Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study
Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study
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基于计算机断层扫描图像的放射组学特征用于术前预测胃癌各个站点的淋巴结转移：一项多中心研究
DOI：
10.1016/j.radonc.2021.11.003
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发表时间：
2021-12-01
影响因子：
5.7
通讯作者：
Li, Guoxin
中科院分区：
文献类型：
--
作者：
Sun, Zepang;Jiang, Yuming;Li, Guoxin
背景：胃癌（GC）的特异性诊断和治疗需要在术前准确预测各个部位的淋巴结转移（LNM），如估计淋巴结清扫的程度。本研究旨在开发基于术前计算机断层扫描（CT）图像的放射组学特征，用于预测每个站点的LNM状态。方法：我们回顾性地从两个中心招募了1506例GC患者作为训练（531例）和外部（975例）验证队列，并从单个中心招募了112例患者作为前瞻性验证队列。从术前CT图像中提取放射组学特征，并结合临床特征构建用于预测单个淋巴结站LNM的形态图。通过校准、鉴别和临床有用性来评估图的性能。结果：在培训、外部和前瞻性验证队列中，放射组学特征与LNM状态显著相关。此外，在多变量logistic回归分析中，放射组学特征是LNM状态的独立预测因子。放射组学模式图显示出良好的预测性能，12个节点站的训练队列auc为0.716-0.871，外部验证队列auc为0.678-0.768，前瞻性验证队列auc为0.700-0.841。模态图显示校准曲线的实际概率和预测概率之间有显著的一致性。决策曲线分析显示，形态图比临床病理特征具有更好的净效益。结论：单个淋巴结站放射组学形态图具有较好的预测准确性，可为胃癌的个体化诊断和治疗提供重要信息。(C) 2021 Elsevier B.V.版权所有
Background: Specific diagnosis and treatment of gastric cancer (GC) require accurate preoperative predictions of lymph node metastasis (LNM) at individual stations, such as estimating the extent of lymph node dissection. This study aimed to develop a radiomics signature based on preoperative computed tomography (CT) images, for predicting the LNM status at each individual station.Methods: We enrolled 1506 GC patients retrospectively from two centers as training (531) and external (975) validation cohorts, and recruited 112 patients prospectively from a single center as prospective validation cohort. Radiomics features were extracted from preoperative CT images and integrated with clinical characteristics to construct nomograms for LNM prediction at individual lymph node stations. Performance of the nomograms was assessed through calibration, discrimination and clinical usefulness.Results: In training, external and prospective validation cohorts, radiomics signature was significantly associated with LNM status. Moreover, radiomics signature was an independent predictor of LNM status in the multivariable logistic regression analysis. The radiomics nomograms revealed good prediction performances, with AUCs of 0.716-0.871 in the training cohort, 0.678-0.768 in the external validation cohort and 0.700-0.841 in the prospective validation cohort for 12 nodal stations. The nomograms demonstrated a significant agreement between the actual probability and predictive probability in calibration curves. Decision curve analysis showed that nomograms had better net benefit than clinicopathologic characteristics.Conclusion: Radiomics nomograms for individual lymph node stations presented good prediction accuracy, which could provide important information for individual diagnosis and treatment of gastric cancer. (C) 2021 Elsevier B.V. All rights reserved.