A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis.
A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis.
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淋巴结转移阴性的浅表性胃癌患者远处转移预测的临床模型及转移患者的生存分析。
DOI：
10.1002/cam4.3680
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发表时间：
2021-03
期刊：
影响因子：
4
通讯作者：
Xue M
中科院分区：
文献类型：
--
作者：
Chen J;Wu L;Zhang Z;Zheng S;Lin Y;Ding N;Sun J;Shi L;Xue M
远端转移在浅表胃癌中相对少见，尤其是在无淋巴结转移的患者中。本研究旨在探讨浅表性胃癌-无淋巴结转移（SGC-NLNM）患者发生DM的主要临床危险因素以及DM患者的预后因素。从公共监测、流行病学和最终结果（SEER）数据库中收集了2004年至2015年期间SGC-NLNM患者的记录。单因素和多因素Logistic回归分析糖尿病的临床危险因素。采用Kaplan-Meier法和考克斯回归模型分析影响糖尿病患者预后的因素。基于多变量逻辑回归构建列线图，并通过C指数、校准和受试者工作特征曲线下面积（AUC）进行评价。我们开发并验证了一个诺模图来预测SGC-NLNM患者的DM，显示种族、年龄、原发部位、深度、大小和分级是独立的风险因素。构建的列线图具有良好的区分性能，C指数为0.836（95%置信区间[CI]：0.813-0.859）。校准图显示，预测的DM概率与训练集和验证集中的实际观察结果相同。训练集和验证集的AUC分别为0.846（95% CI：0.820-0.871）和0.801（95% CI：0.751-0.850）。生存分析的结果显示，手术（风险比[HR] = 0.249; 95% CI，0.125-0.495），化疗（HR = 0.473; 95% CI，0.353-0.633）和分级（HR = 1.374; 95% CI，1.018-1.854）是与癌症特异性生存期（CSS）相关的独立预后因素，但放射治疗不是（对数秩检验，p = 0.676）。我们构建了一个敏感的和有区别的列线图，以识别在初步诊断时可能存在播散的SGC-NLNM高危患者。肿瘤大小和原发部位是糖尿病预测的最大贡献者。与放疗相比，积极的手术和化疗可能是糖尿病患者更好的选择。我们确定了浅表胃癌-无淋巴结转移患者远处转移（DM）的主要临床风险因素以及转移患者的预后因素。此外，还建立了一个糖尿病预测的临床工具。
Distant metastasis (DM) is relatively rare in superficial gastric cancer (SGC), especially in patients without lymph node metastasis. This study aimed to explore the main clinical risk factors for DM in patients with superficial gastric cancer‐no lymph node metastasis (SGC‐NLNM) and the prognostic factors for patients with DM. Records of patients with SGC‐NLNM between 2004 and 2015 were collected from the public Surveillance, Epidemiology, and End Results (SEER) database. Both univariate and multivariate logistic regressions were performed to analyze the clinical risk factors for DM. The Kaplan–Meier method and Cox regression model were used to identify prognostic factors for patients with DM. A nomogram was built based on multivariate logistic regression and evaluated by the C‐index, the calibration, and the area under the receiver operating characteristic curve (AUC). We developed and validated a nomogram to predict DM in patients with SGC‐NLNM, showing that race, age, primary site, depth, size, and grade were independent risk factors. The built nomogram had a good discriminatory performance, with a C‐index of 0.836 (95% confidence interval [CI]: 0.813–0.859). Calibration plots showed that the predicted DM probability was identical to the actual observations in both the training and validation sets. AUC was 0.846 (95% CI: 0.820–0.871) and 0.801 (95% CI: 0.751–0.850) in the training and validation sets, respectively. The results of the survival analysis revealed that surgery (hazard ratio [HR] = 0.249; 95% CI, 0.125–0.495), chemotherapy (HR = 0.473; 95% CI, 0.353–0.633), and grade (HR = 1.374; 95% CI, 1.018–1.854) were independent prognostic factors associated with cancer‐specific survival (CSS), but radiotherapy was not (log‐rank test, p = 0.676). We constructed a sensitive and discriminative nomogram to identify high‐risk patients with SGC‐NLNM who may harbor dissemination at initial diagnosis. The tumor size and primary site were the largest contributors to DM prediction. Compared with radiotherapy, aggressive surgery, and chemotherapy may be better options for patients with DM. We identified the main clinical risk factors for distant metastasis (DM) in superficial gastric cancer‐no lymph node metastasis patients and the prognostic factors for the metastatic patients. Besides, a clinical tool was built for the DM prediction.
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影响因子：
2.8
作者：
Fang, Wen-Liang;Huang, Kuo-Hung;Li, Anna Fen-Yau
通讯作者：
Li, Anna Fen-Yau
影响因子：
3.7
作者：
Pyo, Jeung Hui;Ahn, Soomin;Kim, Sung
通讯作者：
Kim, Sung
DOI：
10.1007/s00464-014-3780-7
发表时间：
2015-05-01
影响因子：
3.1
作者：
Yang, Hyo-Joon;Kim, Sang Gyun;Jung, Hyun Chae
通讯作者：
Jung, Hyun Chae
影响因子：
3.2
作者：
Yang ZeLong;Chang ZhenYu;Wang ChunXi
通讯作者：
Wang ChunXi
影响因子：
5.1
作者：
Chen, Xijie;Chen, Shi;Peng, Junsheng
通讯作者：
Peng, Junsheng