Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features.

Journal: Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
Published Date: Jan 1, 2024

Abstract

BACKGROUND: Nuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteristics pathologists can use to distinguish and stratify this entity.

Authors

  • Xuemei Lan
    Department of Dermatopathology, Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
  • Guanchen Guo
    lnstitute for Al in Medicine, School of Artificial lntelligence, Nanjing University of Information Science and Technology, Nanjing, China.
  • Xiaopo Wang
    Department of Dermatopathology, Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
  • Qiao Yan
    Department of Dermatology, School of Medicine, Zhong Da Hospital, Southeast University, Nanjing, China.
  • Ruzeng Xue
    Dermatology Hospital, Southern Medical University, Guangzhou, China.
  • Yufen Li
    Department of Dermatopathology, Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
  • Jiaping Zhu
    Department of Dermatopathology, Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
  • Zhengbang Dong
    Department of Dermatology, School of Medicine, Zhong Da Hospital, Southeast University, Nanjing, China.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Guomin Li
    Dermatology Hospital, Southern Medical University, Guangzhou, China.
  • Xiangxue Wang
    lnstitute for Al in Medicine, School of Artificial lntelligence, Nanjing University of Information Science and Technology, Nanjing, China.
  • Jun Xu
    Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
  • Yiqun Jiang
    Hospital of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, #12 Jiangwangmiao Road, Nanjing, Jiangsu, China. yiqunjiang@qq.com.

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