Clinical radiomics nomogram and deep learning based on CT in discriminating atypical pulmonary hamartoma from lung adenocarcinoma
10.19405/j.cnki.issn1000-1492.2024.02.026
- VernacularTitle: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
- Author:
Chuanbin WANG
1
,
2
;
Cuiping LI
;
Feng CAO
;
Yankun GAO
;
Baoxin QIAN
;
Jiangning DONG
;
Xingwang WU
Author Information
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
- Keywords:
hamartoma;
lung adenocarcinoma;
nomogram;
deep learning;
artificial intelligence;
computed tomo-graphy
- From:
Acta Universitatis Medicinalis Anhui
2024;59(2):344-350
- CountryChina
- Language:Chinese
-
Abstract:
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 institu-tion 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 com-petition experiment,AI tended to downgrade more Lung-RADS categories in APH and affirm more Lung-RADS cat-egories 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.
