ISSN 1004-4140
CN 11-3017/P
Volume 32 Issue 4
Jul.  2023
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CHENG X Y, WU X H, HAO Y, et al. Effect of Computed Tomography Window Technique on the Results of Artificial Intelligence Classification of Lung Lesions[J]. CT Theory and Applications, 2023, 32(4): 515-522. DOI: 10.15953/j.ctta.2022.210. (in Chinese).
Citation: CHENG X Y, WU X H, HAO Y, et al. Effect of Computed Tomography Window Technique on the Results of Artificial Intelligence Classification of Lung Lesions[J]. CT Theory and Applications, 2023, 32(4): 515-522. DOI: 10.15953/j.ctta.2022.210. (in Chinese).
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Effect of Computed Tomography Window Technique on the Results of Artificial Intelligence Classification of Lung Lesions

  • Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing100050, China

More Information
  • Received Date: October 28, 2022
  • Revised Date: December 05, 2022
  • Accepted Date: December 06, 2022
  • Available Online: January 03, 2023
  • Published Date: July 30, 2023
    Graphical Abstract
    • Abstract
  • Objective: To use three different 3D CNN algorithms and five different computed tomography (CT) window settings to study the effect on the results of artificial intelligence classification of lung lesions in different CT window techniques. Method: A total of 172 cases of peripheral lung cancer and 185 of focal pneumonia who underwent chest CT were analyzed. Three different 3D CNN algorithms were selected (ResNet, ResNext, and DenseNet) to divide the lesions into two groups. Five different CT window settings, including lung window (1500, 600), mediastinal window (350, 40), custom window 1 (SW1) (1000, 40), and custom window 2 (SW2) (1000, 100), were used retrospectively. We calculated classification accuracy, receiver operating characteristic (ROC) curve, and area under the curve (AUC). The ROC curve was compared in pairs. Results: The average classification accuracy of ResNet was the lowest in the mediastinal window (85.732%; AUC value: 0.871) and the highest in the full window (91.596%; AUC value: 0.946). The average classification accuracy of ResNext was the lowest in the mediastinal window (81.528%; AUC value: 0.814) and the highest in the full window (86.568%; AUC value: 0.882). The average classification accuracy of DenseNet was the lowest in the mediastinal window (87.954%; AUC value: 0.906) and the highest in the SW2 window (93.274%; AUC value: 0.951). Medcalc was used to compare ROC curves under five windows of three 3D CNN. The AUC values between mediastinal window and lung window, mediastinal window and SW1, and mediastinal window and SW2 were statistically significant. Conclusion: There is little difference in the diagnostic efficacy of the three 3D CNN. Different CT window settings have an influence on the results of CNN classification of the lung lesions, and the diagnostic efficiency of the three 3D CNN is the worst under the mediastinal window.
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    • References (15)
  • [1]
    Van GINNEKEN B. Fifty years of computer analysis in chest imaging: Rule-based, machine learning, deep learning[J]. Radiological Physics and Technology, 2017, 10(1): 23−32. doi: 10.1007/s12194-017-0394-5
    [2]
    YU X, LU S, GUO L, et al. ResGNet-C: A graph convolutional neural network for detection of COVID-19[J]. Neurocomputing, 2021, 452: 592−605. doi: 10.1016/j.neucom.2020.07.144
    [3]
    LEE S M, SEO J B, YUN J, et al. Deep learning applications in chest radiography and computed tomography: Current state of the art[J]. Journal of Thoracic Imaging, 2019, 34(2): 75−85. doi: 10.1097/RTI.0000000000000387
    [4]
    SHIN H C, ROTH H R, GAO M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning[J]. IEEE Transactions on Medical Imaging, 2016, 35(5): 1285−1298.
    [5]
    KANG G, LIU K, HOU B, et al. 3D multi-view convolutional neural networks for lung nodule classification[J]. Plos One, 2017, 12(11): 12−22.
    [6]
    CHENG X, WEN H, YOU H, et al. Recognition of peripheral lung cancer and focal pneumonia on chest computed tomography images based on convolutional neural network[J]. Technology in Cancer Research & Treatment, 2022, 21: 1-12.
    [7]
    XIE S, GIRSHICK R, DOLLÁR P, et al. Aggregated residual transformations for deep neural networks[C]//In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1492-1500.
    [8]
    HUANG G, LIU Z, Van der MAATEN L, et al. Densely connected convolutional networks[C]//In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700-4708.
    [9]
    田培林, 徐覃莎, 唐利荣, 等. 胸部CT检查的肺窗技术[J]. 生物医学工程与临床, 2002,6(4): 209−213. doi: 10.3969/j.issn.1009-7090.2002.04.010

    TIAN P L, XU Q S, TANG L R, et al. Window techniques for pulmonary image in chest CT[J]. Biomedical Engineering and Clinical Medicine, 2002, 6(4): 209−213. (in Chinese). doi: 10.3969/j.issn.1009-7090.2002.04.010
    [10]
    张胜超, 陈浩, 秦宣, 等. CT窗口技术在肺磨玻璃结节诊断中的临床意义[J]. 中国现代医学杂志, 2019,29(14): 106−109.

    ZHANG S C, CHEN H, QIN X, et al. Clinical significance of CT window technique in the diagnosis and treatment of pulmonary ground-glass nodules[J]. China Journal of Modern Medicine, 2019, 29(14): 106−109. (in Chinese).
    [11]
    KITAMI A, SANO F, HAYASHI S, et al. Correlation between histological invasiveness and the computed tomography value in pure ground-glass nodules[J]. Surgery Today, 2016, 46(5): 593−598. doi: 10.1007/s00595-015-1208-1
    [12]
    张鹏, 徐欣楠, 王洪伟, 等. 基于深度学习的计算机辅助肺癌诊断方法[J]. 计算机辅助设计与图形学学报, 2018,30(1): 90−99.

    ZHANG P, XU X N, WANG H W, et al. Computer-aided lung cancer diagnosis approaches base on deep learning[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(1): 90−99. (in Chinese).
    [13]
    尹柯, 张久权, 伍建林, 等. 对比卷积神经网络分类模型与放射科医师鉴别浸润性肺腺癌的效能[J]. 中国医学影像技术, 2021,37(9): 1338−1342.

    YIN K, ZHANG J Q, WU J L, et al. Comparison on convolutional neural network classification model and radiologists in differentiating invasive lung adenocarcinoma[J]. Chinese Journal of Medical Imaging Technology, 2021, 37(9): 1338−1342. (in Chinese).
    [14]
    戴书华, 刘国芳, 向东生. 肺磨玻璃结节CT值测量在早期癌症诊断中的意义[J]. 中华肺部疾病杂志(电子版), 2019,12(6): 770−771.

    DAI S H, LIU G F, XIANG D S. The significance of CT value measurement of pulmonary ground glass nodule in early cancer diagnosis[J]. Chinese Journal of Lung Diseases Electronic Edition, 2019, 12(6): 770−771. (in Chinese).
    [15]
    HE S, CHEN C, WANG Z, et al. The use of the mean computed tomography value to predict the invasiveness of ground-glass nodules: A meta-analysis[J]. Asian Journal of Surgery, 2022, 18: 1-6.
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