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Volume 13 Issue 4
Jul.  2022
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WANG Hao, SUN Zhongjie, CHEN Dong, WAN Tao, LIANG Zhiyong, LIAN Guoliang, DONG Fang, GONG Shanshan, JI Junyu, QIN Cengchang. Computer-aided Diagnostic Methods for Medial Degeneration in Non-inflammatory Aorta Based on Multi-stained Pathological Images[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(4): 590-596. DOI: 10.12290/xhyxzz.2022-0170
Citation: WANG Hao, SUN Zhongjie, CHEN Dong, WAN Tao, LIANG Zhiyong, LIAN Guoliang, DONG Fang, GONG Shanshan, JI Junyu, QIN Cengchang. Computer-aided Diagnostic Methods for Medial Degeneration in Non-inflammatory Aorta Based on Multi-stained Pathological Images[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(4): 590-596. DOI: 10.12290/xhyxzz.2022-0170
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Computer-aided Diagnostic Methods for Medial Degeneration in Non-inflammatory Aorta Based on Multi-stained Pathological Images

  • 1.

    Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

  • 2.

    Department of Pathology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China

  • 3.

    School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China

  • 4.

    School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

Funds: 

National Natural Science Foundation of China 61876197

The Clinical Technical Innovation Project of Beijing Hospitals Authority XMLX201814

More Information
  • Corresponding author:

    CHEN Dong, E-mail: azchendong@163.com

  • Received Date: March 31, 2022
  • Accepted Date: May 26, 2022
  • Issue Publish Date: July 29, 2022
    Graphical Abstract
    • Abstract
  •   Objective  To explore the feasibility of establishing a computer-aided diagnostic model of multi-stained pathological images in patients with non-inflammatory aortic medial degeneration(MD).
      Methods  In this study, pathological sections of aortic surgical specimens for non-inflammatory lesions from patients with thoracic aortic aneurysms and dissections were retrospectively collected at the Beijing Anzhen Hospital, Capital Medical University from July to December 2018. The lesions were scanned under ×400 magnification as whole slide images(WSI) and then annotated by two pathologists. The annotated WSI images were randomly split into training and test sets in a 6:1 ratio. SE-EmbraceNet was used to train the data to construct a multi-classification model for MD of multi-stained pathology images, including intralamellar mucoid extracellular matrix accumulation (MEMA-I), translamellar mucoid extracellular matrix accumulation (MEMA-T), elastic fiber fragmentation and/or loss(EFFL) and smooth muscle cell nuclei loss (SMCNL). The classification effect of the model was evaluated based on the test set data, and the results were expressed in terms of accuracy, sensitivity, precision, and the F1 value.
      Results  Totally 530 pathological slides of non-inflammatory aortic lesion surgical specimens from patients with aortic aneurysm and dissection were included. Extracted 5265 sets of images, each containing 5 stained pathological images of the same lesion site: HE staining, special staining (elastic fiber/VanGieson, Masson, Alcian blue/periodic acid Schiff) and smooth muscle actin staining. There were 4513 sets of training images, including 987 SMCNL, 2013 EFFL, 1337 MEMA-I, and 176 MEMA-T; and 752 test images including 166 SMCNL, 335 EFFL, 222 MEMA-I, and 29 MEMA-T. The overall performance of the model in the test set showed good results, with an accuracy of 96.54%(726/752). The model had the best classification performance for EFFL, with accuracy, sensitivity, precision, and F1 value all ≥98.51%. The model also had a great classification ability for SMCNL, with all evaluated indexes≥97.59%.
      Conclusion  The multi-stained pathology image-based MD classification model constructed in this study has high classification accuracy and good generalization ability, which has the potential to be applied to assist in the diagnosis of the non-inflammatory aortic lesion.
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