Abstract
Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegeneration. This uncertainty limits enrollment in clinical trials and delays urgent treatment. The Boundary Sharpness Coefficient (BSC) measures how well-defined the gray-white matter boundary looks on structural MRI. This study measures how BSC changes over time, namely, how fast the boundary degrades each year works much better than looking at a single baseline scan for predicting MCI-to-AD conversion. This study analyzed 1,824 T1-weighted MRI scans from 450 ADNI subjects (95 converters, 355 stable; mean follow-up: 4.84 years). BSC voxel-wise maps were computed using tissue segmentation at the gray-white matter cortical ribbon. Previous studies have used CNN and RNN models that reached 96.0% accuracy for AD classification and 84.2% for MCI conversion, but those approaches disregard specific regions within the brain. This study focused specifically on the gray-white matter interface. The approach uses temporal slope features capturing boundary degradation rates, feeding them into Random Survival Forest, a non-parametric ensemble method for right-censored survival data. The Random Survival Forest trained on BSC slopes achieved a test C-index of 0.63, a 163% improvement over baseline parametric models (test C-index: 0.24). Structural MRI costs a fraction of PET imaging ($800--$1,500 vs. $5,000--$7,000) and does not require CSF collection. These temporal biomarkers could help with patient-centered safety screening as well as risk assessment.
Abstract (translated)
预测轻度认知障碍（MCI）患者是否会进展为阿尔茨海默病（AD），在神经退行性病变早期至关重要。这种不确定性限制了临床试验的入组，并延误了急需的治疗。边界锐度系数（BSC）用于测量结构磁共振成像中灰质-白质边界清晰度。本研究通过测量BSC随时间的变化，即每年边界退化的速度，来预测MCI向AD的转化，这比单一基线扫描效果更好。本研究分析了来自450名ADNI受试者（95名转化者，355名稳定者；平均随访时间：4.84年）的1,824个T1加权磁共振扫描。基于灰质-白质皮层带组织分割计算了体素级BSC图谱。先前研究使用CNN和RNN模型在AD分类上达到96.0%的准确率，在MCI转化预测上达到84.2%，但这些方法忽略了大脑特定区域。本研究专门聚焦于灰质-白质界面。该方法采用捕捉边界退化速率的时间斜率特征，将其输入随机生存森林——一种用于右删失生存数据的非参数集成方法。基于BSC斜率训练的随机生存森林在测试中达到0.63的C指数，比基线参数模型（测试C指数：0.24）提升了163%。结构磁共振成像成本远低于PET成像（800-1,500美元 vs. 5,000-7,000美元），且无需采集脑脊液。这些时间生物标志物或可辅助以患者为中心的安全筛查及风险评估。
URL
https://arxiv.org/abs/2603.26007
