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.
• 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
Digital Imaging and Communications in Medicine
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
Receiver operating characteristic
Spread through air space
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The authors state that this work has not received any funding.
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.
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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). https://doi.org/10.1007/s00330-020-06694-z
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