Email Alert RSS
Volume 12 Issue 5
Sep.  2021
Turn off MathJax
Article Contents
Article Navigation >  Medical Journal of Peking Union Medical College Hospital  > 2021  >  12(5): 689-697
LIU Zhaorui, ZHANG Yilan, XIE Fengying, LIU Jie. Early Diagnosis Model of Mycosis Fungoides Based on Intelligent Analysis of Dermoscopic Images[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 689-697. doi: 10.12290/xhyxzz.2021-0496
Citation: LIU Zhaorui, ZHANG Yilan, XIE Fengying, LIU Jie. Early Diagnosis Model of Mycosis Fungoides Based on Intelligent Analysis of Dermoscopic Images[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 689-697. doi: 10.12290/xhyxzz.2021-0496
shu

Early Diagnosis Model of Mycosis Fungoides Based on Intelligent Analysis of Dermoscopic Images

doi: 10.12290/xhyxzz.2021-0496
  • 1.

    Department of Dermatology, State Key Laboratory of Complex Severe and Rare Diseases, National Clinical Research Center for Dermatologic and Immunologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

  • 2.

    Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China

Funds:

National Natural Science Foundation of China 61871011

National Natural Science Foundation of China 62071011

National Natural Science Foundation of China 61771031

National Natural Science Foundation of China 82173449

The Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences 2019XK320024

Beijing Natural Science Foudation 4192032

More Information
  • Corresponding author: XIE Fengying  Tel: 86-10-61716929, E-mail: xfy_73@buaa.edu.cn; LIU Jie  Tel: 86-10-69151536, E-mail: Liujie04672@pumch.cn
  • Received Date: 2021-06-26
  • Accepted Date: 2021-08-02
  • Publish Date: 2021-09-30
    Abstract
  •   Objective  To compare the application value of the binary classification model based on dermoscopic images of convolutional neural network (CNN) in the diagnosis of mycosis fungoides (MF) and inflammatory dermatosis.  Methods  Patients diagnosed with early MF or inflammatory dermatosis with similar clinical manifestations in the dermatology clinic of Peking Union Medical College Hospital from January 2016 to December 2020 were retrospectively included. The patients were divided into the training set and the test set at a ratio of 4∶1. Six classical network structures were trained by using the dermoscopic images of patients in the training set, and the CNN binary classification model was constructed by using transfer learning. At the same time, in the test set, 1 image of each patient that was randomly selected, together with clinical images of the skin lesions, was interpreted by 13 dermatologists. Compare the CNN binary classification model with dermatologists in the differential diagnosis of early MF and inflammatory dermatosis in the test set. The results were expressed in terms of area under the curve (AUC), sensitivity, specificity, Kappa coefficient, etc., and receiver operating characteristic (ROC) curve was used for visual analysis.  Results  A total of 48 patients with early MF (402 dermoscopic images) and 96 patients with inflammatory dermatosis (557 dermoscopic images) were included. Among them, there were 117 cases in the training set (772 dermoscopic images), and 27 cases in the test set (187 dermoscopic images). In the test set, the sensitivity and specificity of dermatologists in the differential diagnosis of early MF and inflammatory dermatosis were 70.19% (95% CI: 59.68%-80.70%) and 94.74% (95% CI: 91.77%-97.71%) respectively, and the Kappa coefficient is 0.677(95% CI: 0.566-0.789). When classified by the single image, the AUC of the CNN binary classification model for the differential diagnosis of early MF and inflammatory dermatosis was 0.87 (95% CI: 0.84-0.89); the sensitivity and specificity were 75.02% (95% CI: 70.19%-79.85%) and 82.02% (95% CI: 79.30%-84.87%), respectively; the Kappa coefficient was 0.563(95% CI: 0.507-0.620). When classified by cases, the AUC of the CNN binary classification model for the differential diagnosis of early MF and inflammatory dermatosis was 0.97 (95% CI: 0.95-0.99); the sensitivity and specificity were 87.50% (95% CI: 78.55%-96.45%) and 93.85% (95% CI: 88.93%-98.77%), respectively; the Kappa coefficient was 0.920(95% CI: 0.884-0.954). The ROC curve showed that the AUC of the CNN binary classification model with EfficientNet-B0 for diagnosing MF was 0.99 when classified by cases, the sensitivity and specifity were 88.9% and 100%, and the corresponding point of the average diagnostic sensitivity and specificity of 13 dermatologists were at the lower right of the curve.  Conclusions  The CNN binary classification model based on the intelligent analysis of dermoscopic images can accurately classify early MF and inflammatory dermatosis, and its ability of differential diagnosis is better than the average level of dermatologists.
    • FullText(HTML)
  • loading
    • References(18)
  • [1] 刘洁, 邹先彪. 实用皮肤镜学[M]. 北京: 人民卫生出版社, 2021: 1-249.
    [2] Olsen E, Vonderheid E, Pimpinelli N, et al. Revisions to the staging and classification of mycosis fungoides and Sezary syndrome: a proposal of the International Society for Cutaneous Lymphomas (ISCL) and the cutaneous lymphoma task force of the European Organization of Research and Treatment of Cancer (EORTC)[J]. Blood, 2007, 110: 1713-1722. doi:  10.1182/blood-2007-03-055749
    [3] Krizhevsky A, Sutskever I, Hinton G. ImageNet Classifica-tion with Deep Convolutional Neural Networks[C]. Advances in Neural Information Processing Systems, 2012, 25: 1097-1105.
    [4] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv, 2014: 1409.1556V6.
    [5] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE Confer-ence on Computer Vision and Pattern Recognition, 2016: 770-778.
    [6] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700-4708.
    [7] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
    [8] Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks[C]. International Confer-ence on Machine Learning, 2019: 6105-6114.
    [9] Hunt RJ. Percent agreement, Pearson's correlation, and kappa as measures of inter-examiner reliability[J]. J Dent Res, 1986, 65: 128-130. doi:  10.1177/00220345860650020701
    [10] Lallas A, Apalla Z, Lefaki I, et al. Dermoscopy of early stage mycosis fungoides[J]. J Eur Acad Dermatol Venereol, 2013, 27: 617-621. doi:  10.1111/j.1468-3083.2012.04499.x
    [11] Ghahramani GK, Goetz KE, Liu V. Dermoscopic characterization of cutaneous lymphomas: a pilot survey[J]. Int J Dermatol, 2018, 57: 339-343. doi:  10.1111/ijd.13860
    [12] Xu C, Liu J, Wang T, et al. Dermoscopic patterns of early-stage mycosis fungoides in a Chinese population[J]. Clin Exp Dermatol, 2019, 44: 169-175. doi:  10.1111/ced.13680
    [13] Bilgic SA, Cicek D, Demir B. Dermoscopy in differential diagnosis of inflammatory dermatoses and mycosis fungoides[J]. Int J Dermatol, 2020, 59: 843-850. doi:  10.1111/ijd.14925
    [14] 谢凤英, 刘洁, 崔勇, 等. 皮肤镜图像计算机辅助诊断技术[J]. 中国医学文摘(皮肤科学), 2016, 33: 45-50. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYXW201601011.htm

