AI assistance enhances histopathologic distinction between sebaceous and squamous cell carcinoma of the eyelid.

Journal: NPJ digital medicine
Published Date:

Abstract

Sebaceous gland carcinoma (SGC) and some poorly differentiated squamous cell carcinomas (SC) of the eyelid may have overlapping clinical and histopathologic features, leading to potential misdiagnosis and delayed treatment. The authors developed a deep learning (DL)-based pathological analysis framework to classify SGC and SC automatically. In total, 282 whole slide images (WSIs) were used for training, validating and inner testing the DL framework and 36 WSIs were obtained from another hospital as an external testing dataset. In WSI level, the diagnostic accuracy of SGC and SC achieved 84.85% and 75.12%, respectively, in the internal testing set and reached 22.22% and 77.78%, respectively, in the external testing set. The accuracy of the pathologists could be improved with the AI framework (60.0 ± 9.8% vs. 76.8 ± 9.6%). This AI-based automatic pathological diagnostic framework achieved the performance of a well-experienced pathologist and can assist pathologists in making diagnoses more accurately, especially for non-ophthalmic pathologists.

Authors

  • Jialu Geng
    Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology&Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Kai Zhang
    Anhui Province Key Laboratory of Respiratory Tumor and Infectious Disease, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
  • Li Dong
    Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, China. donglikn199@163.com.
  • Shiqi Hui
    Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology&Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Qian Zhang
    The Neonatal Intensive Care Unit, Peking Union Medical College Hospital, Peking, China.
  • Zhixi Li
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.
  • Ruiheng Zhang
    Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Xue Jiang
    Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
  • Mingyang Wang
    Department of Ultrasound, Tianjin First Central Hospital, NanKai University, Tianjin, 300192, China.
  • Shuantao Sun
    Senior Department of Ophthalmology, The Third Medical Center of Chinese PLA General Hospital, Beijing, China.
  • Hong Zhang
    Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
  • Yunyun Yang
    Guangdong Provincial Key Laboratory of Chemical Measurement and Emergency Test Technology, Guangdong Provincial Engineering Research Center for Ambient Mass Spectrometry, Institute of Analysis, Guangdong Academy of Sciences (China National Analytical Center, Guangzhou), Guangzhou, 510070, China.
  • Xinji Yang
    Senior Department of Ophthalmology, The Third Medical Center of Chinese PLA General Hospital, Beijing, China. yangxinji68@sina.com.
  • Yingshi Piao
    Department of Pathology, Beijing Tongren Hospital Affiliated with Capital Medical University, Beijing Key Laboratory of Head and Neck Pathology Diagnosis, Beijing, China. piaoyingshi2013@163.com.
  • Dongmei Li
    Clinical and Translational Science Institute, University of Rochester School of Medicine and Dentistry, Rochester, New York.

Keywords

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