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

• Published in: European radiology 2020-07, Vol.30 (7), p.4050-4057
• Main Authors: Jiang, Changsi, Luo, Yan, Yuan, Jialin, You, Shuyuan, Chen, Zhiqiang, Wu, Mingxiang, Wang, Guangsuo, Gong, Jingshan
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Breast; Computed tomography; Correlation coefficient; Correlation coefficients; Diagnostic Radiology; Diagnostic systems; Feature extraction; Forest management; Imaging; Internal Medicine; Interventional Radiology; Invasiveness; Learning algorithms; Lung cancer; Lungs; Machine learning; Medicine; Medicine & Public Health; Model accuracy; Neuroradiology; Prediction models; Radiology; Radiomics; Regression analysis; Risk analysis; Risk factors; Sensitivity; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-06694-z

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  0.9) 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.