Calcium Scoring at Coronary CT Angiography Using Deep Learning.

Journal: Radiology
Published Date:

Abstract

Background Separate noncontrast CT to quantify the coronary artery calcium (CAC) score often precedes coronary CT angiography (CTA). Quantifying CAC scores directly at CTA would eliminate the additional radiation produced at CT but remains challenging. Purpose To quantify CAC scores automatically from a single CTA scan. Materials and Methods In this retrospective study, a deep learning method to quantify CAC scores automatically from a single CTA scan was developed on training and validation sets of 292 patients and 73 patients collected from March 2019 to July 2020. Virtual noncontrast scans obtained with a spectral CT scanner were used to develop the algorithm to alleviate tedious manual annotation of calcium regions. The proposed method was validated on an independent test set of 240 CTA scans collected from three different CT scanners from August 2020 to November 2020 using the Pearson correlation coefficient, the coefficient of determination, or , and the Bland-Altman plot against the semiautomatic Agatston score at noncontrast CT. The cardiovascular risk categorization performance was evaluated using weighted κ based on the Agatston score (CAC score risk categories: 0-10, 11-100, 101-400, and >400). Results Two hundred forty patients (mean age, 60 years ± 11 [standard deviation]; 146 men) were evaluated. The positive correlation between the automatic deep learning CTA and semiautomatic noncontrast CT CAC score was excellent (Pearson correlation = 0.96; = 0.92). The risk categorization agreement based on deep learning CTA and noncontrast CT CAC scores was excellent (weighted κ = 0.94 [95% CI: 0.91, 0.97]), with 223 of 240 scans (93%) categorized correctly. All patients who were miscategorized were in the direct neighboring risk groups. The proposed method's differences from the noncontrast CT CAC score were not statistically significant with regard to scanner ( = .15), sex ( = .051), and section thickness ( = .67). Conclusion A deep learning automatic calcium scoring method accurately quantified coronary artery calcium from CT angiography images and categorized risk. © RSNA, 2021 See also the editorial by Goldfarb and Cao et al in this issue.

Authors

  • Dan Mu
    From the Department of Radiology, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China (D.M., W.C., H.Y., J.L., K.Y., H.L., Z.Q., B.Z.); Keya Medical, Shenzhen, China (J.B., H.Y.Y., J.Z., Y.Y.); Medical School of Nanjing University, Nanjing, China (K.H.); National Institutes of Healthcare Data Science at Nanjing University, Nanjing, China (K.H.); University of South Carolina School of Medicine-Columbia, Columbia, SC (H.W.M.); Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC (U.J.S.); Institute of Brain Science, Nanjing University, Nanjing 210008, China (B.Z.).
  • Junjie Bai
    Department of Engineering, CuraCloud Corporation, Seattle, WA, USA.
  • Wenping Chen
    From the Department of Radiology, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China (D.M., W.C., H.Y., J.L., K.Y., H.L., Z.Q., B.Z.); Keya Medical, Shenzhen, China (J.B., H.Y.Y., J.Z., Y.Y.); Medical School of Nanjing University, Nanjing, China (K.H.); National Institutes of Healthcare Data Science at Nanjing University, Nanjing, China (K.H.); University of South Carolina School of Medicine-Columbia, Columbia, SC (H.W.M.); Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC (U.J.S.); Institute of Brain Science, Nanjing University, Nanjing 210008, China (B.Z.).
  • Hongming Yu
    Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China.
  • Jing Liang
    College of Management Science, Chengdu University of Technology, Chengdu, China.
  • Kejie Yin
    From the Department of Radiology, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China (D.M., W.C., H.Y., J.L., K.Y., H.L., Z.Q., B.Z.); Keya Medical, Shenzhen, China (J.B., H.Y.Y., J.Z., Y.Y.); Medical School of Nanjing University, Nanjing, China (K.H.); National Institutes of Healthcare Data Science at Nanjing University, Nanjing, China (K.H.); University of South Carolina School of Medicine-Columbia, Columbia, SC (H.W.M.); Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC (U.J.S.); Institute of Brain Science, Nanjing University, Nanjing 210008, China (B.Z.).
  • Hui Li
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Zhao Qing
    Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China.
  • Kelei He
  • Hao-Yu Yang
    From the Department of Radiology, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China (D.M., W.C., H.Y., J.L., K.Y., H.L., Z.Q., B.Z.); Keya Medical, Shenzhen, China (J.B., H.Y.Y., J.Z., Y.Y.); Medical School of Nanjing University, Nanjing, China (K.H.); National Institutes of Healthcare Data Science at Nanjing University, Nanjing, China (K.H.); University of South Carolina School of Medicine-Columbia, Columbia, SC (H.W.M.); Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC (U.J.S.); Institute of Brain Science, Nanjing University, Nanjing 210008, China (B.Z.).
  • Jinyao Zhang
    From the Department of Radiology, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China (D.M., W.C., H.Y., J.L., K.Y., H.L., Z.Q., B.Z.); Keya Medical, Shenzhen, China (J.B., H.Y.Y., J.Z., Y.Y.); Medical School of Nanjing University, Nanjing, China (K.H.); National Institutes of Healthcare Data Science at Nanjing University, Nanjing, China (K.H.); University of South Carolina School of Medicine-Columbia, Columbia, SC (H.W.M.); Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC (U.J.S.); Institute of Brain Science, Nanjing University, Nanjing 210008, China (B.Z.).
  • Youbing Yin
    Department of Engineering, CuraCloud Corporation, Seattle, WA, USA.
  • Hunter W McLellan
    From the Department of Radiology, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China (D.M., W.C., H.Y., J.L., K.Y., H.L., Z.Q., B.Z.); Keya Medical, Shenzhen, China (J.B., H.Y.Y., J.Z., Y.Y.); Medical School of Nanjing University, Nanjing, China (K.H.); National Institutes of Healthcare Data Science at Nanjing University, Nanjing, China (K.H.); University of South Carolina School of Medicine-Columbia, Columbia, SC (H.W.M.); Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC (U.J.S.); Institute of Brain Science, Nanjing University, Nanjing 210008, China (B.Z.).
  • U Joseph Schoepf
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260 (S.S.M., D.M., M.v.A., C.N.D.C., R.R.B., C.T., A.V.S., A.M.F., B.E.J., L.P.G., U.J.S.); Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (S.S.M., T.J.V.); Stanford University School of Medicine, Department of Radiology, Stanford, Calif (D.M.); Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (C.N.D.C.); Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC (R.R.B.); Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany (C.T.); Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany (C.T.); Siemens Medical Solutions USA, Malvern, Pa (P.S.); and Department of Emergency Medicine, Medical University of South Carolina, Charleston, SC (A.J.M.).
  • Bing Zhang
    School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China.