Suppr超能文献

利用管道栓塞术后血管造影衍生的影像组学特征预测颅内动脉瘤介入治疗后破裂

Predicting postinterventional rupture of intracranial aneurysms using arteriography-derived radiomic features after pipeline embolization.

作者信息

Ma Chao, Liang Shikai, Liang Fei, Lu Ligong, Zhu Haoyu, Lv Xianli, Yang Xuejun, Jiang Chuhan, Zhang Yupeng

机构信息

School of Clinical Medicine, Tsinghua University, Beijing, China.

Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.

出版信息

Front Neurol. 2024 Mar 7;15:1327127. doi: 10.3389/fneur.2024.1327127. eCollection 2024.

Abstract

BACKGROUND AND PURPOSE

Postinterventional rupture of intracranial aneurysms (IAs) remains a severe complication after flow diverter treatment. However, potential hemodynamic mechanisms underlying independent predictors for postinterventional rupture of IAs remain unclear. In this study, we employed arteriography-derived radiomic features to predict this complication.

METHODS

We included 64 patients who underwent pipeline flow diversion for intracranial aneurysms, distinguishing between 16 patients who experienced postinterventional rupture and 48 who did not. We performed propensity score matching based on clinical and morphological factors to match these patients with 48 patients with postinterventional unruptured IAs at a 1:3 ratio. Postinterventional digital subtraction angiography were used to create five arteriography-derived perfusion parameter maps and then radiomics features were obtained from each map. Informative features were selected through the least absolute shrinkage and selection operator method with five-fold cross-validation. Subsequently, radiomics scores were formulated to predict the occurrence of postinterventional IA ruptures. Prediction performance was evaluated with the training and test datasets using area under the curve (AUC) and confusion matrix-derived metrics.

RESULTS

Overall, 1,459 radiomics features were obtained, and six were selected. The resulting radiomics scores had high efficacy in distinguishing the postinterventional rupture group. The AUC and Youden index were 0.912 (95% confidence interval: 0.767-1.000) and 0.847 for the training dataset, respectively, and 0.938 (95% confidence interval, 0.806-1.000) and 0.800 for the testing dataset, respectively.

CONCLUSION

Radiomics scores generated using arteriography-derived radiomic features effectively predicted postinterventional IA ruptures and may aid in differentiating IAs at high risk of postinterventional rupture.

摘要

背景与目的

颅内动脉瘤(IA)介入治疗后破裂仍然是血流导向治疗后的严重并发症。然而,IA介入治疗后破裂的独立预测因素背后的潜在血流动力学机制仍不清楚。在本研究中,我们采用血管造影衍生的放射组学特征来预测这一并发症。

方法

我们纳入了64例行颅内动脉瘤血流导向治疗的患者,区分出16例发生介入治疗后破裂的患者和48例未发生破裂的患者。我们根据临床和形态学因素进行倾向评分匹配,以1:3的比例将这些患者与48例介入治疗后未破裂IA的患者进行匹配。使用介入治疗后的数字减影血管造影创建五个血管造影衍生的灌注参数图,然后从每个图中获取放射组学特征。通过最小绝对收缩和选择算子方法进行五折交叉验证来选择信息特征。随后,制定放射组学评分以预测介入治疗后IA破裂的发生。使用曲线下面积(AUC)和混淆矩阵衍生指标在训练和测试数据集上评估预测性能。

结果

总体而言,共获得1459个放射组学特征,其中6个被选中。所得的放射组学评分在区分介入治疗后破裂组方面具有较高的效能。训练数据集的AUC和尤登指数分别为0.912(95%置信区间:0.767 - 1.000)和0.847,测试数据集的AUC和尤登指数分别为0.938(95%置信区间,0.806 - 1.000)和0.800。

结论

使用血管造影衍生的放射组学特征生成的放射组学评分有效地预测了介入治疗后IA破裂,可能有助于区分介入治疗后破裂高风险的IA。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d53/10954779/30ad20f30e5c/fneur-15-1327127-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验