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Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT

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Abstract

Objectives

To develop a model for differentiating the predominant subtype-based prognostic groups of lung adenocarcinoma using CT radiomic features, and to validate its performance in comparison with radiologists’ assessments.

Methods

A total of 993 patients presenting with invasive lung adenocarcinoma between March 2010 and June 2016 were identified. Predominant histologic subtypes were categorized into three groups according to their prognosis (group 0: lepidic; group 1: acinar/papillary; group 2: solid/micropapillary). Seven hundred eighteen radiomic features were extracted from segmented lung cancers on contrast-enhanced CT. A model-development set was formed from the images of 893 patients, while 100 image sets were reserved for testing. A least absolute shrinkage and selection operator method was used for feature selection. Performance of the radiomic model was evaluated using receiver operating characteristic curve analysis, and accuracy on the test set was compared with that of three radiologists with varying experiences (6, 7, and 19 years in chest CT).

Results

Our model differentiated the three groups with areas under the curve (AUCs) of 0.892 and 0.895 on the development and test sets, respectively. In pairwise discrimination, the AUC was highest for group 0 vs. 2 (0.984). The accuracy of the model on the test set was higher than the averaged accuracy of the three radiologists without statistical significance (73.0% vs. 61.7%, p = 0.059). For group 2, the model achieved higher PPV than the observers (85.7% vs. 35.0–48.4%).

Conclusions

Predominant subtype-based prognostic groups of lung adenocarcinoma were classified by a CT-based radiomic model with comparable performance to radiologists.

Key Points

• A CT-based radiomic model differentiated three prognosis-based subtype groups of lung adenocarcinoma with areas under the curve (AUCs) of 0.892 and 0.895 on development and test sets, respectively.

• The CT-based radiomic model showed near perfect discrimination between group 0 and group 2 (AUCs, 0.984–1.000).

• The accuracy of the CT-based radiomic model was comparable to the averaged accuracy of the three radiologists with 6, 7, and 19 years of clinical experience in chest CT (73.0% vs. 61.7%, p = 0.059), achieving a higher positive predictive value for group 2 than the observers (85.7% vs. 35.0–48.4%).

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Abbreviations

AUC:

Area under the curve

CI:

Confidence interval

CT:

Computed tomography

LASSO:

Least absolute shrinkage and selection operator

ROC:

Receiver operating characteristic curve

WHO:

World Health Organization

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Funding

This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (grant number: NRF-2016R1A2B1016355).

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Authors and Affiliations

  1. Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138-736, South Korea

    Sohee Park, Sang Min Lee, Han Na Noh, Hye Jeon Hwang, Kyung-Hyun Do & Joon Beom Seo

  2. Department of Medical Statistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea

    Seonok Kim

Authors
  1. Sohee Park
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  2. Sang Min Lee
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  3. Han Na Noh
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  4. Hye Jeon Hwang
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  5. Seonok Kim
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  6. Kyung-Hyun Do
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  7. Joon Beom Seo
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Corresponding author

Correspondence to Sang Min Lee.

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Guarantor

The scientific guarantors of this publication are Sang Min Lee.

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

The statistician Seon Ok Kim who is one of the authors of our study provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Park, S., Lee, S.M., Noh, H.N. et al. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT. Eur Radiol 30, 4883–4892 (2020). https://doi.org/10.1007/s00330-020-06805-w

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  • DOI: https://doi.org/10.1007/s00330-020-06805-w

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