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Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction

Sun, Yingli, Li, Cheng, Jin, Liang, Gao, Pan, Zhao, Wei, Ma, Weiling, Tan, Mingyu, Wu, Weilan +3 more·2020·European Radiology
DOI 10.1007/s00330-020-06776-yPMC PMC7305264
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Abstract

Objectives: To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic–radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed. Results: Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic–radiomics model (AUC 0.77; 95% CI, 0.69–0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62–0.81) and Rad-score (AUC 0.72; 95% CI, 0.63–0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model. Conclusions: The proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs. Key Points: • CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic–radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.

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  • In a study by Sun et al., a combined radiographic-radiomics model with AUC 0.77 (95% CI 0.69-0.86) outperformed a radiographic-only model with AUC 0.71 (95% CI 0.62-0.81) in predicting invasiveness in the validation set of pure ground glass nodules.

    “The combined radiographic–radiomics model (AUC 0.77; 95% CI, 0.69–0.86) outperformed the radiographic-only model (AUC 0.71; 95% CI, 0.62–0.81) in predicting invasiveness in the validation set [28,29].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [28]

  • Machine learning applied to FDG-PET imaging for lung cancer detection achieved sensitivities of 95.9% and 91.5% and specificities of 98.1% and 94.2% for standard and ultralow-dose scans, respectively, suggesting that ML approaches can detect lung cancer even at minimal radiation exposure of 0.11 mSv.

    “Additionally, machine learning (ML) has been applied to FDG-PET imaging for lung cancer detection, achieving sensitivities of 95.9% and 91.5% and specificities of 98.1% and 94.2% for standard and ultralow-dose scans, respectively, suggesting that ML approaches can detect lung cancer even at minimal radiation exposure of 0.11 mSv [28].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [28]

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Research works printed but not linked: 28

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Cited by

Sorted by citations

Authors
Sun, Y., Li, C., Jin, L., Gao, P., Zhao, W., Ma, W., Tan, M., Wu, W., Duan, S., Shan, Y., & Li, M.
Venue
European Radiology
Publisher
Springer Science and Business Media LLC
Published
2020-03-11
Volume
30
Issue
7
Pages
3650-3659
ISSN
  • 0938-7994
  • 1432-1084
DOI
10.1007/s00330-020-06776-y
PMC
PMC7305264
Cited by
111
References
0
Language
en
Licence
Funders
  • Science and Technology Commission of Shanghai Municipality (2018ZHYL0103)
  • National Natural Science Foundation of China (61976238)

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