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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
- Source :
- European Radiology; Jul2020, Vol. 30 Issue 7, p4050-4057, 8p, 2 Color Photographs, 2 Diagrams, 3 Charts, 1 Graph
- Publication Year :
- 2020
-
Abstract
- <bold>Purpose: </bold>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.<bold>Methods and Materials: </bold>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).<bold>Results: </bold>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.<bold>Conclusion: </bold>CT-based radiomics model can preoperatively predict STAS in lung adenocarcinoma with good diagnosis performance.<bold>Key Points: </bold>• 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09387994
- Volume :
- 30
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- European Radiology
- Publication Type :
- Academic Journal
- Accession number :
- 143855618
- Full Text :
- https://doi.org/10.1007/s00330-020-06694-z