Deep Learning for Head and Neck CT Angiography: Stenosis and Plaque Classification.

Journal: Radiology
PMID: 36880944

Abstract

Background Studies have rarely investigated stenosis detection from head and neck CT angiography scans because accurate interpretation is time consuming and labor intensive. Purpose To develop an automated convolutional neural network-based method for accurate stenosis detection and plaque classification in head and neck CT angiography images and compare its performance with that of radiologists. Materials and Methods A deep learning (DL) algorithm was constructed and trained with use of head and neck CT angiography images that were collected retrospectively from four tertiary hospitals between March 2020 and July 2021. CT scans were partitioned into training, validation, and independent test sets at a ratio of 7:2:1. An independent test set of CT angiography scans was collected prospectively between October 2021 and December 2021 in one of the four tertiary centers. Stenosis grade categories were as follows: mild stenosis (<50%), moderate stenosis (50%-69%), severe stenosis (70%-99%), and occlusion (100%). The stenosis diagnosis and plaque classification of the algorithm were compared with the ground truth of consensus by two radiologists (with more than 10 years of experience). The performance of the models was analyzed in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve. Results There were 3266 patients (mean age ± SD, 62 years ± 12; 2096 men) evaluated. The consistency between radiologists and the DL-assisted algorithm on plaque classification was 85.6% (320 of 374 cases [95% CI: 83.2, 88.6]) on a per-vessel basis. Moreover, the artificial intelligence model assisted in visual assessment, such as increasing confidence in the degree of stenosis. This reduced the time needed for diagnosis and report writing of radiologists from 28.8 minutes ± 5.6 to 12.4 minutes ± 2.0 ( < .001). Conclusion A deep learning algorithm for head and neck CT angiography interpretation accurately determined vessel stenosis and plaque classification and had equivalent diagnostic performance when compared with experienced radiologists. © RSNA, 2023

Authors

  • Fan Fu
    From the Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45 Changchun St, Xicheng District, Beijing 100053, China (F.F., Y.S., M.Z., D.R., J.L.); Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China (F.F., Y.S., M.Z., D.R., J.L.); Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University, Shanghai, China (F.F.); Shukun (Beijing) Technology Co, Beijing, China (G.Y., C.Z.); and Department of Radiology, Shandong Provincial Hospital, Jinan, China (X.W.).
  • Yi Shan
    From the Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45 Changchun St, Xicheng District, Beijing 100053, China (F.F., Y.S., M.Z., D.R., J.L.); Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China (F.F., Y.S., M.Z., D.R., J.L.); Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University, Shanghai, China (F.F.); Shukun (Beijing) Technology Co, Beijing, China (G.Y., C.Z.); and Department of Radiology, Shandong Provincial Hospital, Jinan, China (X.W.).
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Chao Zheng
    School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515 People's Republic of China.
  • Miao Zhang
    gRED Computational Science, Genentech, Inc., South San Francisco, California.
  • Dongdong Rong
    From the Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45 Changchun St, Xicheng District, Beijing 100053, China (F.F., Y.S., M.Z., D.R., J.L.); Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China (F.F., Y.S., M.Z., D.R., J.L.); Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University, Shanghai, China (F.F.); Shukun (Beijing) Technology Co, Beijing, China (G.Y., C.Z.); and Department of Radiology, Shandong Provincial Hospital, Jinan, China (X.W.).
  • Ximing Wang
  • Jie Lu
    Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.

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