A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.
Objective: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists.Methods: First, we ret...
| Publicado en: | European Radiology Vol. 30; no. 4; pp. 1847 - 1856 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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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=142141865&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142141865 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2020 vid: 30 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142141865 142141865 NLM31811427 142141865 10.1007/s00330-019-06533-w NLM31811427 142141865 ppf: 1847 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. aug: au: Gong, Jing Liu, Jiyu Hao, Wen Nie, Shengdong Zheng, Bin Wang, Shengping Peng, Weijun affil: Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, 200032, Shanghai, China sug: subj: Adenocarcinoma in Situ Lung Neoplasms Solitary Pulmonary Nodule Retrospective Design Image Processing, Computer Assisted Lung Neoplasms Pathology Artificial Intelligence Middle Age ROC Curve Pilot Studies Male Disease Progression Aged Solitary Pulmonary Nodule Pathology Young Adult Adolescence Adenocarcinoma in Situ Pathology Tomography, X-Ray Computed Methods Female Aged, 80 and Over Adult Neoplasm Invasiveness Funding Source Middle Aged: 45-64 years Aged: 65+ years Adolescent: 13-18 years Aged, 80 & over Adult: 19-44 years Male Female ab: Objective: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists.Methods: First, we retrospectively collected 828 histopathologically confirmed GGNs of 644 patients from two centers. Among them, 209 GGNs are confirmed IA and 619 are non-IA, including 409 adenocarcinomas in situ and 210 minimally invasive adenocarcinomas. Second, we applied a series of pre-preprocessing techniques, such as image resampling, rescaling and cropping, and data augmentation, to process original CT images and generate new training and testing images. Third, we built an AI scheme based on a deep convolutional neural network by using a residual learning architecture and batch normalization technique. Finally, we conducted an observer study and compared the prediction performance of the AI scheme with that of two radiologists using an independent dataset with 102 GGNs.Results: The new AI scheme yielded an area under the receiver operating characteristic curve (AUC) of 0.92 ± 0.03 in classifying between IA and non-IA GGNs, which is equivalent to the senior radiologist's performance (AUC 0.92 ± 0.03) and higher than the score of the junior radiologist (AUC 0.90 ± 0.03). The Kappa value of two sets of subjective prediction scores generated by two radiologists is 0.6.Conclusions: The study result demonstrates using an AI scheme to improve the performance in predicting IA, which can help improve the development of a more effective personalized cancer treatment paradigm.Key Points: • The feasibility of using a deep learning method to predict the likelihood of the ground-glass nodule being invasive adenocarcinoma. • Residual learning-based CNN model improves the performance in classifying between IA and non-IA nodules. • Artificial intelligence (AI) scheme yields higher performance than radiologists in predicting invasive adenocarcinoma. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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