structures in all infants. We then trained an elastic net model to use either only these measures, only the infants' demographic and laboratory data, or both, to predict neurodevelopmental outcomes, as measured by the Bayley Scales of Infant and Toddler Development at 18 months of age.
Among those infants for whom Bayley scores were available for cognitive, language, and motor outcomes, we found sets of MRI-based measures that could predict their Bayley scores with correlations that were greater than the correlations based on only the demographic and laboratory data, explained more of the variance in the observed scores, and generated a smaller error; predictions based on the combination of the demographic-laboratory and MRI-based measures were similar or marginally better.
Our findings show that machine learning models using MRI-based measures can predict neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy across all neurodevelopmental domains and across the full spectrum of outcomes. ANN NEUROL 2025;97:791-802.
新生儿缺氧缺血性脑病是一种与死亡或神经发育障碍相关的严重神经系统疾病。磁共振成像（MRI）常用于神经预后评估，但在神经发育结局预测方面存在很大的主观性和不确定性。我们试图开发一种客观、自动化的方法来分析新生儿脑MRI，以提高预后评估的准确性。
我们从286例接受治疗性低温治疗的婴儿样本中创建了一个解剖MRI模板，并标记了深部灰质结构。我们从所有婴儿的这些结构中提取了定量信息，包括形状相关信息和由复杂模式表示的信息（影像组学测量）。然后，我们训练了一个弹性网络模型，仅使用这些测量值、仅使用婴儿的人口统计学和实验室数据，或同时使用这两者，来预测神经发育结局，以18个月大时的贝利婴幼儿发育量表来衡量。
在那些可获得贝利认知、语言和运动结局评分的婴儿中，我们发现基于MRI的测量集能够预测他们的贝利评分，其相关性大于仅基于人口统计学和实验室数据的相关性，解释了观察到的评分中更多的方差，并产生了更小的误差；基于人口统计学-实验室和基于MRI的测量相结合的预测相似或略好。
我们的研究结果表明，使用基于MRI的测量的机器学习模型可以预测缺氧缺血性脑病新生儿在所有神经发育领域和整个结局范围内的神经发育结局。《神经病学》2025年；97:791 - 802。
