CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia; The University of Queensland, School of Information, Technology and Electrical Engineering, St Lucia, Brisbane, Australia.
CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
Neuroimage Clin. 2016 May 29;11:751-759. doi: 10.1016/j.nicl.2016.05.018. eCollection 2016.
White and grey matter lesions are the most prevalent type of injury observable in the Magnetic Resonance Images (MRIs) of children with cerebral palsy (CP). Previous studies investigating the impact of lesions in children with CP have been qualitative, limited by the lack of automated segmentation approaches in this setting. As a result, the quantitative relationship between lesion burden has yet to be established. In this study, we perform automatic lesion segmentation on a large cohort of data (107 children with unilateral CP and 18 healthy children) with a new, validated method for segmenting both white matter (WM) and grey matter (GM) lesions. The method has better accuracy (94%) than the best current methods (73%), and only requires standard structural MRI sequences. Anatomical lesion burdens most predictive of clinical scores of motor, cognitive, visual and communicative function were identified using the Least Absolute Shrinkage and Selection operator (LASSO). The improved segmentations enabled identification of significant correlations between regional lesion burden and clinical performance, which conform to known structure-function relationships. Model performance was validated in an independent test set, with significant correlations observed for both WM and GM regional lesion burden with motor function (p < 0.008), and between WM and GM lesions alone with cognitive and visual function respectively (p < 0.008). The significant correlation of GM lesions with functional outcome highlights the serious implications GM lesions, in addition to WM lesions, have for prognosis, and the utility of structural MRI alone for quantifying lesion burden and planning therapy interventions.
脑性瘫痪（CP）患儿的磁共振成像（MRI）中最常见的损伤类型是白质和灰质损伤。先前研究 CP 患儿损伤影响的研究都是定性的，这是由于在这种情况下缺乏自动分割方法所致。因此，尚未确定病变负担之间的定量关系。在这项研究中，我们使用一种新的经过验证的方法对大量数据（107 名单侧 CP 患儿和 18 名健康儿童）进行自动病变分割，该方法可用于分割白质（WM）和灰质（GM）病变。该方法的准确性（94％）优于当前最好的方法（73％），并且仅需要标准的结构 MRI 序列。使用最小绝对收缩和选择算子（LASSO）确定了最能预测运动、认知、视觉和交流功能临床评分的解剖病变负担。改进的分割方法能够识别区域病变负担与临床性能之间的显著相关性，这与已知的结构-功能关系一致。在独立的测试集中验证了模型性能，WM 和 GM 区域病变负担与运动功能之间均观察到显著相关性（p <0.008），WM 和 GM 病变分别与认知和视觉功能之间也观察到显著相关性（p <0.008）。GM 病变与功能结果的显著相关性突出表明 GM 病变除了 WM 病变外，对预后有严重影响，并且结构 MRI 本身在量化病变负担和规划治疗干预方面具有实用性。
