A Novel Clinically Based Staging System for Gallbladder Cancer.


Journal

Journal of the National Comprehensive Cancer Network : JNCCN
ISSN: 1540-1413
Titre abrégé: J Natl Compr Canc Netw
Pays: United States
ID NLM: 101162515

Informations de publication

Date de publication:
02 2020
Historique:
received: 08 05 2019
accepted: 04 09 2019
entrez: 6 2 2020
pubmed: 6 2 2020
medline: 9 2 2021
Statut: ppublish

Résumé

Current staging systems for gallbladder cancer (GBC) are primarily based on surgical pathology and therefore are not relevant for unresectable patients and those undergoing neoadjuvant chemotherapy. Patients with a confirmed diagnosis of GBC managed at a tertiary referral center (2000-2016) were included. Independent predictors of overall survival (OS) were identified using multivariable analysis (MVA). A combination of these variables was then assessed to identify a set of factors that provided maximal accuracy in predicting OS, and a nomogram and a new staging system were created based on these factors. Harrell's C-statistic was calculated to evaluate the predictive accuracy of the nomogram and staging system. A total of 528 patients were included in the final analysis. On MVA, factors predictive of poor OS were older age, ECOG performance status, hemoglobin level <9 g/dL, presence of metastases, and alkaline phosphatase (ALP) level >200 U/L. A nomogram and a 4-tier staging system predictive of OS were created using age at diagnosis, ECOG status, tumor size, presence or absence of metastasis, and ALP level. The C-statistic for this novel staging system was 0.71 compared with 0.69 for the TNM staging system (P=.08). In patients who did not undergo surgery, the C-statistics of the novel and TNM staging systems were 0.60 and 0.51, respectively (P<.001). We created a novel, clinically based staging system for GBC based on nonoperative information at the time of diagnosis that was superior to the TNM staging system in predicting OS in patients who did not undergo surgery, and that performed on par with TNM staging in surgical patients.

Sections du résumé

BACKGROUND
Current staging systems for gallbladder cancer (GBC) are primarily based on surgical pathology and therefore are not relevant for unresectable patients and those undergoing neoadjuvant chemotherapy.
METHODS
Patients with a confirmed diagnosis of GBC managed at a tertiary referral center (2000-2016) were included. Independent predictors of overall survival (OS) were identified using multivariable analysis (MVA). A combination of these variables was then assessed to identify a set of factors that provided maximal accuracy in predicting OS, and a nomogram and a new staging system were created based on these factors. Harrell's C-statistic was calculated to evaluate the predictive accuracy of the nomogram and staging system.
RESULTS
A total of 528 patients were included in the final analysis. On MVA, factors predictive of poor OS were older age, ECOG performance status, hemoglobin level <9 g/dL, presence of metastases, and alkaline phosphatase (ALP) level >200 U/L. A nomogram and a 4-tier staging system predictive of OS were created using age at diagnosis, ECOG status, tumor size, presence or absence of metastasis, and ALP level. The C-statistic for this novel staging system was 0.71 compared with 0.69 for the TNM staging system (P=.08). In patients who did not undergo surgery, the C-statistics of the novel and TNM staging systems were 0.60 and 0.51, respectively (P<.001).
CONCLUSIONS
We created a novel, clinically based staging system for GBC based on nonoperative information at the time of diagnosis that was superior to the TNM staging system in predicting OS in patients who did not undergo surgery, and that performed on par with TNM staging in surgical patients.

Identifiants

pubmed: 32023527
doi: 10.6004/jnccn.2019.7357
pii: jnccn19108
doi:
pii:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

151-159

Auteurs

Siddhartha Yadav (S)

Department of Oncology, Mayo Clinic, Rochester, Minnesota.

Sri Harsha Tella (SH)

Department of Medicine, University of South Carolina School of Medicine, Columbia, South Carolina.

Anuhya Kommalapati (A)

H. Lee Moffitt Cancer Center, Tampa, Florida; and.

Kristin Mara (K)

Department of Biomedical Statistics and Informatics.

Kritika Prasai (K)

Department of Oncology, Mayo Clinic, Rochester, Minnesota.

Mohamed Hamdy Mady (MH)

Department of Oncology, Mayo Clinic, Rochester, Minnesota.

Mohamed Hassan (M)

Department of Gastroenterology and Hepatology, and.

Rory L Smoot (RL)

Division of Subspecialty General Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota.

Sean P Cleary (SP)

Division of Subspecialty General Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota.

Mark J Truty (MJ)

Division of Subspecialty General Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota.

Lewis R Roberts (LR)

Department of Gastroenterology and Hepatology, and.

Amit Mahipal (A)

Department of Oncology, Mayo Clinic, Rochester, Minnesota.

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