Abstract
Purpose
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).
Results
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.
Conclusion
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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Abbreviations
- 3D-VOIs:
-
Three-dimensional volumes of interest
- AUC:
-
Area under curve
- CT:
-
Computed tomography
- DICOM:
-
Digital Imaging and Communications in Medicine
- GGN:
-
Ground-glass nodules
- GLCM:
-
Gray level co-occurrence matrix
- GLDM:
-
Gray level dependence matrix
- GLRLM:
-
Gray level run length matrix
- GLSZM:
-
Gray Level size zone matrix
- HGLE:
-
High gray level emphasis
- ICC:
-
Intraclass correlation coefficients
- KV:
-
Kilovolt
- LDCT:
-
Low-dose computed tomography.
- MRI:
-
Magnetic resonance imaging
- NGTDM:
-
Neighboring gray tone difference matrix
- PACS:
-
Picture archiving and communication system
- PC:
-
Personal computer
- RF:
-
Random forest
- ROC:
-
Receiver operating characteristic
- STAS:
-
Spread through air space
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The scientific guarantor of this publication is Jingshan Gong.
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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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DOI: https://doi.org/10.1007/s00330-020-06694-z