Discrimination between Precancerous Gastric Lesions and Gastritis Using a Gastric Cancer Risk Stratification Model.
Biostatistics
Epidemiology
Machine Learning
precancerous lesions
risk stratification
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
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
935-943Subventions
Organisme : NCI NIH HHS
ID : R01 CA174853
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA057726
Pays : United States
Références
Int J Epidemiol. 2016 Jun;45(3):774-81
pubmed: 27170766
J Stat Softw. 2010;33(1):1-22
pubmed: 20808728
Int J Cancer. 2015 Mar 1;136(5):E359-86
pubmed: 25220842
Cancer Epidemiol Biomarkers Prev. 2018 Dec;27(12):1472-1479
pubmed: 30158280
J Dig Dis. 2012 Jan;13(1):2-9
pubmed: 22188910
Gut. 2015 Dec;64(12):1881-8
pubmed: 25748648
CA Cancer J Clin. 2021 May;71(3):209-249
pubmed: 33538338
Cancer Epidemiol Biomarkers Prev. 2006 Jul;15(7):1341-7
pubmed: 16835334
Gut. 2012 Jun;61(6):812-8
pubmed: 21917649
Helicobacter. 2009 Dec;14(6):525-35
pubmed: 19889070
Lancet. 2015 Mar 14;385(9972):977-1010
pubmed: 25467588
Gastroenterology. 2007 Aug;133(2):659-72
pubmed: 17681184
Digestion. 2016;93(1):13-8
pubmed: 26789514
Gastric Cancer. 2019 May;22(3):435-445
pubmed: 30206731
J Natl Cancer Inst. 1980 May;64(5):1263-72
pubmed: 6767876
BMC Bioinformatics. 2011 Mar 17;12:77
pubmed: 21414208
Ann Epidemiol. 1993 Nov;3(6):577-85
pubmed: 7921303
Cancer Epidemiol Biomarkers Prev. 2022 Apr 1;31(4):811-820
pubmed: 35131882
Am J Gastroenterol. 2010 Mar;105(3):493-8
pubmed: 20203636
JAMA. 2019 Nov 12;322(18):1806-1816
pubmed: 31714992
Am J Surg Pathol. 1996 Oct;20(10):1161-81
pubmed: 8827022
J Natl Cancer Inst. 2016 Jul 14;108(9):
pubmed: 27416750
Am J Surg Pathol. 2000 Feb;24(2):167-76
pubmed: 10680883
Stat Med. 1997 Feb 28;16(4):385-95
pubmed: 9044528