Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer

Pancreatic cancer is a fatal tumor, and the status of perineural invasion (PNI) of pancreatic cancer was positively related to poor prognosis including overall survival and recurrence-free survival. This study aims to develop and validate a predictive model based on serum biomarkers to accurately pr...

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Published in:BMC gastroenterology 2023-09, Vol.23 (1), p.315-315, Article 315
Main Authors: Zou, Wenbo, Wu, Dingguo, Wu, Yunyang, Zhou, Kuiping, Lian, Yuanshu, Chang, Gengyun, Feng, Yuze, Liang, Jifeng, Huang, Gao
Format: Article
Language:English
Subjects:
Area Under Curve
Bilirubin
Biological markers
Biomarkers
Blood sugar
CA-19-9 Antigen
Cancer therapies
Decision making
Gastroenterology
Glucose
Humans
Logistics
Medical prognosis
Medical research
Medicine, Experimental
Metastasis
Mortality
Nomogram
Nomograms
Pancreatic cancer
Pancreatic head adenocarcinoma
Pancreatic Neoplasms
Pancreatic Neoplasms - diagnosis
Pancreaticoduodenectomy
Patients
Perineural invasion
Prediction models
Probability
Prognosis
Regression analysis
Risk factors
Serum indicator
Surgery
Survival
Tumors
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container_issue 1
container_start_page 315
container_title BMC gastroenterology
container_volume 23
creator Zou, Wenbo
Wu, Dingguo
Wu, Yunyang
Zhou, Kuiping
Lian, Yuanshu
Chang, Gengyun
Feng, Yuze
Liang, Jifeng
Huang, Gao
description Pancreatic cancer is a fatal tumor, and the status of perineural invasion (PNI) of pancreatic cancer was positively related to poor prognosis including overall survival and recurrence-free survival. This study aims to develop and validate a predictive model based on serum biomarkers to accurately predict the perineural invasion. The patients from No.924 Hospital of PLA Joint Logistic Support Force were included. The predictive model was developed in the training cohort using logistic regression analysis, and then tested in the validation cohort. The area under curve (AUC), calibration curves and decision curve analysis were used to validate the predictive accuracy and clinical benefits of nomogram. A nomogram was developed using preoperative total bilirubin, preoperative blood glucose, preoperative CA19-9. It achieved good AUC values of 0.753 and 0.737 in predicting PNI in training and validation cohorts, respectively. Calibration curves showed nomogram had good uniformity of the practical probability of PNI. Decision curve analyses revealed that the nomogram provided higher diagnostic accuracy and superior net benefit compared to single indicators. The present study constructed and validate a novel nomogram predicted the PNI of resectable PHAC patients with high stability and accuracy. Besides, it could better screen high-risk probability of PNI in these patients, and optimize treatment decision-making.
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subjects Area Under Curve
Bilirubin
Biological markers
Biomarkers
Blood sugar
CA-19-9 Antigen
Cancer therapies
Decision making
Gastroenterology
Glucose
Humans
Logistics
Medical prognosis
Medical research
Medicine, Experimental
Metastasis
Mortality
Nomogram
Nomograms
Pancreatic cancer
Pancreatic head adenocarcinoma
Pancreatic Neoplasms
Pancreatic Neoplasms - diagnosis
Pancreaticoduodenectomy
Patients
Perineural invasion
Prediction models
Probability
Prognosis
Regression analysis
Risk factors
Serum indicator
Surgery
Survival
Tumors
title Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer
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