The Invasiveness Classification of Ground-Glass Nodules Using 3D Attention Network and HRCT.
The early stage lung cancer often appears as ground-glass nodules (GGNs). The diagnosis of GGN as preinvasive lesion (PIL) or invasive adenocarcinoma (IA) is very important for further treatment planning. This paper proposes an automatic GGNs' invasiveness classification algorithm for the adenocarci...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 5; pp. 1144 - 1155 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146532214&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146532214 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2020 vid: 33 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146532214 144720208 146532214 146532214 10.1007/s10278-020-00355-9 146532214 ppf: 1144 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The Invasiveness Classification of Ground-Glass Nodules Using 3D Attention Network and HRCT. aug: au: Ni, Yangfan Yang, Yuanyuan Zheng, Dezhong Xie, Zhe Huang, Haozhe Wang, Weidong affil: Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, 200083, Shanghai, China sug: subj: Lung Neoplasms Adenocarcinoma Neoplasm Invasiveness Classification Image Interpretation, Computer Assisted Algorithms China Human Neural Networks (Computer) Sensitivity and Specificity Random Forest Machine Learning ab: The early stage lung cancer often appears as ground-glass nodules (GGNs). The diagnosis of GGN as preinvasive lesion (PIL) or invasive adenocarcinoma (IA) is very important for further treatment planning. This paper proposes an automatic GGNs' invasiveness classification algorithm for the adenocarcinoma. 1431 clinical cases and a total of 1624 GGNs (3–30 mm) were collected from Shanghai Cancer Center for the study. The data is in high-resolution computed tomography (HRCT) format. Firstly, the automatic GGN detector which is composed by a 3D U-Net and a 3D multi-receptive field (multi-RF) network detects the location of GGNs. Then, a deep 3D convolutional neural network (3D-CNN) called Attention-v1 is used to identify the GGNs' invasiveness. The attention mechanism was introduced to the 3D-CNN. This paper conducted a contract experiment to compare the performance of Attention-v1, ResNet, and random forest algorithm. ResNet is one of the most advanced convolutional neural network structures. The competition performance metrics (CPM) of automatic GGN detector reached 0.896. The accuracy, sensitivity, specificity, and area under curve (AUC) value of Attention-v1 structure are 85.2%, 83.7%, 86.3%, and 92.6%. The algorithm proposed in this paper outperforms ResNet and random forest in sensitivity, accuracy, and AUC value. The deep 3D-CNN's classification result is better than traditional machine learning method. Attention mechanism improves 3D-CNN's performance compared with the residual block. The automatic GGN detector with the addition of Attention-v1 can be used to construct the GGN invasiveness classification algorithm to help the patients and doctors in treatment. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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