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Abstract：
OBJECTIVE: To assess the feasibility and diagnostic accuracy of MRI-derived 3D volumetry of lower lumbar vertebrae and dural sac segments using shape-based machine learning for the detection of Marfan syndrome (MFS) compared with dural sac diameter ratios (the current clinical standard).
METHODS: The final study sample was 144 patients being evaluated for MFS from 01/2012 to 12/2016, of whom 81 were non-MFS patients (46 [67%] female, 36 ± 16 years) and 63 were MFS patients (36 [57%] female, 35 ± 11 years) according to the 2010 Revised Ghent Nosology. All patients underwent 1.5T MRI with isotropic 1 × 1 × 1 mm3 3D T2-weighted acquisition of the lumbosacral spine. Segmentation and quantification of vertebral bodies L3-L5 and dural sac segments L3-S1 were performed using a shape-based machine learning algorithm. For comparison with the current clinical standard, anteroposterior diameters of vertebral bodies and dural sac were measured. Ratios between dural sac volume/diameter at the respective level and vertebral body volume/diameter were calculated.
RESULTS: Three-dimensional volumetry revealed larger dural sac volumes (p < 0.001) and volume ratios (p < 0.001) at L3-S1 levels in MFS patients compared with non-MFS patients. For the detection of MFS, 3D volumetry achieved higher AUCs at L3-S1 levels (0.743, 0.752, 0.808, and 0.824) compared with dural sac diameter ratios (0.673, 0.707, 0.791, and 0.848); a significant difference was observed only for L3 (p < 0.001).
CONCLUSIONS: MRI-derived 3D volumetry of the lumbosacral dural sac and vertebral bodies is a feasible method for quantifying dural ectasia using shape-based machine learning. Non-inferior diagnostic accuracy was observed compared with dural sac diameter ratio (the current clinical standard for MFS detection).
METHODS: The final study sample was 144 patients being evaluated for MFS from 01/2012 to 12/2016, of whom 81 were non-MFS patients (46 [67%] female, 36 ± 16 years) and 63 were MFS patients (36 [57%] female, 35 ± 11 years) according to the 2010 Revised Ghent Nosology. All patients underwent 1.5T MRI with isotropic 1 × 1 × 1 mm3 3D T2-weighted acquisition of the lumbosacral spine. Segmentation and quantification of vertebral bodies L3-L5 and dural sac segments L3-S1 were performed using a shape-based machine learning algorithm. For comparison with the current clinical standard, anteroposterior diameters of vertebral bodies and dural sac were measured. Ratios between dural sac volume/diameter at the respective level and vertebral body volume/diameter were calculated.
RESULTS: Three-dimensional volumetry revealed larger dural sac volumes (p < 0.001) and volume ratios (p < 0.001) at L3-S1 levels in MFS patients compared with non-MFS patients. For the detection of MFS, 3D volumetry achieved higher AUCs at L3-S1 levels (0.743, 0.752, 0.808, and 0.824) compared with dural sac diameter ratios (0.673, 0.707, 0.791, and 0.848); a significant difference was observed only for L3 (p < 0.001).
CONCLUSIONS: MRI-derived 3D volumetry of the lumbosacral dural sac and vertebral bodies is a feasible method for quantifying dural ectasia using shape-based machine learning. Non-inferior diagnostic accuracy was observed compared with dural sac diameter ratio (the current clinical standard for MFS detection).
摘要：
目的：评估使用基于形状的机器学习对下腰椎和硬脑膜囊节段进行MRI衍生的3D容积测量以检测马凡氏综合征（MFS）与硬脑膜囊直径比（当前临床标准）的可行性和诊断准确性。
方法：最终研究样本为2012年1月至2016年12月接受MFS评估的144例患者，其中81例为非MFS患者（46[67%]女性，36±16岁）和63是MFS患者（36[57%]女性，35±11年)，根据2010年修订的根特法案。所有患者均接受1.5TMRI，各向同性1×1×1mm33DT2加权采集腰骶脊柱。使用基于形状的机器学习算法对椎体L3-L5和硬脑膜囊段L3-S1进行分割和量化。为了与目前的临床标准进行比较，测量椎体和硬膜囊的前后直径。计算各个水平的硬膜囊体积/直径与椎体体积/直径之间的比率。
结果：三维容积分析显示，与非MFS患者相比，MFS患者L3-S1水平的硬脑膜囊容积（p<0.001）和容积比（p<0.001）更大。对于MFS的检测，与硬脑膜囊直径比（0.673、0.707、0.791和0.848）相比，3D容积在L3-S1水平获得了更高的AUC（0.743、0.752、0.808和0.824）；仅在L3中观察到显着差异（p<0.001）。
结论：基于MRI的腰骶部硬膜囊和椎体的3D容积分析是一种使用基于形状的机器学习量化硬膜扩张的可行方法。与硬膜囊直径比（MFS检测的当前临床标准）相比，观察到诊断准确性不差。
方法：最终研究样本为2012年1月至2016年12月接受MFS评估的144例患者，其中81例为非MFS患者（46[67%]女性，36±16岁）和63是MFS患者（36[57%]女性，35±11年)，根据2010年修订的根特法案。所有患者均接受1.5TMRI，各向同性1×1×1mm33DT2加权采集腰骶脊柱。使用基于形状的机器学习算法对椎体L3-L5和硬脑膜囊段L3-S1进行分割和量化。为了与目前的临床标准进行比较，测量椎体和硬膜囊的前后直径。计算各个水平的硬膜囊体积/直径与椎体体积/直径之间的比率。
结果：三维容积分析显示，与非MFS患者相比，MFS患者L3-S1水平的硬脑膜囊容积（p<0.001）和容积比（p<0.001）更大。对于MFS的检测，与硬脑膜囊直径比（0.673、0.707、0.791和0.848）相比，3D容积在L3-S1水平获得了更高的AUC（0.743、0.752、0.808和0.824）；仅在L3中观察到显着差异（p<0.001）。
结论：基于MRI的腰骶部硬膜囊和椎体的3D容积分析是一种使用基于形状的机器学习量化硬膜扩张的可行方法。与硬膜囊直径比（MFS检测的当前临床标准）相比，观察到诊断准确性不差。
