Application of artificial intelligence in histopathologic diagnosis and differentiation of extramammary Paget's disease
10.16016/j.2097-0927.202401014
- VernacularTitle:人工智能在乳房外Paget病病理诊断及鉴别中的应用
- Author:
Yiwei ZHU
1
;
Zhe WU
;
Xingcai CHEN
;
Yongjian NIAN
;
Na LUO
;
Lian ZHANG
;
Yi WU
;
Zhifang ZHAI
Author Information
1. 400038 重庆,陆军军医大学(第三军医大学):第一附属医院皮肤科
- Keywords:
Paget's disease,extramammary;
artificial intelligence;
diagnostic model;
histopathologic diagnosis
- From:
Journal of Army Medical University
2024;46(16):1897-1905
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish an artificial intelligence(AI)diagnostic model for the histopathologic diagnosis of extramammary Paget's disease(EMPD)and to evaluate its efficiency for the diagnosis and differential diagnosis of EMPD.Methods All non-tumor skin disease patients who underwent skin tissue biopsy in Department of Dermatology of First Affiliated Hospital of Army Medical University from September 2003 to February 2023 were recruited,and their pathological data were collected,including EMPD,Bowen's disease(BD),squamous cell carcinoma(SCC),and epidermal hyperplasia and hypertrophy.With EMPD as the main research subject,the histopathological images of BD,SCC,and non-tumor skin diseases were included in the study.The histopathological data of 4 types of diseases was classified and diagnosed by ResNet101 and DenseNet121 deep learning neural networks,and the performance of these models was evaluated.Results The AUC values of the ResNet101 diagnostic model for the diagnosis of EMPD,BD,SCC and non-tumor skin diseases on the images at x20 magnification were 0.97,0.98,1.00 and 0.96,respectively,with an accuracy of 0.925±0.011,while the AUC values on the images at x40 magnification were 1.00,0.99,1.00 and 0.97,respectively,with an accuracy of 0.943±0.017.The AUC values of the DenseNet121 diagnostic model for the diagnosis of 4 diseases on the images at x20 magnification were 0.98,0.95,0.99 and 1.00,respectively,with an accuracy of 0.912±0.034,while the AUC values on the images at x40 magnification were 0.99,0.96,1.00 and 1.00,respectively,with an accuracy of 0.971±0.012.Our results indicated that the histopathologic diagnostic model could effectively differentiate EMPD from BD,SCC and non-tumor skin diseases at low power magnification.The FLPOs of ResNet101 was 786.6 M,and the parameter was 4.5 M;The FLPOs of DensNet121 was 289.7 M,and the parameter was 0.8M.Conclusion Our AI diagnostic model is of good effectiveness in the diagnosis and differential diagnosis of EMPD.DenseNet121 is recommended as the dermatopathological diagnostic model of this study.
