医学
无线电技术
深度学习
逻辑回归
腺癌
淋巴结
肿瘤科
转移
阶段(地层学)
内科学
肺癌
放射科
人工智能
淋巴结转移
癌症
计算机科学
古生物学
生物
作者
Xingyu Zhao,Xiang Wang,Wei Xia,Qiong Li,Zhou Liu,Rui Zhang,Jiali Cai,Junming Jian,Li Fan,Wei Wang,Honglin Bai,Zhen Li,Yi Xiao,Yuguo Tang,Xin Gao,Shiyuan Liu
出处
期刊:Lung Cancer
[Elsevier]
日期:2020-07-01
卷期号:145: 10-17
被引量:41
标识
DOI:10.1016/j.lungcan.2020.04.014
摘要
Objectives The evaluation of lymph node (LN) status by radiologists based on preoperative computed tomography (CT) lacks high precision for early lung cancer patients; erroneous evaluations result in inappropriate therapeutic plans and increase the risk of complications. This study aims to develop a cross-modal 3D neural network based on CT images and prior clinical knowledge for accurate prediction of LN metastasis in clinical stage T1 lung adenocarcinoma. Patients and methods Five hundred one lung adenocarcinoma patients with clinical stage T1 were enrolled. Data including: corresponding 3D nodule-centered patches of CT; prior clinical features; and pathological labels of LN status were obtained. We proposed a cross-modal deep learning system, which can successfully incorporate prior clinical knowledge and CT images into a 3D neural network to predict LN metastasis. We trained and validated our system with 401 cases and tested its performance with 100 cases. The result was compared with that of the logistic regression integration model, the single deep learning model without prior clinical knowledge integration, radiomics method, and manual evaluation by radiologists. Results The model proposed DensePriNet achieved an AUC of 0.926, which is significantly higher than the logistic regression integration model (0.904) single deep learning model (0.880), and radiomics method (0.891). The Matthews Correlation Coefficient (MCC) of DensePriNet (0.705) was significantly higher than manual classification by one senior radiologist (0.534) and one junior radiologist (0.416), respectively. Conclusion The performance of the single deep learning method is significantly higher than the radiomics method and the radiologists, and integration of prior clinical knowledge into the deep learning model enhance the diagnostic precision of LN status and facilitate the application of precision medicine.
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