4.7 Article

Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules

Journal

SCIENTIFIC REPORTS
Volume 11, Issue 1, Pages -

Publisher

NATURE RESEARCH
DOI: 10.1038/s41598-021-83167-3

Keywords

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Funding

  1. National Natural Science Foundation of China [61976238]
  2. Future Star of famous doctors' training plan of Fudan University
  3. National Key Research and Development Program of China [2017YFC0112800, 2017YFC0112905]
  4. Medical Imaging Key Program of Wise Information Technology of 120, Health Commission of Shanghai [2018ZHYL0103]

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This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma presented as subcentimeter ground glass nodules. The radiomics signature built on contrast-enhanced CT data showed better predictive performance, with a radiographic-radiomics nomogram demonstrating good clinical utility. Overall, the radiomics signature on CECT could aid in preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.
Controversy and challenges remain regarding the cognition of lung adenocarcinomas presented as subcentimeter ground glass nodules (GGNs). Postoperative lymphatic involvement or intrapulmonary metastasis is found in approximately 15% to 20% of these cases. This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma appearing as subcentimeter ground glass nodules. We retrospectively enrolled 318 subcentimeter GGNs with histopathology-confirmed adenocarcinomas in situ (AIS), minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC). The radiomics features were extracted from manual segmentation based on contrast-enhanced CT (CECT) and non-contrast enhanced CT (NCECT) images after imaging preprocessing. The Lasso algorithm was applied to construct radiomics signatures. The predictive performance of radiomics models was evaluated by receiver operating characteristic (ROC) analysis. A radiographic-radiomics combined nomogram was developed to evaluate its clinical utility. The radiomics signature on CECT (AUC: 0.896 [95% CI 0.815-0.977]) performed better than the radiomics signature on NCECT data (AUC: 0.851[95% CI 0.712-0.989]) in the validation set. An individualized prediction nomogram was developed using radiomics model on CECT and radiographic model including type, shape and vascular change. The C index of the nomogram was 0.915 in the training set and 0.881 in the validation set, demonstrating good discrimination. Decision curve analysis (DCA) revealed that the proposed model was clinically useful. The radiomics signature built on CECT could provide additional benefit to promote the preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.

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