A nomogram based on preoperative inflammatory markers predicting the overall survival of pancreatic ductal adenocarcinoma

Xu, Jie; Shi, Ke‐Qing; Chen, Bicheng; Huang, Zhipeng; Lu, Feng; Zhou, Mengtao

doi:10.1111/jgh.13676

Cited by 32 publications

(39 citation statements)

References 35 publications

(67 reference statements)

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“…The nomogram developed by Brennan et al [ 5 ] included a large number of variables, such as tumor margins on resection, patient weight loss, and portal vein resection in the study, but the C-index of the model was 0.64, which may be explained by the fact that the study included distal PDAC which has a worse prognosis that PDAC of the head of the pancreas [ 18 ]. The nomogram established by Xu et al [ 11 ] showed high predictive accuracy for the prognosis of PDAC, but the development of the model was based on data from a single center with a small study population, and the standard values of the variables were inconsistent.…”

Section: Discussionmentioning

confidence: 99%

A Prognostic Nomogram for Disease-Specific Survival in Patients with Pancreatic Ductal Adenocarcinoma of the Head of the Pancreas Following Pancreaticoduodenectomy

Zhou

Zhao

2018

Med Sci Monit

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BackgroundThis study developed and validated a nomogram to predict patient prognosis for pancreatic ductal adenocarcinoma (PDAC) of the head of the pancreas following pancreaticoduodenectomy.Material/MethodsRetrospective data were obtained from 4,383 patients with PDAC of the head of the pancreas who underwent pancreaticoduodenectomy between 2004–2013 from 11 Registries Research Data of the Surveillance, Epidemiology,and End Results (SEER) database. Cox proportional hazards model was used to identify independent risk factors. The predictive accuracy of the nomogram was determined by the concordance index (C-index) and calibration curve. The results were externally validated by comparison with data from 1,743 patients from 7 other Registries Research Data.ResultsOf the 4,383 patients in the training dataset, median disease-specific survival (DSS) was 17.0 months (range, 1.0–131 months), and postoperative 1-year, 3-year, and 5-year DSS rates were 70.3%, 26.1%, and 16.8%, respectively. Multivariate analysis showed that patient sex, age, tumor grade, regional lymph nodes examined, positive regional lymph nodes, tumor size, extent of local invasion, and tumor metastases were independent risk factors for DSS. The C-index of the internal validation dataset for prediction of DSS was 0.64 (95% CI, 0.63–0.65), which was superior to the American Joint Committee on Cancer (AJCC) staging, 0.57 (95% CI, 0.56–0.58) (P<0.001). The 5-year DSS rates and median DSS time for patients in the low-risk group were significantly greater compared with high-risk group (P<0.001).ConclusionsA validated prognostic disease-specific nomogram for patient survival in PDAC of the head of the pancreas following pancreaticoduodenectomy was developed.

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Section: Discussionmentioning

confidence: 99%

A Prognostic Nomogram for Disease-Specific Survival in Patients with Pancreatic Ductal Adenocarcinoma of the Head of the Pancreas Following Pancreaticoduodenectomy

Zhou

Zhao

2018

Med Sci Monit

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“…The TNM classification developed by the American Joint Committee on Cancer has been used to estimate the prognosis of cancer. However, there are different prognoses in pancreatic cancer patients whose TNM stages are similar (Xu et al, 2017). Previous clinical research has shown the predictive effect of clinical pathological biomarkers such as tumor heterogeneity, main vessel invasion, and complexity at the genomic, epigenetic, and metabolic levels in patients with pancreatic cancer (Kleeff et al, 2016;Neoptolemos et al, 2018;Naito et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Development, Validation and Comparison of Artificial Neural Network Models and Logistic Regression Models Predicting Survival of Unresectable Pancreatic Cancer

Tong

Liu

et al. 2020

Front. Bioeng. Biotechnol.

