基于CT临床放射组学列线图与深度学习鉴别非典型肺错构瘤和肺腺癌  

Clinical radiomics nomogram and deep learning based on CT in discriminating atypical pulmonary hamartoma from lung adenocarcinoma

作　　者：王传彬 李翠平 曹锋 郜言坤 钱宝鑫 董江宁 吴兴旺[1] Wang Chuanbin;Li Cuiping;Cao Feng;Gao Yankun;Qian Baoxin;Dong Jiangning;Wu Xingwang(Dept of Radiology,The First Affiliated Hospital of Anhui Medical University,Hefei 230022;Dept of Imaging,The First Affiliated Hospital of USTC,Division of Life Sciences and Medicine,University of Science and Technology of China,Hefei 230031;Huiying Medical Technology(Beijing),Beijing 100192)

机构地区：[1]安徽医科大学第一附属医院放射科,合肥230022 [2]中国科学技术大学附属第一医院(安徽省立医院)影像科,合肥230031 [3]北京汇医慧影医疗科技有限公司,北京100192

出　　处：《安徽医科大学学报》2024年第2期344-350,共7页Acta Universitatis Medicinalis Anhui

基　　金：安徽省自然科学基金面上项目(编号:2308085MH241)。

摘　　要：目的 探讨临床放射组学列线图(CRN)和深度学习卷积神经网络(DCNN)对非典型肺错构瘤(APH)和非典型肺腺癌(ALA)的鉴别诊断价值。方法 从两家医疗机构回顾性收集307例患者。机构1的患者按照7∶3的比例随机分为训练集(n=184:APH=97,ALA=87)和内部验证集(n=79:APH=41,ALA=38),机构2的患者作为外部验证集(n=44:APH=23,ALA=21)。分别建立CRN模型和DCNN模型,并采用德隆检验和受试者工作特性曲线(ROC)对两种模型的性能进行比较。通过人-机竞赛评估人工智能(AI)在肺结节Lung-RADS分类中的价值。结果 DCNN模型在训练集和内、外部验证集中的曲线下面积(AUC)均高于CRN模型(0.983 vs 0.968、0.973 vs 0.953、0.942 vs 0.932),但差异无统计学意义(P=0.23、0.31、0.34)。在放射科医师-AI竞争实验中,AI倾向于下调APH组中更多的Lung-RADS类别,并肯定ALA中更多的Lung-RADS类别。结论 DCNN及CRN在区分APH和ALA方面具有较高价值,前者表现更优;AI在评价肺结节的Lung-RADS分类方面优于放射科医师。Objective To discuss the value of clinical radiomic nomogram(CRN) and deep convolutional neural network(DCNN) in distinguishing atypical pulmonary hamartoma(APH) from atypical lung adenocarcinoma(ALA).Methods A total of 307 patients were retrospectively recruited from two institutions.Patients in institution 1 were randomly divided into the training(n=184:APH=97,ALA=87) and internal validation sets(n=79:APH=41,ALA=38) in a ratio of 7:3,and patients in institution 2 were assigned as the external validation set(n=44:APH=23,ALA=21).A CRN model and a DCNN model were established,respectively,and the performances of two models were compared by delong test and receiver operating characteristic(ROC) curves.A human-machine competition was conducted to evaluate the value of AI in the Lung-RADS classification.Results The areas under the curve(AUCs) of DCNN model were higher than those of CRN model in the training,internal and external validation sets(0.983 vs 0.968,0.973 vs 0.953,and 0.942 vs 0.932,respectively),however,the differences were not statistically significant(p=0.23,0.31 and 0.34,respectively).With a radiologist-AI competition experiment,AI tended to downgrade more Lung-RADS categories in APH and affirm more Lung-RADS categories in ALA than radiologists.Conclusion Both DCNN and CRN have higher value in distinguishing APH from ALA,with the former performing better.AI is superior to radiologists in evaluating the Lung-RADS classification of pulmonary nodules.

关 键 词：错构瘤 肺腺癌 列线图 深度学习 人工智能 计算机断层扫描 

分 类 号：R734.2[医药卫生—肿瘤] R[医药卫生—临床医学]