Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study

Guangyao Wu, Henry C. Woodruff, Jing Shen, Turkey Refaee, Sebastian Sanduleanu, Abdalla Ibrahim, Ralph T. H. Leijenaar, Rui Wang, Jingtong Xiong, Jie Bian, Jianlin Wu*, Philippe Lambin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Background: Solid components of part-solid nodules (PSNs) at CT are reflective of invasive adenocarcinoma, but studies describing radiomic features of PSNs and the perinodular region are lacking.

Purpose: To develop and to validate radiomic signatures diagnosing invasive lung adenocarcinoma in PSNs compared with the Brock, clinical-semantic features, and volumetric models.

Materials and Methods: This retrospective multicenter study (https://ClinicalTrials.gov, NCT03872362) included 291 patients (medianage, 60 years; interquartile range, 55-65 years; 191 women) from January 2013 to October 2017 with 297 PSN lung adenocarcinomas split into training (n = 229) and test (n = 68) data sets. Radiomic features were extracted from the different regions (gross tumor volume [GTV], solid, ground-glass, and perinodular). Random-forest models were trained using clinical-semantic, volumetric,and radiomic features, and an online nodule calculator was used to compute the Brock model. Performances of models were evaluated using standard metrics such as area under the curve (AUC), accuracy, and calibration. The integrated discrimination improvement was applied to assess model performance changes after the addition of perinodular features.

Results: The radiomics model based on ground-glass and solid features yielded an AUC of 0.98 (95% confidence interval [CI]: 0.96, 1.00) on the test data set, which was significantly higher than the Brock (AUC, 0.83 [95% CI: 0.72, 0.94]; P =.007), clinical-semantic (AUC, 0.90 [95% CI: 0.83, 0.98]; P =.03), volumetric GTV (AUC, 0.87 [95% CI: 0.78, 0.96]; P =.008), and radiomics GTV (AUC, 0.88 [95% CI: 0.80, 0.96]; P =.01) models. It also achieved the best accuracy (93% [95% CI: 84%, 98%]). Both this model and the model with added perinodular features showed good calibration, whereas adding perinodular features did not improve the performance (integrated discrimination improvement, -0.02; P =.56).

Conclusion: Separating ground-glass and solid CT radiomic features of part-solid nodules was useful in diagnosing the invasiveness of lung adenocarcinoma, yielding a better predictive performance than the Brock, clinical-semantic, volumetric, and radiomics gross tumor volume models.

Original languageEnglish
Pages (from-to)451-458
Number of pages8
JournalRadiology
Volume297
Issue number2
DOIs
Publication statusPublished - Nov 2020

Keywords

  • CLASSIFICATION
  • COMPONENT
  • COMPUTED-TOMOGRAPHY
  • SECTION
  • SIZE
  • TUMOR
  • BIOMARKER
  • DISCRIMINATION

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