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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
Jiang, Changsi; Luo, Yan; Yuan, Jialin; You, Shuyuan; Chen, Zhiqiang; Wu, Mingxiang; Wang, Guangsuo; Gong, Jingshan.
Afiliação
  • Jiang C; Department of Radiology, Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
  • Luo Y; Department of Radiology, Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
  • Yuan J; Department of Radiology, Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
  • You S; Department of Pathology, Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
  • Chen Z; Department of Pathology, Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
  • Wu M; Department of Radiology, Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
  • Wang G; Department of Thoracic Surgery, Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
  • Gong J; Department of Radiology, Shenzhen People's Hospital, the Second Clinical Medical College of Jinan University, Shenzhen, 518020, China. jshgong@sina.com.
Eur Radiol ; 30(7): 4050-4057, 2020 Jul.
Article em En | MEDLINE | ID: mdl-32112116
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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Tomografia Computadorizada por Raios X / Aprendizado de Máquina / Adenocarcinoma de Pulmão / Neoplasias Pulmonares Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Tomografia Computadorizada por Raios X / Aprendizado de Máquina / Adenocarcinoma de Pulmão / Neoplasias Pulmonares Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article