基于深度学习算法的胃炎组织病理学诊断系统

巴伟; 王书浩; 刘灿城; 王跃峰; 石怀银; 宋志刚

doi:10.24920/003962

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基于深度学习算法的胃炎组织病理学诊断系统

中国医学科学杂志（英文版） 2021年36卷第3期页码：204-209

论著 | Updated：2024-04-10

- 基于深度学习算法的胃炎组织病理学诊断系统
- Affiliations：
  
  1. 1Department of Pathology, Chinese PLA General Hospital & Medical School, Beijing 100853, China
  2. 2Artificial Intelligence Lab, Thorough Images,Beijing 100853, China
- Author bio：
  
  *Tel: 86-13810410643; E-mail: songzhg301@139.com
- Funds：
  
  supported by the PLA General Hospital Medical Big Data and Artificial Intelligence Project;解放军总医院医疗大数据与人工智能研发项目(2019MBD-038)
- DOI：10.24920/003962
  中图分类号：
- 收稿日期：2021-06-29，
  
  录用日期：2021-8-18，
  
  网络出版日期：2021-08-31，
  
  纸质出版日期：2021-08-31
- Accepted：
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巴伟, 王书浩, 刘灿城, 等. 基于深度学习算法的胃炎组织病理学诊断系统[J]. 中国医学科学杂志（英文版）, 2021,36(3):204-209.

Ba Wei, Wang Shuhao, Liu Cancheng, et al. Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm[J]. Chinese medical sciences journal, 2021, 36(3): 204-209.
巴伟, 王书浩, 刘灿城, 等. 基于深度学习算法的胃炎组织病理学诊断系统[J]. 中国医学科学杂志（英文版）, 2021,36(3):204-209. DOI： 10.24920/003962.

Ba Wei, Wang Shuhao, Liu Cancheng, et al. Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm[J]. Chinese medical sciences journal, 2021, 36(3): 204-209. DOI： 10.24920/003962.

摘要

目的

开发一种用于慢性胃炎病理分类的深度学习算法

并使用全切片病理图像（whole slide images

WSI）评估其性能。

方法

回顾性收集解放军总医院胃活检标本1

250例（胃炎1

128例

正常胃黏膜122例）。分别使用1

008张和100张WSIs

基于DeepLab v3（ResNet-50）架构训练和验证深度学习算法

并在142张WSIs的独立测试集上测试该算法对不同胃炎亚型的诊断效能。

结果

模型为测试集中的慢性浅表性胃炎（chronic superficial gastritis

CSuG）、慢性活动性胃炎（chronic active gastritis

CAcG）和慢性萎缩性胃炎（chronic atrophic gastritis

CAtG）作出诊断所生成的受试者工作特征（receiver operating characteristic

ROC）曲线的曲线下面积分别为0.882、0.905和0.910。深度学习算法对CSuG、CAcG和CAtG分类的敏感性和特异性分别为0.790和1.000（准确度0.880）、0.985和0.829（准确度0.901）、0.952和0.992（准确度0.986）。对三种不同类型胃炎诊断的总体准确度为 0.867。通过在 WSI 中标记算法识别的可疑区域

可以生成更为透明和可解释的诊断结果。

结论

深度学习算法使用WSI对慢性胃炎进行病理学分型具有较高的准确性。通过预先标记出不同类型胃炎的区域

深度学习算法可以作为辅助诊断工具

提高病理医生的工作效率。

Abstract

Objective

To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs).

Methods

We retrospectively collected 1

250 gastric biopsy specimens (1

128 gastritis

122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1

008 WSIs and 100 WSIs

respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs

with the pathologists’ consensus diagnosis as the gold standard.

Results

The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG)

chronic active gastritis (CAcG)

and chronic atrophic gastritis (CAtG) in the test set

respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG

CAcG

and CAtG were 0.882

0.905 and 0.910

respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG

CAcG

and CAtG were 0.790 and 1.000 (accuracy 0.880)

0.985 and 0.829 (accuracy 0.901)

0.952 and 0.992 (accuracy 0.986)

respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI

a more transparent and interpretable diagnosis can be generated.

Conclusion

The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions

it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.

关键词

Keywords

references

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