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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma



Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography (CT)‑based radiomics model for preoperative prediction of STAS in lung adenocarcinoma.

Methods and materials

This retrospective study was approved by an institutional review board and included 462 (mean age, 58.06 years) patients with pathologically confirmed lung adenocarcinoma. STAS was identified in 90 patients (19.5%). Two experienced radiologists segmented and extracted radiomics features on preoperative thin-slice CT images with radiomics extension independently. Intraclass correlation coefficients (ICC) and Pearson’s correlation were used to rule out those low reliable (ICC < 0.75) and redundant (r > 0.9) features. Univariate logistic regression was applied to select radiomics features which were associated with STAS. A radiomics-based machine learning predictive model using a random forest (RF) was developed and calibrated with fivefold cross-validation. The diagnostic performance of the model was measured by the area under the curve (AUC) of receiver operating characteristic (ROC).


With univariate analysis, 12 radiomics features and age were found to be associated with STAS significantly. The RF model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.


CT-based radiomics model can preoperatively predict STAS in lung adenocarcinoma with good diagnosis performance.

Key Points

• CT-based radiomics and machine learning model can predict spread through air space (STAS) in lung adenocarcinoma with high accuracy.

• The random forest (RF) model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.

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Three-dimensional volumes of interest


Area under curve


Computed tomography


Digital Imaging and Communications in Medicine


Ground-glass nodules


Gray level co-occurrence matrix


Gray level dependence matrix


Gray level run length matrix


Gray Level size zone matrix


High gray level emphasis


Intraclass correlation coefficients




Low-dose computed tomography.


Magnetic resonance imaging


Neighboring gray tone difference matrix


Picture archiving and communication system


Personal computer


Random forest


Receiver operating characteristic


Spread through air space


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Correspondence to Jingshan Gong.

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The scientific guarantor of this publication is Jingshan Gong.

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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

• Observational

• Performed at one institution

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Jiang, C., Luo, Y., Yuan, J. et al. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol 30, 4050–4057 (2020).

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  • Lung
  • Adenocarcinoma
  • Metastasis
  • Radiomics
  • Machine learning