A Clinical-Radiomics Nomogram for Functional Outcome Predictions in Ischemic Stroke.
A Clinical-Radiomics Nomogram for Functional Outcome Predictions in Ischemic Stroke.
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DOI：
10.1007/s40120-021-00263-2
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发表时间：
2021-12
影响因子：
3.7
通讯作者：
Song B
中科院分区：
文献类型：
--
作者：
Wang H;Sun Y;Ge Y;Wu PY;Lin J;Zhao J;Song B
中风仍然是全球死亡和残疾的主要原因。有效和及时的预后评估对于确定适当的管理策略至关重要。放射组学是一种新兴的非侵入性方法，用于识别预测重要临床结局的定量成像指标。本研究采用改良的兰金量表（mRS）研究并验证放射组学列线图预测缺血性卒中预后。回顾性评估了2018年1月至2019年12月期间经弥散加权成像（DWI）证实的598例连续亚急性梗死患者。他们分别被分配到良好（mRS ≤ 2）和差（mRS > 2）功能结局组。然后，将399名由MR扫描仪1检查的患者和199名由MR扫描仪2扫描的患者分别分配到训练和验证队列。在DWI上对梗死病灶进行手动分割，提取402个放射学特征。使用多变量逻辑回归模型构建包含患者特征和放射组学特征的放射组学列线图。在训练和验证队列中评价列线图的性能。最后，进行决策曲线分析，以评估诺模图的临床价值。还使用单变量分析评价了梗死病灶体积的性能。卒中病灶体积表现出中等性能，曲线下面积（AUC）为0.678。放射组学签名，包括11个放射组学特征，表现出良好的预测性能。放射组学列线图，包括临床特征（年龄、出血和24小时国立卫生研究院卒中量表评分）和放射组学特征在训练队列中表现出良好的区分潜力[AUC = 0.80; 95%置信区间（CI）0.75-0.86]，这在验证队列中得到了验证（AUC = 0.73; 95% CI 0.63-0.82）。此外，它在训练（p = 0.55）和验证（p = 0.21）队列中表现出良好的校准。决策曲线分析证实了该诺模图的临床价值。这种新的无创临床放射组学列线图在预测缺血性卒中预后方面表现出良好的性能。在线版本包含补充材料，可通过10.1007/s40120-021-00263-2获得。
Stroke remains a leading cause of death and disability worldwide. Effective and prompt prognostic evaluation is vital for determining the appropriate management strategy. Radiomics is an emerging noninvasive method used to identify the quantitative imaging indicators for predicting important clinical outcomes. This study was conducted to investigate and validate a radiomics nomogram for predicting ischemic stroke prognosis using the modified Rankin scale (mRS). A total of 598 consecutive patients with subacute infarction confirmed by diffusion-weighted imaging (DWI), from January 2018 to December 2019, were retrospectively assessed. They were assigned to the good (mRS ≤ 2) and poor (mRS > 2) functional outcome groups, respectively. Then, 399 patients examined by MR scanner 1 and 199 patients scanned by MR scanner 2 were assigned to the training and validation cohorts, respectively. Infarction lesions underwent manual segmentation on DWI, extracting 402 radiomic features. A radiomics nomogram encompassing patient characteristics and the radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the training and validation cohorts. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram. The performance of infarction lesion volume was also evaluated using univariate analysis. Stroke lesion volume showed moderate performance, with an area under the curve (AUC) of 0.678. The radiomics signature, including 11 radiomics features, exhibited good prediction performance. The radiomics nomogram, encompassing clinical characteristics (age, hemorrhage, and 24 h National Institutes of Health Stroke Scale score) and the radiomics signature, presented good discriminatory potential in the training cohort [AUC = 0.80; 95% confidence interval (CI) 0.75–0.86], which was validated in the validation cohort (AUC = 0.73; 95% CI 0.63–0.82). In addition, it demonstrated good calibration in the training (p = 0.55) and validation (p = 0.21) cohorts. Decision curve analysis confirmed the clinical value of this nomogram. This novel noninvasive clinical-radiomics nomogram shows good performance in predicting ischemic stroke prognosis. The online version contains supplementary material available at 10.1007/s40120-021-00263-2.
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影响因子：
3.3
作者：
Wang, Hao;Song, Bin;Chen, Bihong T.
通讯作者：
Chen, Bihong T.
影响因子：
6
作者：
Tang, Tian-yu;Jiao, Yun;Ju, Sheng-hong
通讯作者：
Ju, Sheng-hong
DOI：
10.1016/s0003-9993(98)90271-4
发表时间：
1998-10-01
影响因子：
4.3
作者：
Macciocchi, SN;Diamond, PT;Mertz, T
通讯作者：
Mertz, T
急性中风：灌注 CT 定量并行评估的预后价值
影响因子：
19.7
作者：
Shi, Feina;Gong, Xiaoxian;Lou, Min
通讯作者：
Lou, Min
影响因子：
4.5
作者：
Nakamura M;Kajiwara Y;Otsuka A;Kimura H
通讯作者：
Kimura H