Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi District, Tianjin 300060, China; National Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China; The Key Laboratory of Cancer Prevention and Therapy, Huanhuxi Road, Hexi District, Tianjin 300060, China.
Department of general surgery, Weifang People's Hospital, Guangwen Street, Kuiwen District, Weifang City, 261000, Shandong Province, China.
Eur J Radiol. 2020 Aug;129:109069. doi: 10.1016/j.ejrad.2020.109069. Epub 2020 May 18.
To develop and validate a radiomics-based model for preoperative prediction of lymph node metastasis (LNM) in gastric cancer (GC).
A total of 768 GC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography (CT) scans. A radiomics signature was built with highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method in the training cohort (n = 486). The signature was further validated in internal validation (n = 240) and external testing cohorts (n = 42). Multivariate logistic regression analysis was conducted to build a model that combined radiomics signature, serum biomarkers, and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness. The predictive value of the model was also evaluated in early stage GC (EGC) subgroup.
The radiomics signature comprised 7 robust features showed favorable prediction efficacy in all cohorts. A radiomics-based model that incorporated radiomics signature, serum CA72-4, and CT-reported lymph node status had good calibration and discrimination in training cohort [AUC, 0.92; 95% confidence interval (CI), 0.89-0.95] and validation cohort (AUC 0.86; 95% CI, 0.81-0.91). The model also showed a favorable predictive performance for EGC patients with an AUC of 0.85 (95% CI, 0.76-0.94). Decision curve analysis confirmed the clinical utility of this model.
The radiomics-based model showed favorable accuracy for prediction of LNM in GC. The model may also serve as a noninvasive tool for preoperative evaluation of LNM in EGC.
开发并验证一种基于放射组学的模型，用于术前预测胃癌（GC）的淋巴结转移（LNM）。
本回顾性研究共纳入 768 例 GC 患者。从门静脉期 CT 扫描中提取放射组学特征。在训练队列（n=486）中，使用最小绝对收缩和选择算子（LASSO）方法，从高度可重复的特征中构建放射组学特征。在内部验证队列（n=240）和外部测试队列（n=42）中进一步验证该特征。根据 CT 进行的多变量逻辑回归分析用于构建一种结合放射组学特征、血清标志物和淋巴结状态的模型。通过其判别能力、校准能力和临床实用性来确定模型的性能。还在早期胃癌（EGC）亚组中评估了模型的预测价值。
放射组学特征由 7 个稳健特征组成，在所有队列中均显示出良好的预测效果。纳入放射组学特征、血清 CA72-4 和 CT 报告的淋巴结状态的放射组学模型在训练队列（AUC：0.92；95%置信区间（CI）：0.89-0.95）和验证队列（AUC：0.86；95%CI：0.81-0.91）中具有良好的校准和判别能力。该模型对 EGC 患者也具有良好的预测性能，AUC 为 0.85（95%CI：0.76-0.94）。决策曲线分析证实了该模型的临床实用性。
基于放射组学的模型对 GC 的 LNM 预测具有良好的准确性。该模型也可能成为 EGC 患者术前评估 LNM 的非侵入性工具。
