A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma

doi:10.1016/j.cmpb.2022.106651

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma
	Zhu, Lin1,2 ; Zhang, Lingling 3; Hu, Wenxing 4; Chen, Haixu 5,6; Li, Han 2,7; Wei, Shoushui 1; Chen, Xuzhu 3; Ma, Xibo2,8
发表期刊	COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN	0169-2607
	2022-04-01
卷号	216 页码:13
通讯作者	Wei, Shoushui(sswei@sdu.edu.cn) ; Chen, Xuzhu(radiology888@aliyun.com) ; Ma, Xibo(xibo.ma@ia.ac.cn)
摘要	Background and Objective: Craniopharyngioma is a kind of benign brain tumor in histography. However, it might be clinically aggressive and have severe manifestations, such as increased intracranial pressure, hypothalamic-pituitary dysfunction, and visual impairment. It is considered challenging for radiologists to predict the invasiveness of craniopharyngioma through MRI images. Therefore, developing a non-invasive method that can predict the invasiveness and boundary of CP as a reference before surgery is of clinical value for making more appropriate and individualized treatment decisions and reducing the occurrence of inappropriate surgical plan choices. Methods: The MT-Brain system has consisted of two pathways, a sub-path based on 2D CNN for capturing the features from each slice of MRI images, and a 3D sub-network for capturing additional context information between slices. By introducing the two-path architecture, our system can make full use of the fusion of the above 2D and 3D features for classification. Furthermore, position encoding and mask-guided attention also have been introduced to improve the segmentation and diagnosis performance. To verify the performance of the MT-Brain system, we have enrolled 1032 patients with craniopharyngioma (302 invasion and 730 non-invasion patients), segmented the tumors on postcontrast coronal T1WI and randomized them into a training dataset and a testing dataset at a ratio of 8:2. Results: The MT-Brain system achieved a remarkable performance in diagnosing the invasiveness of craniopharyngioma with the AUC of 83.84%, the accuracy of 77.94%, the sensitivity of 70.97%, and the specificity of 80.99%. In the lesion segmentation task, the predicted boundaries of lesions were similar to those labeled by radiologists with the dice of 66.36%. In addition, some explorations also have been made on the interpretability of deep learning models, illustrating the reliability of the model. Conclusions: To the best of our knowledge, this study is the first to develop an integrated deep learning model to predict the invasiveness of craniopharyngioma preoperatively and locate the lesion boundary synchronously on MRI. The excellent performances indicate that the MT-Brain system has great potential in real-world clinical applications. (C) 2022 Published by Elsevier B.V.
关键词	Craniopharyngioma MRI Imaging Deep learning Invasiveness diagnosis Lesion segmentation
DOI	10.1016/j.cmpb.2022.106651
收录类别	SCI
语种	英语
资助项目	National Key Research Program of China[2016YFA0100902] ; National Natural Science Foundation Projects of China[82072014] ; National Natural Science Foundation Projects of China[82090051] ; National Natural Science Foundation Projects of China[81871442] ; National Natural Science Foundation Projects of China[817720 05] ; Shandong Province Natural Science Foundation[ZR2020MF028] ; Youth Innovation Promotion Association CAS[Y201930] ; Beijing Municipal Science & Technology Commission[Z191100006619088]
项目资助者	National Key Research Program of China ; National Natural Science Foundation Projects of China ; Shandong Province Natural Science Foundation ; Youth Innovation Promotion Association CAS ; Beijing Municipal Science & Technology Commission
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
WOS类目	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS记录号	WOS:000766134000003
出版者	ELSEVIER IRELAND LTD
七大方向——子方向分类	人工智能+医疗
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48018
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Wei, Shoushui; Chen, Xuzhu; Ma, Xibo
作者单位	1.Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Shandong, Peoples R China 2.Chinese Acad Sci, Inst Automat, CBSR & NLPR, Beijing 100190, Peoples R China 3.Capital Med Univ, Beijing Tiantan Hosp, Dept Radiol, Beijing, Peoples R China 4.Univ New South Wales, Sydney, NSW, Australia 5.Chinese Peoples Liberat Army Gen Hosp, Inst Geriatr, Med Ctr 2, Beijing 100853, Peoples R China 6.Chinese Peoples Liberat Army Gen Hosp, Natl Clin Res Ctr Geriatr Dis, Med Ctr 2, Beijing 100853, Peoples R China 7.Guangdong Acad Med Sci, Dept Orthoped, Guangdong Prov Peoples Hosp, Guangzhou, Peoples R China 8.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhu, Lin,Zhang, Lingling,Hu, Wenxing,et al. A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2022,216:13.
APA	Zhu, Lin.,Zhang, Lingling.,Hu, Wenxing.,Chen, Haixu.,Li, Han.,...&Ma, Xibo.(2022).A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,216,13.
MLA	Zhu, Lin,et al."A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 216(2022):13.