Discrimination between Precancerous Gastric Lesions and Gastritis Using a Gastric Cancer Risk Stratification Model.


Journal

Asian Pacific journal of cancer prevention : APJCP
ISSN: 2476-762X
Titre abrégé: Asian Pac J Cancer Prev
Pays: Thailand
ID NLM: 101130625

Informations de publication

Date de publication:
01 Mar 2023
Historique:
received: 06 10 2022
medline: 29 3 2023
entrez: 28 3 2023
pubmed: 29 3 2023
Statut: epublish

Résumé

Seropositivity to certain Helicobacter pylori proteins may affect development of gastric lesions that could become cancerous. Previously, we developed a model of gastric cancer risk including gender, age, HP0305 sero-positivity, HP1564 sero-positivity, UreA antibody titer and serologically defined chronic atrophic gastritis (termed: "Lasso model"). We evaluated the Lasso model's ability to discriminate individuals with precancerous gastric lesions (n=320) from individuals with superficial or mild atrophic gastritis (n=226) in Linqu County, China, a population at high risk for gastric cancer. We also compared its performance to the ABC Method, a gastric cancer risk stratification tool currently used in East Asia. For distinguishing precancerous lesions from those with gastritis, the receiver operating characteristic curve had an area under the curve (AUC) of 73.41% (95% CI: 69.10%, 77.71%) and, at Youden's Index, a sensitivity of 78.44% (59.38%, 82.50%) and specificity of 64.72% (95% CI: 58.85%, 81.42%). Positive predictive value (PPV) was 75.38% (72.78%, 82.51%). Specificity, AUC and PPV were significantly greater (p < 0.05) than those of the ABC Method. When specificity was held constant, the Lasso model had greater sensitivity, PPV and negative predictive value (NPV) than the ABC Method. However, adjusting the ABC Method for age and gender negated the Lasso model's significant improvement in AUC. The Lasso model for gastric cancer risk prediction can classify precancerous lesions with significantly greater AUC than the ABC Method and, at constant specificity, with greater sensitivity, PPV and NPV. However, adding age and gender to the ABC Method, as included in the Lasso model, substantially improved its performance and negated the Lasso model's advantage.

Sections du résumé

BACKGROUND BACKGROUND
Seropositivity to certain Helicobacter pylori proteins may affect development of gastric lesions that could become cancerous. Previously, we developed a model of gastric cancer risk including gender, age, HP0305 sero-positivity, HP1564 sero-positivity, UreA antibody titer and serologically defined chronic atrophic gastritis (termed: "Lasso model").
METHODS METHODS
We evaluated the Lasso model's ability to discriminate individuals with precancerous gastric lesions (n=320) from individuals with superficial or mild atrophic gastritis (n=226) in Linqu County, China, a population at high risk for gastric cancer. We also compared its performance to the ABC Method, a gastric cancer risk stratification tool currently used in East Asia.
RESULTS RESULTS
For distinguishing precancerous lesions from those with gastritis, the receiver operating characteristic curve had an area under the curve (AUC) of 73.41% (95% CI: 69.10%, 77.71%) and, at Youden's Index, a sensitivity of 78.44% (59.38%, 82.50%) and specificity of 64.72% (95% CI: 58.85%, 81.42%). Positive predictive value (PPV) was 75.38% (72.78%, 82.51%). Specificity, AUC and PPV were significantly greater (p < 0.05) than those of the ABC Method. When specificity was held constant, the Lasso model had greater sensitivity, PPV and negative predictive value (NPV) than the ABC Method. However, adjusting the ABC Method for age and gender negated the Lasso model's significant improvement in AUC.
CONCLUSIONS CONCLUSIONS
The Lasso model for gastric cancer risk prediction can classify precancerous lesions with significantly greater AUC than the ABC Method and, at constant specificity, with greater sensitivity, PPV and NPV. However, adding age and gender to the ABC Method, as included in the Lasso model, substantially improved its performance and negated the Lasso model's advantage.

Identifiants

pubmed: 36974548
doi: 10.31557/APJCP.2023.24.3.935
pmc: PMC10334080
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

935-943

Subventions

Organisme : NCI NIH HHS
ID : R01 CA174853
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA057726
Pays : United States

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Auteurs

John D Murphy (JD)

University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Department of Epidemiology, Chapel Hill, NC, USA.

Meira Epplein (M)

Duke University, Department of Population Health Sciences, and Duke Cancer Institute, Cancer Risk, Detection, and Interception Program, Durham, NC, USA.

Feng-Chang Lin (FC)

University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Department of Biostatistics, Chapel Hill, NC, USA.

Melissa A Troester (MA)

University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Department of Epidemiology, Chapel Hill, NC, USA.

Hazel B Nichols (HB)

University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Department of Epidemiology, Chapel Hill, NC, USA.

Julia Butt (J)

Deutsches Krebsforschungszentrum, Heidelberg, Germany.

Kaifeng Pan (K)

Peking University Cancer Hospital, Beijing, China.

Weicheng You (W)

Peking University Cancer Hospital, Beijing, China.

Andrew Olshan (A)

University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Department of Epidemiology, Chapel Hill, NC, USA.

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Classifications MeSH