Santo Briana A, Poppenberg Kerry E, Ciecierska Shiau-Sing K, Baig Ammad A, Raygor Kunal P, Patel Tatsat R, Shah Munjal, Levy Elad I, Siddiqui Adnan H, Tutino Vincent M
Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo , New York , USA.
Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo , New York , USA.
Neurosurgery. 2024 Dec 1;95(6):1285-1296. doi: 10.1227/neu.0000000000003003. Epub 2024 May 29.
Histologic and transcriptomic analyses of retrieved stroke clots have identified features associated with patient outcomes. Previous studies have demonstrated the predictive capacity of histology or expression features in isolation. Few studies, however, have investigated how paired histologic image features and expression patterns from the retrieved clots can improve understanding of clot pathobiology and our ability to predict long-term prognosis. We hypothesized that computational models trained using clot histomics and mRNA expression can predict early neurological improvement (ENI) and 90-day functional outcome (modified Rankin Scale Score, mRS) better than models developed using histological composition or expression data alone.
We performed paired histological and transcriptomic analysis of 32 stroke clots. ENI was defined as a delta-National Institutes of Health Stroke Score/Scale > 4, and a good long-term outcome was defined as mRS ≤2 at 90 days after procedure. Clots were H&E-stained and whole-slide imaged at 40×. An established digital pathology pipeline was used to extract 237 histomic features and to compute clot percent composition (%Comp). When dichotomized by either the ENI or mRS thresholds, differentially expressed genes were identified as those with absolute fold-change >1.5 and q < 0.05. Machine learning with recursive feature elimination (RFE) was used to select clot features and evaluate computational models for outcome prognostication.
For ENI, RFE identified 9 optimal histologic and transcriptomic features for the hybrid model, which achieved an accuracy of 90.8% (area under the curve [AUC] = 0.98 ± 0.08) in testing and outperformed models based on histomics (AUC = 0.94 ± 0.09), transcriptomics (AUC = 0.86 ± 0.16), or %Comp (AUC = 0.70 ± 0.15) alone. For mRS, RFE identified 7 optimal histomic and transcriptomic features for the hybrid model. This model achieved an accuracy of 93.7% (AUC = 0.94 ± 0.09) in testing, also outperforming models based on histomics (AUC = 0.90 ± 0.11), transcriptomics (AUC = 0.55 ± 0.27), or %Comp (AUC = 0.58 ± 0.16) alone.
Hybrid models offer improved outcome prognostication for patients with stroke. Identified digital histology and mRNA signatures warrant further investigation as biomarkers of patient functional outcome after thrombectomy.
对取出的中风血栓进行组织学和转录组学分析已确定了与患者预后相关的特征。以往研究已单独证明了组织学或表达特征的预测能力。然而，很少有研究调查取出的血栓的配对组织学图像特征和表达模式如何能增进对血栓病理生物学的理解以及我们预测长期预后的能力。我们假设，使用血栓组织组学和mRNA表达训练的计算模型比仅使用组织学组成或表达数据开发的模型能更好地预测早期神经功能改善（ENI）和90天功能结局（改良Rankin量表评分，mRS）。
我们对32个中风血栓进行了配对组织学和转录组学分析。ENI定义为美国国立卫生研究院卒中量表评分变化值>4，良好的长期结局定义为术后90天时mRS≤2。血栓进行苏木精-伊红染色并以40倍全玻片成像。使用既定的数字病理学流程提取237个组织组学特征并计算血栓百分比组成（%Comp）。当按ENI或mRS阈值进行二分法划分时，差异表达基因被确定为绝对变化倍数>1.5且q<0.05的基因。使用带递归特征消除（RFE）的机器学习来选择血栓特征并评估用于结局预后的计算模型。
对于ENI，RFE为混合模型确定了9个最佳组织学和转录组学特征，该模型在测试中的准确率为90.8%（曲线下面积[AUC]=0.98±0.08），优于仅基于组织组学（AUC=0.94±0.09）、转录组学（AUC=0.86±0.16）或%Comp（AUC=0.70±0.15）的模型。对于mRS，RFE为混合模型确定了7个最佳组织学和转录组学特征。该模型在测试中的准确率为93.7%（AUC=0.94±0.09），同样优于仅基于组织组学（AUC=0.90±0.11）、转录组学（AUC=0.55±0.27）或%Comp（AUC=0.58±0.16）的模型。
混合模型为中风患者提供了更好的结局预后。所确定的数字组织学和mRNA特征作为血栓切除术后患者功能结局的生物标志物值得进一步研究。
