Dataset Information

Preoperative plasma D-dimer independently predicts survival in patients with pancreatic ductal adenocarcinoma undergoing radical resection.

ABSTRACT:

Background

Elevated plasma D-dimer levels have been reported as an unfavorable prognostic indicator in many solid tumors. However, there are limited relevant studies in pancreatic cancer patients following radical surgery, and the clinical significance remains controversial. The aim of this study was to investigate the clinical and prognostic significance of preoperative plasma D-dimer in patients with pancreatic ductal adenocarcinoma (PDAC) undergoing resection.

Methods

A retrospective analysis was performed on all patients who consecutively underwent radical surgery for PDAC by laparotomy or robotic surgery from December 2011 to December 2018. Baseline clinicopathologic characteristics, preoperative laboratory parameters, and follow-up information were collected. Univariate and multivariate analyses were performed to analyze the prognostic value of preoperative plasma D-dimer.

Results

Among 1351 patients, elevated preoperative plasma D-dimer levels (≥ 0.55 ng/mL) were found in 417 (30.9%) patients. Three hundred twelve (23.09%) underwent minimally invasive robotic pancreatectomy. The median overall survival (OS) of patients with elevated D-dimer levels was 6.3 months shorter than that of patients with normal D-dimer levels (15.0 months vs 21.3 months, p < 0.001). Multivariate analysis showed that elevated D-dimer levels independently predicted poorer OS (hazard ratio, 1.33; 95% confidence interval, 1.17-1.51, p < 0.001). Subgroup analysis demonstrated that D-dimer was a reliable prognostic factor in patients who underwent R0 resection. In addition, integration of D-dimer, carbohydrate antigen 19-9 (CA19-9), and NLR provided a better prognostic model for PDAC patients before operation.

Conclusion

An elevated preoperative plasma D-dimer level was a reliable independent prognostic factor for OS in patients with PDAC undergoing resection. Combination of D-dimer, CA19-9, and NLR can enhance the prognostic accuracy before operation.

SUBMITTER: Chen H

PROVIDER: S-EPMC8191214 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters.MethodsDatasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk.FindingsThe developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set.InterpretationThis study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies.FundingThis work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

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