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A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images



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.


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.


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.


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.

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Fig. 1
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Atypical adenomatous hyperplasia




Adenocarcinoma in situ


Area under a ROC curve


Computer-aided diagnosis


Convolutional neural network


Computed tomography


Disease-free survival


Fully connected


Ground-glass nodules


Invasive adenocarcinoma


Matthews correlation coefficient


Minimally invasive adenocarcinoma


Non-small cell lung cancer


Quantitative imaging


Rectified linear unit


Receiver operating characteristic


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This work was partially funded by China Postdoctoral Science Foundation under Grant No. 2019M651372, the Shanghai Science and Technology Funds under Grant No. 13411950107, the Natural Science Foundation of Shanghai under Grant No. 14ZR1427900.

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Corresponding authors

Correspondence to Shengping Wang or Weijun Peng.

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The scientific guarantor of this publication is Weijun Peng.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• multicenter study

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Gong, J., Liu, J., Hao, W. et al. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol 30, 1847–1855 (2020).

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  • Multiple pulmonary nodules
  • Computer-assisted image interpretation
  • Carcinoma
  • Lung neoplasms
  • X-Ray computed tomography scanners