Background Clinical outcome prediction based on acute-phase ischemic stroke data is valuable for planning health care resources, designing clinical trials, and setting patient expectations. Existing methods require individualized features and often involve manually engineered, time-consuming postprocessing activities. Purpose To predict the 90-day modified Rankin Scale (mRS) score with a deep learning (DL) model fusing noncontrast-enhanced CT (NCCT) and clinical information from the acute phase of stroke. Materials and Methods This retrospective study included data from six patient datasets from four multicenter trials and two registries. The DL-based imaging and clinical model was trained by using NCCT data obtained 1-7 days after baseline imaging and clinical data (age; sex; baseline and 24-hour National Institutes of Health Stroke Scale scores; and history of hypertension, diabetes, and atrial fibrillation). This model was compared with models based on either NCCT or clinical information alone. Model-specific mRS score prediction accuracy, mRS score accuracy within 1 point of the actual mRS score, mean absolute error (MAE), and performance in identifying unfavorable outcomes (mRS score, >2) were evaluated. Results A total of 1335 patients (median age, 71 years; IQR, 60-80 years; 674 female patients) were included for model development and testing through sixfold cross validation, with distributions of 979, 133, and 223 patients across training, validation, and test sets in each of the six cross-validation folds, respectively. The fused model achieved an MAE of 0.94 (95% CI: 0.89, 0.98) for predicting the specific mRS score, outperforming the imaging-only (MAE, 1.10; 95% CI: 1.05, 1.16; P < .001) and the clinical information-only (MAE, 1.00; 95% CI: 0.94, 1.05; P = .04) models. The fused model achieved an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.92) for predicting unfavorable outcomes, outperforming the clinical information-only model (AUC, 0.88; 95% CI: 0.87, 0.90; P < .001) and the imaging-only model (AUC, 0.85; 95% CI: 0.84, 0.87; P < .001). Conclusion A fused DL-based NCCT and clinical model outperformed an imaging-only model and a clinical-information-only model in predicting 90-day mRS scores. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Lee in this issue.

中文翻译：

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急性期平扫 CT 和临床信息对缺血性卒中功能结果的预测。


背景 基于急性期缺血性卒中数据的临床结果预测对于规划医疗保健资源、设计临床试验和设定患者期望很有价值。现有方法需要个性化的特征，并且通常涉及手动设计、耗时的后处理活动。目的 使用融合非造影增强 CT （NCCT） 和中风急性期临床信息的深度学习 （DL） 模型预测 90 天改良 Rankin 量表 （mRS） 评分。材料和方法 本回顾性研究包括来自 4 项多中心试验和 2 个注册库的 6 个患者数据集的数据。基于 DL 的成像和临床模型是使用基线成像和临床数据 （年龄、性别、基线和 24 小时美国国立卫生研究院卒中量表评分、高血压、糖尿病和心房颤动病史） 后 1-7 天获得的 NCCT 数据进行训练的。该模型与仅基于 NCCT 或临床信息的模型进行了比较。评估了模型特异性 mRS 评分预测准确性、实际 mRS 评分 1 分以内的 mRS 评分准确性、平均绝对误差 （MAE） 以及识别不良结果的性能 （mRS 评分，>2）。结果 共有 1335 例患者 （中位年龄 71 岁;IQR，60-80 岁;674 名女性患者）通过六重交叉验证进行模型开发和测试，在六个交叉验证折叠中，训练、验证和测试集分别分布 979 名、 133 名和 223 名患者。融合模型预测特定 mRS 评分的 MAE 为 0.94（95% CI：0.89,0.98），优于仅成像模型（MAE，1.10;95% CI：1.05,1.16;P < .001） 和仅临床信息 （MAE， 1.00;95% CI： 0.94， 1.05;P = .04） 模型。融合模型在预测不良结局方面的受试者工作特征曲线下面积 （AUC） 为 0.91 （95% CI： 0.89， 0.92），优于仅临床信息模型 （AUC， 0.88;95% CI： 0.87， 0.90;P < .001） 和纯成像模型 （AUC， 0.85;95% CI： 0.84， 0.87;P < .001）.结论 融合的基于 DL 的 NCCT 和临床模型在预测 90 天 mRS 评分方面优于仅影像学模型和仅临床信息模型。© RSNA，2024 年本文提供补充材料。另请参见本期 Lee 的社论。