Jin Yin, Xu Yilun, Li Yanyan, Chen Renpin, Cai Weiyang
Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Urology, Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
Front Oncol. 2021 Nov 5;11:755271. doi: 10.3389/fonc.2021.755271. eCollection 2021.
Gastric cancer (GC) is a typical heterogeneous malignant tumor, whose insensitivity to chemotherapy is a common cause of tumor recurrence and metastasis. There is no doubt regarding the effectiveness of adjuvant chemotherapy (ACT) for GC, but the population for whom it is indicated and the selection of specific options remain the focus of present research. The conventional pathological TNM prediction focuses on cancer cells to predict prognosis, while they do not provide sufficient prediction. Enhanced computed tomography (CT) scanning is a validated tool that assesses the involvement of careful identification of the tumor, lymph node involvement, and metastatic spread. Using the radiomics approach, we selected the least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for predicting the overall survival (OS) and disease-free survival (DFS) of patients with complete postoperative gastric cancer and further identifying candidate benefits from ACT. The radiomics trait-associated genes captured clinically relevant molecular pathways and potential chemotherapeutic drug metabolism mechanisms. Our results of precise surrogates using radiogenomics can lead to additional benefit from adjuvant chemotherapy and then survival prediction in postoperative GC patients.
胃癌(GC)是一种典型的异质性恶性肿瘤,其对化疗的不敏感性是肿瘤复发和转移的常见原因。辅助化疗(ACT)对GC的有效性毋庸置疑,但适用人群以及具体方案的选择仍是当前研究的重点。传统的病理TNM预测侧重于癌细胞来预测预后,但其预测并不充分。增强计算机断层扫描(CT)是一种经过验证的工具,可用于评估肿瘤的精确识别、淋巴结受累情况以及转移扩散情况。我们采用放射组学方法,选择最小绝对收缩和选择算子(LASSO)Cox回归模型构建放射组学特征,以预测完全切除术后胃癌患者的总生存期(OS)和无病生存期(DFS),并进一步确定ACT的潜在获益。放射组学特征相关基因捕捉到了临床相关的分子途径和潜在的化疗药物代谢机制。我们利用放射基因组学进行精确替代指标的研究结果,可为辅助化疗带来额外获益,进而实现对术后GC患者的生存预测。