Knowledge-Augmented Deep Learning for Segmenting and Detecting Cerebral Aneurysms With CT Angiography: A Multicenter Study.

Journal: Radiology
PMID: 39162636

Abstract

Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing cerebral aneurysms but requires large multicenter data sets. Purpose To construct a DL model using a multicenter data set for accurate cerebral aneurysm segmentation and detection on CT angiography (CTA) images and to compare its performance with radiology reports. Materials and Methods Consecutive head or head and neck CTA images of suspected unruptured cerebral aneurysms were gathered retrospectively from eight hospitals between February 2018 and October 2021 for model development. An external test set with reference standard digital subtraction angiography (DSA) scans was obtained retrospectively from one of the eight hospitals between February 2022 and February 2023. Radiologists (reference standard) assessed aneurysm segmentation, while model performance was evaluated using the Dice similarity coefficient (DSC). The model's aneurysm detection performance was assessed by sensitivity and comparing areas under the receiver operating characteristic curves (AUCs) between the model and radiology reports in the DSA data set with use of the DeLong test. Results Images from 6060 patients (mean age, 56 years ± 12 [SD]; 3375 [55.7%] female) were included for model development (training: 4342; validation: 1086; and internal test set: 632). Another 118 patients (mean age, 59 years ± 14; 79 [66.9%] female) were included in an external test set to evaluate performance based on DSA. The model achieved a DSC of 0.87 for aneurysm segmentation performance in the internal test set. Using DSA, the model achieved 85.7% (108 of 126 aneurysms [95% CI: 78.1, 90.1]) sensitivity in detecting aneurysms on per-vessel analysis, with no evidence of a difference versus radiology reports (AUC, 0.93 [95% CI: 0.90, 0.95] vs 0.91 [95% CI: 0.87, 0.94]; = .67). Model processing time from reconstruction to detection was 1.76 minutes ± 0.32 per scan. Conclusion The proposed DL model could accurately segment and detect cerebral aneurysms at CTA with no evidence of a significant difference in diagnostic performance compared with radiology reports. © RSNA, 2024 See also the editorial by Payabvash in this issue.

Authors

  • Jianyong Wei
    ShuKun (BeiJing) Technology Co., Ltd., Beijing, China.
  • Xinyu Song
    Beijing Institute of Radiation Medicine, Beijing, 100850, China.
  • Xiaoer Wei
    Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200233, Shanghai, China.
  • Zhiwen Yang
    ShuKun Technology Co., Ltd., Jinhui Bd, Qiyang Rd, Beijing 100029, China.
  • Lisong Dai
    Department of Radiology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Mengfei Wang
    School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, China.
  • Zheng Sun
    Department of Electrical and Computer Engineering, California Baptist University, 3739 Adams Street, Riverside, CA, 92504, USA.
  • Yidong Jin
    From the Clinical Research Center (J.W.) and Institute of Diagnostic and Interventional Radiology, Department of Radiology (X.S., X.W., L.D., Z.S., Y. Zhu, Y.L.), Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600 Yi Shan Rd, Shanghai 200233, China; School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China (J.W., M.W., Y.J.); Shukun (Beijing) Network Technology, Beijing, China (Zhiwen Yang, C.M.); Department of Radiology, The First Affiliated Hospital of Soochow University, Jiangsu, China (C.H.); Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (X.X.); Department of Cardiology, Beijing Friendship Hospital of Capital Medical University, Beijing, China (Zhenghan Yang); Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (Y. Zhang); Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (F.L.); and Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China (J.L.).
  • Chune Ma
    ShuKun Technology Co., Ltd., Jinhui Bd, Qiyang Rd, Beijing 100029, China.
  • Chunhong Hu
    Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Xueqian Xie
    Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080 China.
  • Zhenghan Yang
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Yonggao Zhang
    The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China.
  • Fajin Lv
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Jie Lu
    Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
  • Yueqi Zhu
    Department of Radiology and Medical Imaging, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai 200233, China.
  • Yuehua Li
    From the Institute of Diagnostic and Interventional Radiology (Y.L., M.Y., X.D., J.Z.) and Department of Cardiology (Z.L., C.S.), Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China 200233; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China (Y.W.); and Department of Radiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (B.L.).

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