CASIA OpenIR  > 中国科学院分子影像重点实验室
Identifying Sinus Invasion in Meningioma Patients before Surgery with Deep Learning
Qi Qiu1,5; Kai Sun1,2; Jing Zhang3; Panpan Liu4; Liang Wang4; Junting Zhang4; Junlin Zhou3; Zhenyu Liu1,5; Jie Tian1,2,5,6
Conference NameMedical Imaging 2022: Computer-Aided Diagnosis. SPIE, 2022
Conference Date2022-4
Conference Place线上

Meningioma is the most common intracranial non-malignant tumor but is usually closely associated with the major venous sinuses. It has been recognized by neurosurgeons that meningioma should be treated with different surgical options depending on the status of sinus invasion. Therefore, it is necessary to accurately identify the venous sinus invasion status of meningioma patients before surgery; however, appropriate techniques are still lacking. Our study aimed to construct a deep learning model for accurate determination of sinus invasion before surgery.

In this study, we collected multi-modal imaging data and clinical information for a total of 1048 meningioma patients from two hospitals. ResNet-50 with a dual attention mechanism was used on the preprocessed T1c and T2WI data respectively, and the final model was generated by combining the two unimodal models. The classification performance was evaluated by the area under receiver operating characteristic (ROC) curve (AUC).

The results implied that the multimodal fusion classification model showed good performance in predicting meningioma sinus invasion. Further analysis on the patients with different WHO gradings indicated that our model has the best classification ability under WHO grading 1 in an independent validation cohort(0.84 AUC) . This study shows that deep learning is a reliable method for predicting sinus invasion in patients with meningioma before surgery.

KeywordDeep learning Meningioma Sinus invasion Multimodal fusion
Indexed ByEI
Sub direction classification人工智能+医疗
planning direction of the national heavy laboratory其他
Paper associated data
Document Type会议论文
Corresponding AuthorZhenyu Liu
Affiliation1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
2.Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, China
3.Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China
4.Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nansihuan Xilu 119, Fengtai District, Beijing, China
5.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
6.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
Recommended Citation
GB/T 7714
Qi Qiu,Kai Sun,Jing Zhang,et al. Identifying Sinus Invasion in Meningioma Patients before Surgery with Deep Learning[C],2022.
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