1
|
Luo Y, Zhang J, Yang Y, Rao Y, Chen X, Shi T, Xu S, Jia R, Gao X. Deep learning-based fully automated differential diagnosis of eyelid basal cell and sebaceous carcinoma using whole slide images. Quant Imaging Med Surg 2022; 12:4166-4175. [PMID: 35919066 PMCID: PMC9338367 DOI: 10.21037/qims-22-98] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 06/01/2022] [Indexed: 11/06/2022]
Abstract
Background The differential diagnosis of eyelid basal cell carcinoma (BCC) and sebaceous carcinoma (SC) is highly dependent on pathologist’s experience. Herein, we proposed a fully automated differential diagnostic method, which used deep learning (DL) to accurately classify eyelid BCC and SC based on whole slide images (WSIs). Methods We used 116 haematoxylin and eosin (H&E)-stained sections from 116 eyelid BCC patients and 180 H&E-stained sections from 129 eyelid SC patients treated at the Shanghai Ninth People’s Hospital from 2017 to 2019. The method comprises two stages: patch prediction by the DenseNet-161 architecture-based DL model and WSI differentiation by an average-probability strategy-based integration module, and its differential performance was assessed by the carcinoma differentiation accuracy and F1 score. We compared the classification performance of the method with that of three pathologists, two junior and one senior. To validate the auxiliary value of the method, we compared the pathologists’ BCC and SC classification with and without the assistance of our proposed method. Results Our proposed method achieved an accuracy of 0.983, significantly higher than that of the three pathologists (0.644 and 0.729 for the two junior pathologists and 0.831 for the senior pathologist). With the method’s assistance, the pathologists’ accuracy increased significantly (P<0.05), by 28.8% and 15.2%, respectively, for the two junior pathologists and by 11.8% for the senior pathologist. Conclusions Our proposed method accurately classifies eyelid BCC and SC and effectively improves the diagnostic accuracy of pathologists. It may therefore facilitate the development of appropriate and timely therapeutic plans.
Collapse
Affiliation(s)
- Yingxiu Luo
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Jiayi Zhang
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.,School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, China
| | - Yidi Yang
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Yamin Rao
- Department of Pathology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xingyu Chen
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Tianlei Shi
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.,School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, China
| | - Shiqiong Xu
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Renbing Jia
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Xin Gao
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.,School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, China.,Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Jinan, China
| |
Collapse
|