Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Radiother Oncol. 2021 Dec;165:179-190. doi: 10.1016/j.radonc.2021.11.003. Epub 2021 Nov 11.
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.
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.
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.
Radiomics nomograms for individual lymph node stations presented good prediction accuracy, which could provide important information for individual diagnosis and treatment of gastric cancer.
胃癌（GC）的特定诊断和治疗需要准确预测各个淋巴结转移（LNM）部位，如估计淋巴结清扫范围。本研究旨在基于术前计算机断层扫描（CT）图像开发一种放射组学特征，用于预测每个单独淋巴结站的 LNM 状态。
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，并从单个中心前瞻性招募了 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，并将其与临床特征相结合，构建用于预测各个淋巴结站 LNM 状态的列线图。通过校准、鉴别和临床实用性评估列线图的性能。
在训练、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，在多变量逻辑回归分析中，放射组学特征是 LNM 状态的独立预测因子。放射组学列线图显示出良好的预测性能，在训练队列中的 AUC 为 0.716-0.871，在外部验证队列中的 AUC 为 0.678-0.768，在前瞻性验证队列中的 AUC 为 0.700-0.841，用于预测 12 个淋巴结站。校准曲线显示，列线图在实际概率和预测概率之间具有显著的一致性。决策曲线分析表明，列线图比临床病理特征具有更好的净获益。
用于各个淋巴结站的放射组学列线图具有良好的预测准确性，可为胃癌的个体化诊断和治疗提供重要信息。
