CT-based radiomics nomogram for preoperative prediction of No.10 lymph nodes metastasis in advanced proximal gastric cancer.

Preoperative diagnosis of No.10 lymph nodes (LNs) metastases in advanced proximal gastric cancer (APGC) patients remains a challenge. The aim of this study was to develop a CT-based radiomics nomogram for identification of No.10 LNs status in APGCs. A total of 515 patients with primary APGCs were retrospectively selected and divided into a training cohort (n = 340) and a validation cohort (n = 175). Total incidence of No.10 LNM was 12.4% (64/515). CT based radiomics nomogram combining with radiomic signature calculated from venous CT imaging features and CT-defined No.10 LNs status evaluated by radiologists was built and tested to predict the No.10 LNs status in APGCs. CT based radiomics nomogram yielded classification accuracy with areas under ROC curves, AUC = 0.896 and 0.814 in training and validation cohort, respectively, while radiomic signature and radiologist' diagnosis based on contrast-enhanced CT images yielded lower AUCs ranging in 0.742-0.866 and 0.619-0.685, respectively. In the specificity higher than 80%, the sensitivity of using radiomics nomogram, radiomic signature and radiologists' evaluation to detect No.10 LNs positive cases was 82.8% (53/64), 67.2% (43/64) and 39.1% (25/64), respectively. The CT-based radiomics nomogram provides a promising and more effective method to yield high accuracy in identification of No.10 LNs metastases in APGC patients.

Wang L ，Gong J ，Huang X ，Lin G ，Zheng B ，Chen J ，Xie J ，Lin R ，Duan Q ，Lin W ... -  《-》

Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer.

Wang X ，Li C ，Fang M ，Zhang L ，Zhong L ，Dong D ，Tian J ，Shan X ... -  《BMC MEDICAL IMAGING》

CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer.

Wang Y ，Liu W ，Yu Y ，Liu JJ ，Xue HD ，Qi YF ，Lei J ，Yu JC ，Jin ZY ... -  《-》

Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.

Li J ，Dong D ，Fang M ，Wang R ，Tian J ，Li H ，Gao J ... -  《-》

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.

Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery. Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n = 128) and a validation cohort (n = 64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared. Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates. The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

Zhang W ，Wang S ，Dong Q ，Chen W ，Wang P ，Zhu G ，Chen X ，Cai Y ... -  《PeerJ》