    Xie FY, Liu J, Cui Y, et al. Computer aided diagnosis of dermoscopic images[J]. Zhongguo Yixue Wenzhai(Pifu Kexue), 2016, 33: 45-50. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYXW201601011.htm
    [15] Schindewolf T, Stolz W, Albert R, et al. Classification of melanocytic lesions with color and texture analysis using digital image processing[J]. Anal Quant Cytol Histol, 1993, 1: 1-11. http://europepmc.org/abstract/MED/8471104
    [16] 谢斌, 何小宇, 黄伟红, 等. 基于卷积神经网络的基底细胞癌和色素痣的临床图像鉴别[J]. 中南大学学报(医学版), 2019, 44: 113-120. https://www.cnki.com.cn/Article/CJFDTOTAL-HNYD201909019.htm

    Xie B, He XY, Huang WH, et al. Clinical image identification of basal cell carcinoma and pigmented nevi based on convolutional neural network[J]. Zhongnan Daxue Xuebao (Yixueban), 2019, 44: 113-120. https://www.cnki.com.cn/Article/CJFDTOTAL-HNYD201909019.htm
    [17] Serener A, Serte S. Keratinocyte carcinoma detection via convolutional neural networks[C]. 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2019: 1-5.
    [18] Pangti R, Mathur J, Chouhan V, et al. A machine learning‐based, decision support, mobile phone application for diagnosis of common dermatological diseases[J]. J Eur Acad Dermatol Venereol, 2021, 35: 536-545. doi:  10.1111/jdv.16967
    • Relative Articles(0)
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (742) PDF downloads(62) Cited by()
    Proportional views
    Related
    Links: National Health Commission of the People's Republic of China Chinese Academy of Medical Sciences(CAMS) AMLC Check CNKI Database CNKI Term Online Chinese General Practice
    Address：Room 302,Third Floor,Building 7, No. 1 Shuaifuyuan, Wangfujing, Dongcheng District, Beijing 100730, China
    Tel：010-69154261/4262
    E - mail：medj@pumch.cn
    mjpumch@126.com
    Copyright of website：Editorial Office of Medical Journal of Peking Union Medical College Hospital

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return