To evaluate the efficiency of radiomics model on CT images of intratumoral and peritumoral lung parenchyma for preoperative prediction of lymph node (LN) metastasis in clinical stage T1 peripheral lung adenocarcinoma patients.
Three hundred sixty-six peripheral lung adenocarcinoma patients with clinical stage T1 were evaluated using five CT scanners. For each patient, two volumes of interest (VOIs) on CT were defined as the gross tumor volume (GTV) and the peritumoral volume (PTV, 1.5 cm around the tumor). One thousand nine hundred forty-six radiomic features were obtained from each VOI, and then refined for reproducibility and redundancy. The refined features were investigated for usefulness in building radiomic signatures by mRMR feature ranking method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic nomogram incorporating the radiomic signature and clinical parameters. The prediction performance was evaluated on the validation cohort.
The radiomic signatures using the features of GTV and PTV showed a good ability in predicting LN metastasis with an AUC of 0.829 (95% CI, 0.745–0.913) and 0.825 (95% CI, 0.733–0.918), respectively. By incorporating the features of GTV and PTV, the AUC of radiomic signature increased to 0.843 (95% CI, 0.770–0.916). The AUC of radiomic nomogram was 0.869 (95% CI, 0.800–0.938).
Radiomic signatures of GTV and PTV both had a good prediction ability in the prediction of LN metastasis, and there is no significant difference of AUC between the two groups. The proposed nomogram can be conveniently used to facilitate the preoperative prediction of LN metastasis in T1 peripheral lung adenocarcinomas.
• Radiomics from peritumoral lung parenchyma increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT.
• A radiomic nomogram was developed and validated to predict LN metastasis.
• Different scan parameters on CT showed that radiomics signature had good predictive performance.
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Gross and peritumoral volume
Gross tumor volume
Least absolute shrinkage and selection operator
Minimum redundancy maximum relevance
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The National Key Research and Development Program of China for Intergovernmental Cooperation (2016YFE0103000), Shanghai Municipal Commission of Health and Family Planning Program (grant numbers 20184Y0037 and 2018ZHYL0101).
The scientific guarantor of this publication is Prof. Xin Gao.
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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
One of the authors has significant statistical expertise.
Written informed consent was waived by the Institutional Review Board.
This retrospective analysis was approved by the ethical review board of our hospital (No. 2018SL049).
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Wang, X., Zhao, X., Li, Q. et al. Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?. Eur Radiol 29, 6049–6058 (2019). https://doi.org/10.1007/s00330-019-06084-0
- Lymph node