Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi District, Tianjin 300060, China; National Clinical Research Center for Cancer, Tianjin, China; Tianjin's Clinical Research Center for Cancer, Tianjin, China; The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Department of General Surgery, Weifang People's Hospital, Weifang City, Shandong Province, China.
Acad Radiol. 2021 Jun;28(6):e155-e164. doi: 10.1016/j.acra.2020.03.045. Epub 2020 Jun 2.
To develop and validate a CT-based radiomics model for preoperative prediction of lymph node metastasis (LNM) in early stage gastric cancer (EGC).
Four hundred and sixty-three consecutive EGC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. The predictive performance of radiomics signature was tested in the training and testing cohorts. Multivariate logistic regression analysis was conducted to build a radiomics-based model combined radiomics signature and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness.
The radiomics signature comprised six robust features showed significant association with LNM in both cohorts. A radiomics model that incorporated radiomics signature and CT-reported lymph node status showed good calibration and discrimination in the training cohort (AUC = 0.91) and testing cohort (AUC = 0.89). Decision curve analysis confirmed the clinical utility of this model.
The CT-based radiomics model showed favorable accuracy for prediction of LNM in EGC and may help to improve clinical decision-making.
开发并验证基于 CT 的放射组学模型，以预测早期胃癌（EGC）患者的淋巴结转移（LNM）。
本回顾性研究纳入了 463 例连续 EGC 患者。从门静脉期 CT 扫描中提取放射组学特征。使用最小绝对值收缩和选择算子（LASSO）方法，基于高度可重复的特征构建放射组学特征。在训练和测试队列中测试放射组学特征的预测性能。进行多变量逻辑回归分析，根据 CT 构建结合放射组学特征和淋巴结状态的放射组学模型。通过判别、校准和临床实用性来确定模型的性能。
放射组学特征由 6 个稳健的特征组成，在两个队列中均与 LNM 显著相关。纳入放射组学特征和 CT 报告的淋巴结状态的放射组学模型在训练队列（AUC=0.91）和测试队列（AUC=0.89）中均显示出良好的校准和判别能力。决策曲线分析证实了该模型的临床实用性。
基于 CT 的放射组学模型对预测 EGC 患者的 LNM 具有较好的准确性，可能有助于改善临床决策。
