A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions

doi:10.1016/j.ejrad.2022.110187

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions
	Sun, Kai 2,3; Zhang, Jing 1; Liu, Zhenyu 2,7; Qiu, Qi 2; Gao, Han 6; Liu, Panpan 5; Chen, Kuntao 1; Wei, Wei 2; Wang, Liang 5; Zhang, Junting 5
刊名	EUROPEAN JOURNAL OF RADIOLOGY
	2022-04-01
卷号	149 页码:7
关键词	Sinus invasion Meningioma ResNet50 Peritumoral Preoperative identification
ISSN号	0720-048X
DOI	10.1016/j.ejrad.2022.110187
通讯作者	Wang, Liang(saintage7@126.com) ; Zhang, Junting(zhangjunting2003@aliyun.com) ; Zhou, Junlin(ery_zhoujl@lzu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Background: For patients with meningioma, surgical procedures are different because of the status of sinus invasion. However, there is still no suitable technique to identify the status of sinus invasion in patients with meningiomas. We aimed to build a deep learning radiomics model to identify sinus invasion before surgery.& nbsp;Methods: A total of 1048 patients with meningiomas were retrospectively enrolled from two hospitals. T1 enhanced-weighted (T1c) and T2-weighted MRI data for each patient were collected. Tumors and their corresponding peritumors were analyzed. Four ResNet50 models were built with different types of regions of interest (ROIs) (tumor and peritumor) and different modal images (T1c and T2) to predict the status of sinus invasion. Several data enhancement methods were applied before ResNet50 model building. The final model was generated by combining four ResNet50 models.& nbsp;Results: The models with a combination of tumors and peritumors using multimodal images achieved the highest predictive performance (AUC = 0.884, ACC = 78.1%) in the independent test cohort. The Delong test proved that the model built with combination ROIs achieved significantly higher performance than the model built only with tumors. The net reclassification improvement and integrated discrimination improvement tests both proved that including peritumor ROIs in the tumor ROIs could significantly improve the prediction ability.& nbsp;Conclusion: In the current study, the deep learning model showed potential for identifying sinus invasion before surgery in patients with meningioma. Including peritumors could significantly improve predictive performance.
WOS关键词	PREOPERATIVE EVALUATION ; VENOUS SYSTEMS ; VENOGRAPHY ; OUTCOMES
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	ELSEVIER IRELAND LTD
WOS记录号	WOS:000784005600005
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48387]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Wang, Liang; Zhang, Junting; Zhou, Junlin; Tian, Jie
作者单位	1.Zunyi Med Univ, Affiliated Hosp 5, Dept Radiol, Zhuhai, Peoples R China 2.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 3.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China 4.Lanzhou Univ, Hosp 2, Dept Radiol, Cuiyingmen 82, Lanzhou 730030, Peoples R China 5.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Nansihuan Xilu 119, Beijing, Peoples R China 6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China 7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Sun, Kai,Zhang, Jing,Liu, Zhenyu,et al. A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions[J]. EUROPEAN JOURNAL OF RADIOLOGY,2022,149:7.
APA	Sun, Kai.,Zhang, Jing.,Liu, Zhenyu.,Qiu, Qi.,Gao, Han.,...&Tian, Jie.(2022).A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions.EUROPEAN JOURNAL OF RADIOLOGY,149,7.
MLA	Sun, Kai,et al."A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions".EUROPEAN JOURNAL OF RADIOLOGY 149(2022):7.