Volume 7, Issue 3 2201486
Research Article

Diagnosis of Unruptured Intracranial Aneurysm by High-Performance Serum Metabolic Fingerprints

Jiabin Su

Jiabin Su

Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

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Jing Cao

Jing Cao

State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127 P. R. China

School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China

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Heng Yang

Heng Yang

National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

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Wei Xu

Wei Xu

State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127 P. R. China

School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China

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Wanshan Liu

Wanshan Liu

State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127 P. R. China

School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China

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Ruimin Wang

Ruimin Wang

State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127 P. R. China

School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China

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Yida Huang

Yida Huang

State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127 P. R. China

School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China

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Jiao Wu

Jiao Wu

State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127 P. R. China

School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China

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Xinjie Gao

Xinjie Gao

Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

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Ruiyuan Weng

Ruiyuan Weng

Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

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Jun Pu

Jun Pu

School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China

State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127 P. R. China

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Ning Liu

Ning Liu

School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China

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Yuxiang Gu

Corresponding Author

Yuxiang Gu

Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

E-mail: [email protected]; [email protected]; [email protected]

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Kun Qian

Corresponding Author

Kun Qian

State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, 160 Pujian Road, Shanghai, 200127 P. R. China

School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030 P. R. China

E-mail: [email protected]; [email protected]; [email protected]

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Wei Ni

Corresponding Author

Wei Ni

Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 P. R. China

E-mail: [email protected]; [email protected]; [email protected]

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First published: 12 January 2023
Citations: 4

Abstract

Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation, the effective medical management of which depends on high-performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time-consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high-performance UIA-specific serum metabolic fingerprints. Diagnostic performance with an area-under-the-curve (AUC) of 0.842 (95% confidence interval (CI): 0.783-0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3-methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727-0.897) and effective growth risk assessment (p<0.05, two-tailed t-test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.

Conflict of Interest

The authors declare competing financial interest. The authors have filed patents for both the technology and the use of the technology to detect biosamples.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.