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

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

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.

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Abbreviations

AAH:

Atypical adenomatous hyperplasia

ACC:

Accuracy

AIS:

Adenocarcinoma in situ

AUC:

Area under a ROC curve

CADx:

Computer-aided diagnosis

CNN:

Convolutional neural network

CT:

Computed tomography

DFS:

Disease-free survival

FC:

Fully connected

GGN:

Ground-glass nodules

IAC:

Invasive adenocarcinoma

MCC:

Matthews correlation coefficient

MIA:

Minimally invasive adenocarcinoma

NSCLC:

Non-small cell lung cancer

QI:

Quantitative imaging

ReLU:

Rectified linear unit

ROC:

Receiver operating characteristic

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Funding

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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Author notes

  1. Jing Gong and Jiyu Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China

    Jing Gong, Wen Hao, Shengping Wang & Weijun Peng

  2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China

    Jing Gong, Shengping Wang & Weijun Peng

  3. Department of Radiology, Shanghai Pulmonary Hospital, 507 Zheng Min Road, Shanghai, 200433, China

    Jiyu Liu

  4. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jun Gong Road, Shanghai, 200093, China

    Shengdong Nie

  5. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA

    Bin Zheng

Authors
  1. Jing Gong
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Corresponding authors

Correspondence to Shengping Wang or Weijun Peng.

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Guarantor

The scientific guarantor of this publication is Weijun Peng.

Conflict of interest

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.

Methodology

• 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). https://doi.org/10.1007/s00330-019-06533-w

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  • DOI: https://doi.org/10.1007/s00330-019-06533-w

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