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

doi:10.3389/fendo.2021.635795

	Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease
	Zhang, Wentai 2; Sun, Mengke 3; Fan, Yanghua 2; Wang, He 2; Feng, Ming 2; Zhou, Shaohua 3; Wang, Renzhi 2
刊名	FRONTIERS IN ENDOCRINOLOGY
	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
DOI	10.3389/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
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:000629245500001
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/16804]
专题	中国科学院计算技术研究所
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.