AUTHOR=Zeng Chao , Zhang Wei , Liu Meiyue , Liu Jianping , Zheng Qiangxin , Li Jianing , Wang Zhiwu , Sun Guogui 

TITLE=Efficacy of radiomics model based on the concept of gross tumor volume and clinical target volume in predicting occult lymph node metastasis in non-small cell lung cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1096364

DOI=10.3389/fonc.2023.1096364

ISSN=2234-943X

ABSTRACT=Objective: To extract the gross tumor volume (GTV) and clinical target volume (CTV) of lung cancer from computed tomography (CT) images by radiomics method. To establish a predictive model for occult Lymph node metastasis (LNM) in patients with stage Ⅰ-Ⅱ A non-small cell lung cancer (NSCLC) based on contrast-enhanced CT.

Results: Eight optimal radiomics features were finally locked and significantly correlated with occult LNM (all P<0.05). The ROC curves of the three models showed good predictive effects. The AUC values of the GTV and CTV models in the validation group were 0.821 and 0.812. The AUC of the GTV+CTV hybrid model was significantly better than that of the CTV model (the AUC of the training group and the validation group in the GTV+CTV hybrid model was 0.869 and 0.906, respectively). Moreover, the GTV model (the AUC of the training group and the validation group in the CTV model were 0.834 and 0.812, and the AUC of the training group and the validation group in the GTV model were 0. 845 and 0.821), and statistically significant by Delong test. ROC curve and decision curve showed that the radiomics model had clinical application value.
Conclusions: The radiomics prediction model based on GTV and CTV was developed and verified in this study to be able to predict occult LNM in patients with preoperative clinical stage I-IIA NSCLC, and it was found that the combined model is significantly more accurate than either model operating independently.