Optimal number of harvested lymph nodes for curatively resected gallbladder adenocarcinoma based on a Bayesian network model.
Bayesian network
curative resection
gallbladder adenocarcinoma
lymph nodes
number
Journal
Journal of surgical oncology
ISSN: 1096-9098
Titre abrégé: J Surg Oncol
Pays: United States
ID NLM: 0222643
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
23
05
2020
revised:
03
08
2020
accepted:
03
08
2020
pubmed:
21
8
2020
medline:
29
12
2020
entrez:
22
8
2020
Statut:
ppublish
Résumé
To identify the optimal range and the minimum number of lymph nodes (LNs) to be examined to maximize survival time of patients with curatively resected gallbladder adenocarcinoma (GBAC). Data were collected from the surveillance, epidemiology, and end results database on patients with GBAC who underwent curative resection between 2004 and 2015. A Bayesian network (BN) model was constructed to identify the optimal range of harvested LNs. Model accuracy was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve. A total of 1268 patients were enrolled in this study. Accuracy of the BN model was 72.82%, and the area under the curve of the ROC for the testing dataset was 78.49%. We found that at least seven LNs should be harvested to maximize survival time, and that the optimal count of harvested LNs was in the range of 7 to 10 overall, with an optimal range of 10 to 11 for N+ patients, 7 to 10 for stage T1-T2 patients, and 7 to 11 for stage T3-T4 patients. According to a BN model, at least seven LNs should be retrieved for GBAC with curative resection, with an overall optimal range of 7 to 10 harvested LNs.
Sections du résumé
BACKGROUND AND OBJECTIVES
OBJECTIVE
To identify the optimal range and the minimum number of lymph nodes (LNs) to be examined to maximize survival time of patients with curatively resected gallbladder adenocarcinoma (GBAC).
METHODS
METHODS
Data were collected from the surveillance, epidemiology, and end results database on patients with GBAC who underwent curative resection between 2004 and 2015. A Bayesian network (BN) model was constructed to identify the optimal range of harvested LNs. Model accuracy was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve.
RESULTS
RESULTS
A total of 1268 patients were enrolled in this study. Accuracy of the BN model was 72.82%, and the area under the curve of the ROC for the testing dataset was 78.49%. We found that at least seven LNs should be harvested to maximize survival time, and that the optimal count of harvested LNs was in the range of 7 to 10 overall, with an optimal range of 10 to 11 for N+ patients, 7 to 10 for stage T1-T2 patients, and 7 to 11 for stage T3-T4 patients.
CONCLUSIONS
CONCLUSIONS
According to a BN model, at least seven LNs should be retrieved for GBAC with curative resection, with an overall optimal range of 7 to 10 harvested LNs.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1409-1417Subventions
Organisme : National Natural Science Foundation of China
ID : 81572420
Organisme : National Natural Science Foundation of China
ID : 71871181
Organisme : Key Research and Development Program of Shaanxi Province
ID : 2017ZDXM-SF-055
Organisme : Clinical Training Program of Shanghai Xinhua Hospital Affiliated to Shanghai Jiaotong University, School of Medicine
ID : 17CSK06
Organisme : Multicenter Clinical Research Project of School of Medicine, Shanghai Jiaotong University
ID : DLY201807
Informations de copyright
© 2020 Wiley Periodicals LLC.
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