Abstract：
UNASSIGNED: There is a controversy about whether surgery should proceed among metastatic pancreatic cancer (mPC) patients. A survival benefit was observed in mPC patients who underwent primary tumor resection; however, determining which patients would benefit from surgery is complex. For this purpose, we created a model to identify mPC patients who may benefit from primary tumor excision.
UNASSIGNED: Patients with mPC were extracted from the Surveillance, Epidemiology, and End Results database, and separated into surgery and nonsurgery groups based on whether the primary tumor was resected. Propensity score matching (PSM) was applied to balance confounding factors between the two groups. A nomogram was developed using multivariable logistic regression to estimate surgical benefit. Our model is evaluated using multiple methods.
UNASSIGNED: About 662 of 14,183 mPC patients had primary tumor surgery. Kaplan-Meier analyses showed that the surgery group had a better prognosis. After PSM, a survival benefit was still observed in the surgery group. Among the surgery cohort, 202 patients survived longer than 4 months (surgery-beneficial group). The nomogram discriminated better in training and validation sets under the receiver operating characteristic (ROC) curve (AUC), and calibration curves were consistent. Decision curve analysis (DCA) revealed that it was clinically valuable. This model is better at identifying candidates for primary tumor excision.
UNASSIGNED: A helpful prediction model was developed and validated to identify ideal candidates who may benefit from primary tumor resection in mPC.
UNASSIGNED: Patients with mPC were extracted from the Surveillance, Epidemiology, and End Results database, and separated into surgery and nonsurgery groups based on whether the primary tumor was resected. Propensity score matching (PSM) was applied to balance confounding factors between the two groups. A nomogram was developed using multivariable logistic regression to estimate surgical benefit. Our model is evaluated using multiple methods.
UNASSIGNED: About 662 of 14,183 mPC patients had primary tumor surgery. Kaplan-Meier analyses showed that the surgery group had a better prognosis. After PSM, a survival benefit was still observed in the surgery group. Among the surgery cohort, 202 patients survived longer than 4 months (surgery-beneficial group). The nomogram discriminated better in training and validation sets under the receiver operating characteristic (ROC) curve (AUC), and calibration curves were consistent. Decision curve analysis (DCA) revealed that it was clinically valuable. This model is better at identifying candidates for primary tumor excision.
UNASSIGNED: A helpful prediction model was developed and validated to identify ideal candidates who may benefit from primary tumor resection in mPC.
摘要：
■关于是否应在转移性胰腺癌（mPC）患者中进行手术存在争议。在接受原发性肿瘤切除术的mPC患者中观察到生存获益；然而，确定哪些患者将从手术中受益是复杂的。为此,我们创建了一个模型来识别可能受益于原发肿瘤切除的mPC患者.
■从监测中提取mPC患者，流行病学,和最终结果数据库，并根据是否切除原发肿瘤分为手术组和非手术组。采用倾向评分匹配(PSM)来平衡两组间的混杂因素。使用多变量逻辑回归估计手术获益。我们的模型使用多种方法进行评估。
■14,183例mPC患者中约有662例进行了原发肿瘤手术。Kaplan-Meier分析显示手术组预后较好。PSM之后,手术组仍有生存获益.在手术队列中，202例患者存活超过4个月（手术受益组）。在受试者工作特性(ROC)曲线(AUC)下，列线图在训练集和验证集上有较好的区分，和校准曲线是一致的。决策曲线分析（DCA）表明它具有临床价值。该模型在识别原发性肿瘤切除的候选者方面更好。
■开发并验证了一种有用的预测模型，以确定可能从mPC中的原发性肿瘤切除中受益的理想候选人。
■从监测中提取mPC患者，流行病学,和最终结果数据库，并根据是否切除原发肿瘤分为手术组和非手术组。采用倾向评分匹配(PSM)来平衡两组间的混杂因素。使用多变量逻辑回归估计手术获益。我们的模型使用多种方法进行评估。
■14,183例mPC患者中约有662例进行了原发肿瘤手术。Kaplan-Meier分析显示手术组预后较好。PSM之后,手术组仍有生存获益.在手术队列中，202例患者存活超过4个月（手术受益组）。在受试者工作特性(ROC)曲线(AUC)下，列线图在训练集和验证集上有较好的区分，和校准曲线是一致的。决策曲线分析（DCA）表明它具有临床价值。该模型在识别原发性肿瘤切除的候选者方面更好。
■开发并验证了一种有用的预测模型，以确定可能从mPC中的原发性肿瘤切除中受益的理想候选人。
