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

Jiang, Changsi1; Luo, Yan1; Yuan, Jialin1; You, Shuyuan2; Chen, Zhiqiang2; Wu, Mingxiang1; Wang, Guangsuo3; Gong, Jingshan1,4

2020-02-28

10.1007/s00330-020-06694-z

EUROPEAN RADIOLOGY   影响因子和分区

0938-7994

1432-1084

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.

Lung Adenocarcinoma Metastasis Radiomics Machine learning

SCI

英语

通讯

Radiology, Nuclear Medicine & Medical Imaging

Radiology, Nuclear Medicine & Medical Imaging

WOS:000517016300002

SPRINGER

CLINICAL MEDICINE

Web of Science

1.Jinan Univ, Dept Radiol, Shenzhen Peoples Hosp, Clin Med Coll 2, Shenzhen 518020, Peoples R China
2.Jinan Univ, Dept Pathol, Shenzhen Peoples Hosp, Clin Med Coll 2, Shenzhen 518020, Peoples R China
3.Jinan Univ, Dept Thorac Surg, Shenzhen Peoples Hosp, Clin Med Coll 2, Shenzhen 518020, Peoples R China
4.Southern Univ Sci & Technol, Affiliated Hosp 1, Dept Radiol, Shenzhen 518020, Peoples R China

Jiang, Changsi,Luo, Yan,Yuan, Jialin,et al. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma[J]. EUROPEAN RADIOLOGY,2020,30(7):4050-4057.

Jiang, Changsi.,Luo, Yan.,Yuan, Jialin.,You, Shuyuan.,Chen, Zhiqiang.,...&Gong, Jingshan.(2020).CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.EUROPEAN RADIOLOGY,30(7),4050-4057.

Jiang, Changsi,et al."CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma".EUROPEAN RADIOLOGY 30.7(2020):4050-4057.