用脑部影像边界退化速度预测轻度认知障碍转为阿尔茨海默病的时间。
Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort
- 计算灰白质交界处边界清晰度随时间的下降速率作为预测特征
- 边界退化速率模型在4.84年随访中实现C-index 0.63，比基线模型提升163%
- 适合关注早期阿尔茨海默病风险筛查与临床试验入组评估的研究者
预测轻度认知障碍（MCI）患者是否会进展为阿尔茨海默病（AD）对神经退行性疾病的早期干预至关重要。本研究利用ADNI队列中的1,824张T1加权MRI扫描数据（450名受试者，95例转化者，355例稳定者，平均随访4.84年），分析灰白质交界处边界清晰度系数（BSC）的纵向变化。通过组织分割生成皮层灰白质交界处的体素级BSC图谱，提取其时间斜率作为特征，输入随机生存森林（Random Survival Forest）进行生存分析。该方法在测试集上获得C-index 0.63，较基线参数模型（测试C-index 0.24）提升163%。相比需昂贵费用与脑脊液采集的PET和生物标志物检测，结构磁共振成本仅为800–1,500美元，且无需侵入性采样，具备潜在临床应用价值。
原文摘要 · Abstract (English)
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.
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