Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma

Abstract

Objectives

Lung adenocarcinomas which manifest as ground-glass nodules (GGNs) have different degrees of pathological invasion and differentiating among them is critical for treatment. Our goal was to evaluate the addition of marginal features to a baseline radiomics model on computed tomography (CT) images to predict the degree of pathologic invasiveness.

Methods

We identified 236 patients from two cohorts (training, n = 189; validation, n = 47) who underwent surgery for GGNs. All GGNs were pathologically confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). The regions of interest were semi-automatically annotated and 40 radiomics features were computed. We selected features using L1-norm regularization to build the baseline radiomics model. Additional marginal features were developed using the cumulative distribution function (CDF) of intratumoral intensities. An improved model was built combining the baseline model with CDF features. Three classifiers were tested for both models.

Results

The baseline radiomics model included five features and resulted in an average area under the curve (AUC) of 0.8419 (training) and 0.9142 (validation) for the three classifiers. The second model, with the additional marginal features, resulted in AUCs of 0.8560 (training) and 0.9581 (validation). All three classifiers performed better with the added features. The support vector machine showed the most performance improvement (AUC improvement = 0.0790) and the best performance was achieved by the logistic classifier (validation AUC = 0.9825).

Conclusion

Our novel marginal features, when combined with a baseline radiomics model, can help differentiate IA from AIS and MIA on preoperative CT scans.

Key Points

• Our novel marginal features could improve the existing radiomics model to predict the degree of pathologic invasiveness in lung adenocarcinoma.

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Abbreviations

AIS:

Adenocarcinoma in situ

AUC:

Area under the curve

CDF:

Cumulative distribution function

CT:

Computed tomography

DFS:

Disease-free survival

GGNs:

Ground-glass nodules

GLCM:

Gray-level co-occurrence matrix

HU:

Hounsfield unit

IA:

Invasive adenocarcinoma

ISZM:

Intensity size zone matrix

LASSO:

Least absolute shrinkage and selection operator

MIA:

Minimally invasive adenocarcinoma

MSE:

Mean squared error

RF:

Random forest

ROC:

Receiver operator characteristic

ROI:

Region of interest

SVM:

Support vector machine

VNC:

Virtual non-contrast-enhanced

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Funding

This research was supported by Korea Health Industry Development Institute (HI17C0086), National Research Foundation (NRF-2019R1H1A2079721 and NRF-2017M2A2A7A02018568), Ministry of Science and ICT (IITP-2019-2018-0-01798), IITP grant funded by the AI Graduate School Support Program (No. 2019-0-00421), and Institute for Basic Science (IBS-R015-D1).

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Correspondence to Ho Yun Lee or Hyunjin Park.

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The scientific guarantor of this publication is Ho Yun Lee

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Statistics and biometry

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Study subjects or cohort overlap

Some study subjects have been previously reported in (Son JY, Lee HY, Kim JH, et al (2016) Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non-invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping. Eur Radiol. https://doi.org/10.1007/s00330-015-3816-y.)

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• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Cho, Hh., Lee, G., Lee, H.Y. et al. Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma. Eur Radiol 30, 2984–2994 (2020). https://doi.org/10.1007/s00330-019-06581-2

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Keywords

  • Lung adenocarcinoma
  • Classification
  • Tumor microenvironment
  • Quantitative evaluation
  • Machine learning