摘要
目的比较基于皮肤镜图像的卷积神经网络(convolutional neural network,CNN)二分类模型在蕈样肉芽肿(mycosis fungoides,MF)与炎症性疾病鉴别诊断中的应用价值。方法回顾性纳入2016年1月至2020年12月北京协和医院皮肤科门诊确诊的早期MF患者和临床表现与之相似的炎症性皮肤病患者,并按4∶1的比例随机分为训练集和测试集。使用训练集患者的皮肤镜图像对6种经典网络结构利用迁移学习进行训练,以构建CNN二分类模型。同时,在测试集中随机挑选每例患者1幅图像,并结合皮损的临床图像,由13名皮肤科医师对疾病归类进行判读。比较CNN二分类模型与皮肤科医师对测试集病例早期MF与炎症性疾病鉴别诊断的性能,结果以曲线下面积(area under the curve,AUC)、灵敏度、特异度、Kappa值等表示,并采用受试者工作特征(receiver operating characteristic,ROC)曲线进行可视化分析。结果共纳入48例早期MF患者(皮肤镜图像402幅)和96例炎症性皮肤病患者(皮肤镜图像557幅),其中训练集117例(皮肤镜图像772幅),测试集27例(皮肤镜图像187幅)。测试集中,皮肤科医师鉴别诊断早期MF与炎症性皮肤病的灵敏度和特异度分别为70.19%(95%CI:59.68%~80.70%)和94.74%(95%CI:91.77%~97.71%),Kappa值为0.677(95%CI:0.566~0.789)。按图像分类时,CNN二分类模型对早期MF与炎症性皮肤病鉴别诊断的AUC为0.87(95%CI:0.84~0.89),灵敏度和特异度分别为75.02%(95%CI:70.19%~79.85%)和82.02%(95%CI:79.30%~84.87%),Kappa值为0.563(95%CI:0.507~0.620);按病例分类时,CNN二分类模型对早期MF与炎症性皮肤病鉴别诊断的AUC为0.97(95%CI:0.95~0.99),灵敏度和特异度分别为87.50%(95%CI:78.55%~96.45%)和93.85%(95%CI:88.93%~98.77%),Kappa值为0.920(95%CI:0.884~0.954)。ROC曲线显示,按病例分类时网络结构为EfficientNet-B0的CNN二分类模型诊断早期MF的AUC为0.99,灵敏度和特异度分别为88.9%和100%,且13名皮肤科医师诊断的灵敏度和�
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%
作者
刘兆睿
张漪澜
谢凤英
刘洁
LIU Zhaorui;ZHANG Yilan;XIE Fengying;LIU Jie(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;Image Processing Center,School of Astronautics,Beihang University,Beijing 100191,China)
出处
《协和医学杂志》
CSCD
2021年第5期689-697,共9页
Medical Journal of Peking Union Medical College Hospital
基金
国家自然科学基金(61871011,62071011,61771031,82173449)
中国医学科学院中央级公益性科研院所基本科研业务费专项资金(2019XK320024)
北京市自然科学基金(4192032)。
关键词
早期蕈样肉芽肿
炎症性皮肤病
皮肤镜图像
卷积神经网络
early mycosis fungoides
inflammatory dermatosis
dermoscopic image
convolutional neural network
