背景：准确的结果预测在定制个性化治疗计划方面具有重要的临床意义，减少复苏不良的情况，客观准确地评价治疗效果。本研究旨在评估临床文本信息(CTI)的性能，影像组学功能，和生存特征（SurvF）预测缺血性卒中患者的功能结局。
方法：基于CTI和mRS影像组学特征（mRSRF）构建SurvF，以提高对3个月（90天mRS）功能结局的预测。十个机器学习模型预测了三种情况下的功能结果(2类，4类,和7类)使用CTI构建的七个特征组，mRSRF,和SurvF.
结果：对于2类，所有（CTI+mRSRF+SurvF）表现最好，mAUC为0.884，mAcc为0.864，mPre为0.877，mF1为0.86，mRecall为0.864。对于4类，ALL也获得了0.787的最佳mAuc，而CTISurvF以mAcc=0.611，mPre=0.622，mF1=0.595和mRe-call=0.611获得了最佳得分。对于7类，CTI+SurvF表现最好,mAuc为0.788，mPre为0.519，mAcc为0.529，mF1为0.495，mRecall为0.47。
结论：以上结果表明，mRSRF+CTI可以通过适当的机器学习模型准确预测缺血性卒中患者的功能结局。此外，与原始特征相比，组合SurvF将提高预测效果。然而,受样本量小的限制，需要对更大和更多样化的数据集进行进一步验证。
BACKGROUND: Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke.
METHODS: SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF.
RESULTS: For 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47.
CONCLUSIONS: The above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary.
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