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Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease

文献类型：期刊论文

作者Zhang, Wentai2; Sun, Mengke3; Fan, Yanghua2; Wang, He2; Feng, Ming2; Zhou, Shaohua3; Wang, Renzhi2
刊名FRONTIERS IN ENDOCRINOLOGY
出版日期2021-03-02
卷号12页码:9
关键词Cushing’ s disease machine learning transsphenoidal surgery preoperative prediction immediate remission
ISSN号1664-2392
DOI10.3389/fendo.2021.635795
英文摘要Background There are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transsphenoidal surgery (TSS) in patients diagnosed with histology-positive Cushing's disease (CD). Purpose Our current study aims to devise and assess an ML-based model to preoperatively predict immediate remission after TSS in patients with CD. Methods A total of 1,045 participants with CD who received TSS at Peking Union Medical College Hospital in a 20-year period (between February 2000 and September 2019) were enrolled in the present study. In total nine ML classifiers were applied to construct models for the preoperative prediction of immediate remission with preoperative factors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the models. The performance of each ML-based model was evaluated in terms of AUC. Results The overall immediate remission rate was 73.3% (766/1045). First operation (p<0.001), cavernous sinus invasion on preoperative MRI(p<0.001), tumour size (p<0.001), preoperative ACTH (p=0.008), and disease duration (p=0.010) were significantly related to immediate remission on logistic univariate analysis. The AUCs of the models ranged between 0.664 and 0.743. The highest AUC, i.e., the best performance, was 0.743, which was achieved by stacking ensemble method with four factors: first operation, cavernous sinus invasion on preoperative MRI, tumour size and preoperative ACTH. Conclusion We developed a readily available ML-based model for the preoperative prediction of immediate remission in patients with CD.
资助项目Graduate Innovation Fund of Peking Union Medical College[2018-1002-01-10] ; Natural Science Foundation of Beijing Municipality[7182137]
WOS研究方向Endocrinology & Metabolism
语种英语
WOS记录号WOS:000629245500001
出版者FRONTIERS MEDIA SA
源URL[http://119.78.100.204/handle/2XEOYT63/16804]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Feng, Ming; Zhou, Shaohua; Wang, Renzhi
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Med Sci & Peking Union Med Coll, Dept Neurosurg, Peking Union Med Coll Hosp, Beijing, Peoples R China
3.Chinese Acad Sci, Analyt Comp Lab Engn MIRACLE, Key Lab Intelligent Informat Proc, Med Imaging,Inst Comp Technol,CAS,Robot, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Wentai,Sun, Mengke,Fan, Yanghua,et al. Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease[J]. FRONTIERS IN ENDOCRINOLOGY,2021,12:9.
APA Zhang, Wentai.,Sun, Mengke.,Fan, Yanghua.,Wang, He.,Feng, Ming.,...&Wang, Renzhi.(2021).Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease.FRONTIERS IN ENDOCRINOLOGY,12,9.
MLA Zhang, Wentai,et al."Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease".FRONTIERS IN ENDOCRINOLOGY 12(2021):9.

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来源：计算技术研究所

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