Penumbra-based radiomics signature as prognostic biomarkers for thrombolysis of acute ischemic stroke patients: a multicenter cohort study
Penumbra-based radiomics signature as prognostic biomarkers for thrombolysis of acute ischemic stroke patients: a multicenter cohort study
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DOI：
10.1007/s00415-020-09713-7
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发表时间：
2020-02-01
影响因子：
6
通讯作者：
Ju, Sheng-hong
中科院分区：
文献类型：
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作者：
Tang, Tian-yu;Jiao, Yun;Ju, Sheng-hong
背景与目的本研究旨在开发一种放射组学特征（R评分）作为基于半暗带量化的预后生物标志物，并验证放射组学nomogram预测急性缺血性卒中（AIS）患者溶栓的临床结果。方法回顾性分析来自7个中心的168例患者。不匹配得分定义为MIS。基于短期临床标签，采用特征选择方法对456个放射组学特征进行评估。选取特征构建R评分。为了比较临床因素、MIS和R评分的预测能力，根据第7天的短期临床评估，制作并评估了三个nomogram。最后，在外部队列中，通过预测AIS患者3个月的临床结果来验证放射组学图。结果在训练和验证数据集中，临床结果良好的患者R评分明显更高。放射组学nomogram预估良好临床结果的预测价值一般，外部验证数据集的一致性指数（C-index）为0.695[95%可信区间（CI） 0.667-0.723]。此外，预测临床预后良好的放射组学nomogram曲线下面积（AUC）在第7天达到0.886 (95% CI 0.809-0.963)， 3个月时达到0.777 （95% CI 0.666-0.888）。结论放射组学特征是评估AIS患者临床预后的独立生物标志物。通过提高对发病3个月后AIS患者临床结果的个体化预测，放射组学图为当前的临床决策过程增加了更多价值。
Background and Purpose This study aimed at developing a radiomics signature (R score) as prognostic biomarkers based on penumbra quantification and to validate the radiomics nomogram to predict the clinical outcomes for thrombolysis for acute ischemic stroke (AIS) patients. Methods In total, 168 patients collected from seven centers were retrospectively included. A score of mismatch was defined as MIS. Based on a short-term clinical label, 456 radiomics features were evaluated with feature selection methods. R score was constructed with the selected features. To compare the predictive capabilities of the clinical factors, MIS, and R score, three nomograms were developed and evaluated, according to the short-term clinical assessment on day 7. Finally, the radiomics nomogram was validated by predicting the 3-month clinical outcomes of AIS patients, in an external cohort. Results R scores were found to be significantly higher in patients with favorable clinical outcomes in both training and validation datasets. The predictive value of the radiomics nomogram estimating favorable clinical outcomes was modest, with a concordance index (C-index) of 0.695 [95% confidence interval (CI) 0.667-0.723) in an external validation dataset. In addition, the area under curve (AUC) of the radiomics nomogram predicting favorable clinical outcome reached 0.886 (95% CI 0.809-0.963) on day 7 and 0.777 (95% CI 0.666-0.888) at 3 months. Conclusions The radiomics signature is an independent biomarker for estimating the clinical outcomes in AIS patients. By improving the individualized prediction of the clinical outcome for AIS patients 3 months after onset, the radiomics nomogram adds more value to the current clinical decision-making process.