Detecting Large Vessel Occlusion at Multiphase CT Angiography by Using a Deep Convolutional Neural Network.

Journal: Radiology
Published Date: Sep 29, 2020

Abstract

Background Large vessel occlusion (LVO) stroke is one of the most time-sensitive diagnoses in medicine and requires emergent endovascular therapy to reduce morbidity and mortality. Leveraging recent advances in deep learning may facilitate rapid detection and reduce time to treatment. Purpose To develop a convolutional neural network to detect LVOs at multiphase CT angiography. Materials and Methods This multicenter retrospective study evaluated 540 adults with CT angiography examinations for suspected acute ischemic stroke from February 2017 to June 2018. Examinations positive for LVO ( = 270) were confirmed by catheter angiography and LVO-negative examinations ( = 270) were confirmed through review of clinical and radiology reports. Preprocessing of the CT angiography examinations included vasculature segmentation and the creation of maximum intensity projection images to emphasize the contrast agent-enhanced vasculature. Seven experiments were performed by using combinations of the three phases (arterial, phase 1; peak venous, phase 2; and late venous, phase 3) of the CT angiography. Model performance was evaluated on the held-out test set. Metrics included area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results The test set included 62 patients (mean age, 69.5 years; 48% women). Single-phase CT angiography achieved an AUC of 0.74 (95% confidence interval [CI]: 0.63, 0.85) with sensitivity of 77% (24 of 31; 95% CI: 59%, 89%) and specificity of 71% (22 of 31; 95% CI: 53%, 84%). Phases 1, 2, and 3 together achieved an AUC of 0.89 (95% CI: 0.81, 0.96), sensitivity of 100% (31 of 31; 95% CI: 99%, 100%), and specificity of 77% (24 of 31; 95% CI: 59%, 89%), a statistically significant improvement relative to single-phase CT angiography ( = .01). Likewise, phases 1 and 3 and phases 2 and 3 also demonstrated improved fit relative to single phase ( = .03). Conclusion This deep learning model was able to detect the presence of large vessel occlusion and its diagnostic performance was enhanced by using delayed phases at multiphase CT angiography examinations. © RSNA, 2020 See also the editorial by Ospel and Goyal in this issue.

Authors

  • Matthew T Stib
    Department of Diagnostic Imaging, Rhode Island Hospital, Alpert Medical School of Brown University, Providence, RI.
  • Justin Vasquez
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Mary P Dong
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Yun Ho Kim
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Sumera S Subzwari
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Harold J Triedman
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Amy Wang
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Hsin-Lei Charlene Wang
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Anthony D Yao
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Mahesh Jayaraman
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Jerrold L Boxerman
    Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA.
  • Carsten Eickhoff
    Department of Computer Science, ETH Zurich, Zurich, Switzerland; Center for Biomedical Informatics, Brown University, Providence, RI, USA.
  • Ugur Cetintemel
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Grayson L Baird
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Ryan A McTaggart
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).

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