Clinically Applicable Pathological Diagnosis System for Cell Clumps in Endometrial Cancer Screening via Deep Convolutional Neural Networks

Qing Li; Ruijie Wang; Zhonglin Xie; Lanbo Zhao; Yiran Wang; Chao Sun; Lu Han; Yu Liu; Huilian Hou; Chen Liu; Guanjun Zhang; Guizhi Shi; Dexing Zhong; Qiling Li

doi:10.3390/cancers14174109

Clinically Applicable Pathological Diagnosis System for Cell Clumps in Endometrial Cancer Screening via Deep Convolutional Neural Networks

Cancers (Basel). 2022 Aug 25;14(17):4109. doi: 10.3390/cancers14174109.

Authors

Qing Li^{1

2}, Ruijie Wang³, Zhonglin Xie³, Lanbo Zhao¹, Yiran Wang¹, Chao Sun¹, Lu Han¹, Yu Liu⁴, Huilian Hou⁴, Chen Liu², Guanjun Zhang⁴, Guizhi Shi⁵, Dexing Zhong^{3

6

7}, Qiling Li^{1

2}

Affiliations

¹ Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
² Department of Obstetrics and Gynecology, Northwest Women's and Children's Hospital, Xi'an 710061, China.
³ School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
⁴ Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
⁵ Laboratory Animal Center, Institute of Biophysics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100101, China.
⁶ State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China.
⁷ Pazhou Lab, Guangzhou 510335, China.

Abstract

Objectives: The soaring demand for endometrial cancer screening has exposed a huge shortage of cytopathologists worldwide. To address this problem, our study set out to establish an artificial intelligence system that automatically recognizes and diagnoses pathological images of endometrial cell clumps (ECCs).

Methods: We used Li Brush to acquire endometrial cells from patients. Liquid-based cytology technology was used to provide slides. The slides were scanned and divided into malignant and benign groups. We proposed two (a U-net segmentation and a DenseNet classification) networks to identify images. Another four classification networks were used for comparison tests.

Results: A total of 113 (42 malignant and 71 benign) endometrial samples were collected, and a dataset containing 15,913 images was constructed. A total of 39,000 ECCs patches were obtained by the segmentation network. Then, 26,880 and 11,520 patches were used for training and testing, respectively. On the premise that the training set reached 100%, the testing set gained 93.5% accuracy, 92.2% specificity, and 92.0% sensitivity. The remaining 600 malignant patches were used for verification.

Conclusions: An artificial intelligence system was successfully built to classify malignant and benign ECCs.

Keywords: cell clumps; deep learning; endometrial cancer; pathological diagnosis system; screening.

Grants and funding

This study was supported by the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University, China (No. XJTU1AF-CRF-2019-002); the Natural Science Basic Research Program of Shaanxi (2017ZDJC-11, 2018JM7073); the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University, China (XJTU1AF-2018-017); the Key Research and Development Program of Shaanxi (2017ZDXM-SF-068, 2019QYPY-138); the Innovation Capability Support Program of Shaanxi (2017XT-026, 2018XT-002); and the Medical Research Project of Xi’an Social Development Guidance Plan (2017117SF/YX011-3). The sponsors had no involvement in the study’s design, data collection, analysis, and interpretation, or in writing of the manuscript.