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Background: Prediction models for the overall survival of pancreatic cancer remain unsatisfactory. We aimed to explore artificial neural networks (ANNs) modeling to predict the survival of unresectable pancreatic cancer patients. Methods: Thirty-two clinical parameters were collected from 221 unresectable pancreatic cancer patients, and their prognostic ability was evaluated using univariate and multivariate logistic regression. ANN and logistic regression (LR) models were developed on a training group (168 patients), and the area under the ROC curve (AUC) was used for comparison of the ANN and LR models. The models were further tested on the testing group (53 patients), and k-statistics were used for accuracy comparison. Results: We built three ANN models, based on 3, 7, and 32 basic features, to predict 8 month survival. All 3 ANN models showed better performance, with AUCs significantly higher than those from the respective LR models (0.811 vs. 0.680, 0.844 vs. 0.722, 0.921 vs. 0.849, all p < 0.05). The ability of the ANN models to discriminate 8 month survival with higher accuracy than the respective LR models was further confirmed in 53 consecutive patients. Conclusion: We developed ANN models predicting the 8 month survival of unresectable pancreatic cancer patients. These models may help to optimize personalized patient management.

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“…To overcome this defect, numerous recent studies have tried to investigate the prognostic significance of the albumin/globulin ratio (AGR) in cancer patients, which combines two factors and thus might reduce the influence of confounding factors. For instance, a body of studies reported that a low pretreatment AGR is closely associated with worse prognosis in patients with digestive system cancers, such as gastric cancer[ 9 , 10 ], colorectal cancer [ 11 – 15 ], pancreatic cancer[ 16 ], hepatocellular carcinoma[ 17 ] and so on. However, most studies published to date use a small sample size, which might affect the reliability of their conclusions.…”

Section: Introductionmentioning

confidence: 99%

Prognostic value of pretreatment albumin/globulin ratio in digestive system cancers: A meta-analysis

Guo¹,

Yuan²,

Chen³

et al. 2018

PLoS ONE

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The albumin/globulin ratio (AGR) has been widely reported to be a potential predictor of prognosis in digestive system cancers (DSCs), but convincing conclusions have not been made. Therefore, herein, we performed a meta-analysis of relevant studies regarding this topic to evaluate the prognostic value of AGR in patients with DSCs. Three databases, including PubMed, EMBase, and Web of science, were searched comprehensively for eligible studies through September 8, 2017. The outcomes of interest included overall survival (OS), disease-free survival (DFS), and cancer-specific survival (CSS). In our meta-analysis, pooled analysis of 13 studies with 9269 patients showed that a low AGR was significantly correlated with poor OS (HR = 1.94; 95% CI: 1.57–2.38; P <0.001). Five studies with 6538 participants involved DFS, and our pooled analysis of these studies also demonstrated that there was a significant association of a low AGR with worse DFS (HR = 1.49; 95% CI: 1.10 to 2.00; P < 0.001). In addition, only 2 studies referred to CSS, and we also detected a significant relationship between a low AGR and worse CSS from the results of our meta-analysis. In summary, a low pretreatment AGR was related to unfavorable survival in human digestive system cancers. A low pretreatment AGR may be a useful predictive prognostic biomarker in human digestive system cancers.

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A nomogram based on preoperative inflammatory markers predicting the overall survival of pancreatic ductal adenocarcinoma

Abstract: Based on clinicopathology characteristics and inflammatory markers, we developed a nomogram providing an individualized risk estimate for PDAC patients.

Cited by 32 publications

References 35 publications

A Prognostic Nomogram for Disease-Specific Survival in Patients with Pancreatic Ductal Adenocarcinoma of the Head of the Pancreas Following Pancreaticoduodenectomy

A Prognostic Nomogram for Disease-Specific Survival in Patients with Pancreatic Ductal Adenocarcinoma of the Head of the Pancreas Following Pancreaticoduodenectomy

Development, Validation and Comparison of Artificial Neural Network Models and Logistic Regression Models Predicting Survival of Unresectable Pancreatic Cancer

Prognostic value of pretreatment albumin/globulin ratio in digestive system cancers: A meta-analysis

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