Department of General Surgery, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, China.
Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue North, 510515 Guangzhou, China.
EBioMedicine. 2018 Oct;36:171-182. doi: 10.1016/j.ebiom.2018.09.007. Epub 2018 Sep 14.
To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in 1591 consecutive patients. A radiomics signature was generated by using the Lasso-Cox regression model in 228 patients and validated in internal and external validation cohorts. Radiomics nomograms integrating the radiomics signature were constructed, demonstrating the incremental value of the radiomics signature to the traditional staging system for individualized survival estimation. The performance of the nomograms was assessed with respect to calibration, discrimination, and clinical usefulness. The radiomics signature consisted of 19 selected features and was significantly associated with DFS (disease-free survival) and OS (overall survival). Multivariate analysis demonstrated that the radiomics signature was an independent prognostic factor. Incorporating the radiomics signature into the radiomics-based nomograms resulted in better performance for the estimation of DFS and OS than the clinicopathological nomograms and TNM staging system, with improved accuracy of the classification of survival outcomes. Further analysis showed that stage II and III patients with higher radiomics scores exhibited a favorable response to chemotherapy. In conclusion, the newly developed radiomics signature is a powerful predictor of DFS and OS, and it may predict which patients with stage II and III GC benefit from chemotherapy.
为了开发和验证一种用于预测胃癌（GC）生存和化疗获益的放射组学特征。在这项多中心回顾性分析中，我们分析了 1591 例连续患者门静脉期 CT 的放射组学特征。在 228 例患者中，使用 Lasso-Cox 回归模型生成放射组学特征，在内部和外部验证队列中进行验证。构建了整合放射组学特征的放射组学列线图，证明了放射组学特征对传统分期系统进行个体化生存评估的附加价值。利用校准、判别和临床实用性评估了列线图的性能。放射组学特征由 19 个选定的特征组成，与DFS（无病生存）和 OS（总生存）显著相关。多变量分析表明，放射组学特征是独立的预后因素。将放射组学特征纳入放射组学列线图中，用于估计 DFS 和 OS 的性能优于临床病理列线图和 TNM 分期系统，提高了生存结局分类的准确性。进一步分析表明，放射组学评分较高的 II 期和 III 期患者对化疗有较好的反应。总之，新开发的放射组学特征是 DFS 和 OS 的有力预测指标，它可能预测哪些 II 期和 III 期 GC 患者受益于化疗。
