Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome

被引:10
|
作者
Guo, Yingwei [1 ,2 ]
Yang, Yingjian [1 ,2 ]
Cao, Fengqiu [1 ]
Li, Wei [2 ]
Wang, Mingming [3 ]
Luo, Yu [3 ]
Guo, Jia [4 ]
Zaman, Asim [2 ,5 ]
Zeng, Xueqiang [2 ,5 ]
Miu, Xiaoqiang [1 ,2 ]
Li, Longyu [2 ]
Qiu, Weiyan [2 ]
Kang, Yan [1 ,2 ,5 ,6 ]
机构
[1] Northeastern Univ, Coll Med & Biol Informat Engn, Shenyang 110169, Peoples R China
[2] Shenzhen Technol Univ, Coll Hlth Sci & Environm Engn, Shenzhen 518118, Peoples R China
[3] Tongji Univ, Dept Radiol, Shanghai Peoples Hosp 4, Sch Med, Shanghai 200434, Peoples R China
[4] Columbia Univ, Dept Psychiat, New York, NY 10027 USA
[5] Minist Educ, Engn Res Ctr Med Imaging & Intelligent Anal, Shenyang 110169, Peoples R China
[6] Shenzhen Univ, Sch Appl Technol, Shenzhen 518060, Peoples R China
基金
中国国家自然科学基金;
关键词
ischemic stroke outcome; clinical text information; radiomics features; survival features; machine learning; BLOOD-FLOW; PERFUSION; IDENTIFICATION; DIFFUSION; TRIAL; MORTALITY; EVOLUTION; ADMISSION; INFARCT; LESIONS;
D O I
10.3390/diagnostics12071664
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
引用
收藏
页数:23
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