Survival prediction for gallbladder carcinoma after curative resection: Comparison of nomogram and Bayesian network models.
Adult
Age Factors
Aged
Aged, 80 and over
Area Under Curve
Bayes Theorem
Carcinoma
/ mortality
Cholecystectomy
Clinical Decision Rules
Female
Gallbladder Neoplasms
/ mortality
Humans
Liver
/ pathology
Lymph Nodes
/ pathology
Male
Margins of Excision
Middle Aged
Neoplasm Grading
Neoplasm Invasiveness
Neoplasm Staging
Nomograms
Prognosis
Proportional Hazards Models
Survival Rate
Bayesian network
Gallbladder carcinoma
Nomogram
Prediction model
Journal
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
ISSN: 1532-2157
Titre abrégé: Eur J Surg Oncol
Pays: England
ID NLM: 8504356
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
02
04
2020
revised:
24
06
2020
accepted:
07
07
2020
pubmed:
19
8
2020
medline:
2
3
2021
entrez:
19
8
2020
Statut:
ppublish
Résumé
In this study, we developed a nomogram and a Bayesian network (BN) model for prediction of survival in gallbladder carcinoma (GBC) patients following surgery and compared the performance of the two models. Survival prediction models were established and validated using data from 698 patients with GBC who underwent curative-intent resection between 2008 and 2017 at one of six Chinese tertiary hospitals. Model construction and internal validation were performed using data from 381 patients at one hepatobiliary center, and external validation was then performed using data from 317 patients at the other five centers. A BN model and a nomogram model were constructed based on the independent prognostic variables. Performance of the BN and nomogram models was compared based on area under receiver operating characteristic curves (AUC), model accuracy, and a confusion matrix. Independent prognostic variables included age, pathological grade, liver infiltration, T stage, N stage, and margin. In internal validation, AUC was 84.14% and 78.22% for the BN and nomogram, respectively, and model accuracy was 75.65% and 72.17%, respectively. In external validation, AUC was 76.46% and 70.19% for the BN and nomogram, respectively, with model accuracy of 66.88% and 60.25%, respectively. Based on the confusion matrix, the nomogram had a higher true positive rate but a substantially lower true negative rate compared to the BN. A BN model was more accurate than a Cox regression-based nomogram for prediction of survival in GBC patients undergoing curative-intent resection.
Sections du résumé
BACKGROUND
In this study, we developed a nomogram and a Bayesian network (BN) model for prediction of survival in gallbladder carcinoma (GBC) patients following surgery and compared the performance of the two models.
METHODS
Survival prediction models were established and validated using data from 698 patients with GBC who underwent curative-intent resection between 2008 and 2017 at one of six Chinese tertiary hospitals. Model construction and internal validation were performed using data from 381 patients at one hepatobiliary center, and external validation was then performed using data from 317 patients at the other five centers. A BN model and a nomogram model were constructed based on the independent prognostic variables. Performance of the BN and nomogram models was compared based on area under receiver operating characteristic curves (AUC), model accuracy, and a confusion matrix.
RESULTS
Independent prognostic variables included age, pathological grade, liver infiltration, T stage, N stage, and margin. In internal validation, AUC was 84.14% and 78.22% for the BN and nomogram, respectively, and model accuracy was 75.65% and 72.17%, respectively. In external validation, AUC was 76.46% and 70.19% for the BN and nomogram, respectively, with model accuracy of 66.88% and 60.25%, respectively. Based on the confusion matrix, the nomogram had a higher true positive rate but a substantially lower true negative rate compared to the BN.
CONCLUSION
A BN model was more accurate than a Cox regression-based nomogram for prediction of survival in GBC patients undergoing curative-intent resection.
Identifiants
pubmed: 32807616
pii: S0748-7983(20)30616-8
doi: 10.1016/j.ejso.2020.07.009
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2106-2113Informations de copyright
Copyright © 2020 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors have no conflict of interest to declare.