General Surgery Center, The General Hospital of Western Theater, Chengdu, 610083, Sichuan Province, China.
College of Medicine, Southwest Jiaotong University, Chengdu, China.
BMC Gastroenterol. 2022 Nov 2;22(1):444. doi: 10.1186/s12876-022-02544-y.
Gallbladder cancer (GBC) is a highly aggressive malignancy in elderly patients. Our goal is aimed to construct a novel nomogram to predict cancer-specific survival (CSS) in elderly GBC patients.
We extracted clinicopathological data of elderly GBC patients from the SEER database. We used univariate and multivariate Cox proportional hazard regression analysis to select the independent risk factors of elderly GBC patients. These risk factors were subsequently integrated to construct a predictive nomogram model. C-index, calibration curve, and area under the receiver operating curve (AUC) were used to validate the accuracy and discrimination of the predictive nomogram model. A decision analysis curve (DCA) was used to evaluate the clinical value of the nomogram.
A total of 4241 elderly GBC patients were enrolled. We randomly divided patients from 2004 to 2015 into training cohort (n = 2237) and validation cohort (n = 1000), and patients from 2016 to 2018 as external validation cohort (n = 1004). Univariate and multivariate Cox proportional hazard regression analysis found that age, tumor histological grade, TNM stage, surgical method, chemotherapy, and tumor size were independent risk factors for the prognosis of elderly GBC patients. All independent risk factors selected were integrated into the nomogram to predict cancer-specific survival at 1-, 3-, and 5- years. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the nomogram was 0.763, 0.756, and 0.786, respectively. The calibration curves suggested that the predicted value of the nomogram is highly consistent with the actual observed value. AUC also showed the high authenticity of the prediction model. DCA manifested that the nomogram model had better prediction ability than the conventional TNM staging system.
We constructed a predictive nomogram model to predict CSS in elderly GBC patients by integrating independent risk factors. With relatively high accuracy and reliability, the nomogram can help clinicians predict the prognosis of patients and make more rational clinical decisions.
胆囊癌（GBC）是老年患者中一种侵袭性很强的恶性肿瘤。我们的目标是构建一种新的列线图，以预测老年 GBC 患者的癌症特异性生存（CSS）。
我们从 SEER 数据库中提取了老年 GBC 患者的临床病理数据。我们使用单因素和多因素 Cox 比例风险回归分析来选择老年 GBC 患者的独立风险因素。这些风险因素随后被整合到预测列线图模型中。C 指数、校准曲线和接收者操作特征曲线下的面积（AUC）用于验证预测列线图模型的准确性和区分度。决策分析曲线（DCA）用于评估列线图的临床价值。
共纳入 4241 例老年 GBC 患者。我们将 2004 年至 2015 年的患者随机分为训练队列（n=2237）和验证队列（n=1000），将 2016 年至 2018 年的患者作为外部验证队列（n=1004）。单因素和多因素 Cox 比例风险回归分析发现，年龄、肿瘤组织学分级、TNM 分期、手术方式、化疗和肿瘤大小是老年 GBC 患者预后的独立危险因素。所有选定的独立危险因素都被整合到列线图中，以预测 1、3 和 5 年的癌症特异性生存。在训练队列、内部验证队列和外部验证队列中，列线图的 C 指数分别为 0.763、0.756 和 0.786。校准曲线表明，列线图的预测值与实际观察值高度一致。AUC 也显示了预测模型的高真实性。DCA 表明，列线图模型比传统的 TNM 分期系统具有更好的预测能力。
我们通过整合独立风险因素构建了一个预测老年 GBC 患者 CSS 的列线图模型。该列线图具有较高的准确性和可靠性，可帮助临床医生预测患者的预后，并做出更合理的临床决策。
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