摘要
目的研究并建立原发性骨肿瘤的开源影像数据库及智能辅助诊断系统。方法回顾性搜集2013年1月至2022年12月期间在六家医疗机构确诊为原发性骨肿瘤患者的三项临床信息(性别、年龄、部位)和影像学检查资料(X线、CT和MRI影像)作为数据库输入资料,采用B/S技术在云平台构建开源的数据库。采用部分数据库影像数据,通过深度学习算法对X线图像构建原发性骨肿瘤良恶性分类辅助诊断系统并验证性能。结果成功建立了一个功能友好的开源原发性骨肿瘤影像数据库,数据库包含2127份病例;同时基于本数据库X线图像开发了一个辅助诊断系统,并作为诊断模块整合至数据库中,系统在测试集平均受试者工作特征曲线曲线下面积(AUC)为0.8499。结论构建了一个开源原发性骨肿瘤影像数据库,并据此验证性地开发了一个原发性骨肿瘤辅助诊断系统,为骨肿瘤医学研究和临床实践提供了有力工具。
Objective The aim of this study is to develop an open-source imaging database and an intelligent assisted diagnosis system for primary bone tumors.Methods Clinical information(gender,age,and location)and radiological data(X-ray,CT,and MRI)from patients diagnosed with primary bone tumors at six healthcare facilities between January 2013 and December 2022 were retrospectively collected and inputted into the database,which was constructed on a cloud platform using B/S technology.A subset of the database's imaging data was utilized to develop and validate a diagnostic support system that classifies primary bone tumors into benign or malignant categories using deep learning algorithms applied to X-ray images.Results A user-friendly open-source imaging database for primary bone tumors has been successfully established,containing 2,127 cases;an auxiliary diagnostic system based on this database's X-ray images has been developed and integrated as a diagnostic module within the database,achieving an average AUC of 84.99%on the test set.Conclusion In this study,we constructed an open-source primary bone tumor image database and validated the development of a primary bone tumor-assisted diagnosis system based on it,which provides a powerful tool for bone tumor medical research and clinical practice.
作者
黄元祥
姚顺晗
李世源
陈庆锋
HUANG Yuanxiang;YAO Shunhan;LI Shiyuan(School of Computer,Electronics and Information in Guangxi University,Nanning,Guangxi Province 530004,P.R.China)
出处
《临床放射学杂志》
北大核心
2024年第10期1782-1786,共5页
Journal of Clinical Radiology
基金
国家留学基金项目(编号:202306660024)
广西科技计划项目(编号:桂科AD23026308)。
关键词
原发性骨肿瘤
医学影像数据库
辅助诊断
深度学习
Primary bone tumor
Medical image database
Computer-assisted diagnosis
Deep learning
作者简介
通讯作者:陈庆锋。
