从诊断性CT到DTI纤维束成像标签：利用深度学习评估脑出血患者皮质脊髓束损伤并预测预后
From Diagnostic CT to DTI Tractography labels: Using Deep Learning for Corticospinal Tract Injury Assessment and Outcome Prediction in Intracerebral Haemorrhage
AI总结：
本研究提出用nnU-Net仅基于诊断CT分割脑出血患者的皮质脊髓束，Dice系数达57%，且该模型的CST完整性指标可预测患者预后，为无法接受DTI的患者提供预后标志物，助力筛选手术获益亚组。
摘要：
皮质脊髓束（CST）的保留是卒中后运动功能良好恢复的关键。影像学评估CST的金标准方法是弥散张量纤维束成像。然而，大多数脑出血（ICH）患者无法接受该项检查。无对比剂CT扫描是多数ICH诊断流程中的常规检查，但从CT图像中勾勒白质极具挑战性。我们利用在配对的诊断性CT扫描和高弥散方向纤维束成像图谱上训练的nnU-Net，仅通过诊断性CT扫描即可分割CST，且实验表明我们的模型复现基于弥散成像的CST纤维束图谱的Dice相似系数为57%。\n脑出血后有时会进行血肿清除手术，但迄今已发表的临床试验显示，尽管手术可降低死亡率，却没有证据表明其能改善功能恢复。将手术限定于CST完整的患者，可能会筛选出血肿清除术可改善其功能预后的患者亚组。我们在MISTIE III临床试验数据集上探究了该模型的临床效用。结果发现，我们模型的CST完整性指标可显著预测脑出血患者急性期和慢性期的预后，从而为无法接受高级弥散张量成像的患者提供了一种预后标志物。这将有助于未来探究可能从手术中获益的患者亚组。
英文摘要：
The preservation of the corticospinal tract (CST) is key to good motor recovery after stroke. The gold standard method of assessing the CST with imaging is diffusion tensor tractography. However, this is not available for most intracerebral haemorrhage (ICH) patients. Non-contrast CT scans are routinely available in most ICH diagnostic pipelines, but delineating white matter from a CT scan is challenging. We utilise nnU-Net, trained on paired diagnostic CT scans and high-directional diffusion tractography maps, to segment the CST from diagnostic CT scans alone, and we show our model reproduces diffusion based tractography maps of the CST with a Dice similarity coefficient of 57%. Surgical haematoma evacuation is sometimes performed after ICH, but published clinical trials to date show that whilst surgery reduces mortality, there is no evidence of improved functional recovery. Restricting surgery to patients with an intact CST may reveal a subset of patients for whom haematoma evacuation improves functional outcome. We investigated the clinical utility of our model in the MISTIE III clinical trial dataset. We found that our model's CST integrity measure significantly predicted outcome after ICH in the acute and chronic time frames, therefore providing a prognostic marker for patients to whom advanced diffusion tensor imaging is unavailable. This will allow for future probing of subgroups who may benefit from surgery.
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