Analysis of Risk Factors for Distant Metastasis of Pancreatic Ductal Adenocarcinoma without Regional Lymph Node Metastasis and a Nomogram Prediction Model for Survival
Abstract:Background. Negative regional lymph nodes do not indicate a lack of distant metastasis. A considerable number of patients with negative regional lymph node pancreatic cancer will skip the step of regional lymph node metastasis and directly develop distant metastasis. Methods. We retrospectively analyzed the clinicopathological characteristics of patients with negative regional lymph node pancreatic cancer and distant metastasis in the Surveillance, Epidemiology, and End Results database from 2010 to 2015. Mult… Show more
Background
Lymph node metastasis (LNM) is a critical prognostic factor in resectable pancreatic cancer (PC) patients, determining treatment strategies. This study aimed to develop a clinical model to adequately and accurately predict the risk of LNM in PC patients.
Methods
13,200 resectable PC patients were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database, and randomly divided into a training group and an internal validation group at a ratio of 7:3. An independent group (n = 62) obtained from The First Affiliated Hospital of Xinxiang Medical University was enrolled as the external validation group. The univariate and multivariate logistic regression analyses were used to screen independent risk factors for LNM. The minimum Akaike’s information criterion (AIC) was performed to select the optimal model parameters and construct a nomogram for assessing the risk of LNM. The performance of the nomogram was assessed by the receiver operating characteristics (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, an online web calculator was designed to assess the risk of LNM.
Result
A total of six risk predictors (including age at diagnosis, race, primary site, grade, histology, and T-stage) were identified and included in the nomogram. The areas under the curves (AUCs) [95% confidential interval (CI)] were 0.711 (95%CI: 0.700–0.722), 0.700 (95%CI: 0.683–0.717), and 0.845 (95%CI: 0.749–0.942) in the training, internal validation and external validation groups, respectively. The calibration curves showed satisfied consistency between nomogram-predicted LNM and actual observed LNM. The concordance indexes (C-indexes) in the training, internal, and external validation sets were 0.689, 0.686, and 0.752, respectively. The DCA curves of the nomogram demonstrated good clinical utility.
Conclusion
We constructed a nomogram model for predicting LNM in pancreatic cancer patients, which may help oncologists and surgeons to choose more individualized clinical treatment strategies and make better clinical decisions.
Background
Lymph node metastasis (LNM) is a critical prognostic factor in resectable pancreatic cancer (PC) patients, determining treatment strategies. This study aimed to develop a clinical model to adequately and accurately predict the risk of LNM in PC patients.
Methods
13,200 resectable PC patients were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database, and randomly divided into a training group and an internal validation group at a ratio of 7:3. An independent group (n = 62) obtained from The First Affiliated Hospital of Xinxiang Medical University was enrolled as the external validation group. The univariate and multivariate logistic regression analyses were used to screen independent risk factors for LNM. The minimum Akaike’s information criterion (AIC) was performed to select the optimal model parameters and construct a nomogram for assessing the risk of LNM. The performance of the nomogram was assessed by the receiver operating characteristics (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, an online web calculator was designed to assess the risk of LNM.
Result
A total of six risk predictors (including age at diagnosis, race, primary site, grade, histology, and T-stage) were identified and included in the nomogram. The areas under the curves (AUCs) [95% confidential interval (CI)] were 0.711 (95%CI: 0.700–0.722), 0.700 (95%CI: 0.683–0.717), and 0.845 (95%CI: 0.749–0.942) in the training, internal validation and external validation groups, respectively. The calibration curves showed satisfied consistency between nomogram-predicted LNM and actual observed LNM. The concordance indexes (C-indexes) in the training, internal, and external validation sets were 0.689, 0.686, and 0.752, respectively. The DCA curves of the nomogram demonstrated good clinical utility.
Conclusion
We constructed a nomogram model for predicting LNM in pancreatic cancer patients, which may help oncologists and surgeons to choose more individualized clinical treatment strategies and make better clinical decisions.
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