Radiomics Signature on Computed Tomography Imaging: Association With Lymph Node Metastasis in Patients With Gastric Cancer - Jiang, Yuming; Wang, Wei; Chen, Chuanli; Zhang, Xiaodong; Zha, Xuefan; Lv, Wenbing; Xie, Jingjing; Huang, Weicai; Sun, Zepang; Hu, Yanfeng; Yu, Jiang; Li, Tuanjie; et al. (Frontiers in Oncology, 2019)
Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status. Methods: We retrospectively analyzed radiomics features of CT images in 1,689 consecutive patients from three cancer centers. The prediction model was developed in the training cohort and validated in internal and external validation cohorts. Lasso regression model was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model. We incorporated the radiomics signature, clinical T and N stage, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness. Results: The radiomics signature was significantly associated with pathological LN stage in training and validation cohorts. Multivariable logistic analysis found the radiomics signature was an independent predictor for LN metastasis. The nomogram showed good discrimination and calibration. Conclusions: The newly developed radiomic signature was a powerful predictor of LN metastasis and the radiomics nomogram could facilitate the preoperative individualized prediction of LN status.