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BMC Cancer
Published in: BMC Cancer 1/2017

Open Access 01-12-2017 | Research article

Receiver operating characteristic analysis of prediction for gastric cancer development using serum pepsinogen and Helicobacter pylori antibody tests

Authors: Chisato Hamashima, Shizuka Sasazuki, Manami Inoue, Shoichiro Tsugane, for the JPHC Study Group

Published in: BMC Cancer | Issue 1/2017

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Abstract

Background

Chronic Helicobacter pylori infection plays a central role in the development of gastric cancer as shown by biological and epidemiological studies. The H. pylori antibody and serum pepsinogen (PG) tests have been anticipated to predict gastric cancer development.

Methods

We determined the predictive sensitivity and specificity of gastric cancer development using these tests. Receiver operating characteristic analysis was performed, and areas under the curve were estimated. The predictive sensitivity and specificity of gastric cancer development were compared among single tests and combined methods using serum pepsinogen and H. pylori antibody tests.

Results

From a large-scale population-based cohort of over 100,000 subjects followed between 1990 and 2004, 497 gastric cancer subjects and 497 matched healthy controls were chosen. The predictive sensitivity and specificity were low in all single tests and combination methods. The highest predictive sensitivity and specificity were obtained for the serum PG I/II ratio. The optimal PG I/II cut-off values were 2.5 and 3.0. At a PG I/II cut-off value of 3.0, the sensitivity was 86.9% and the specificity was 39.8%. Even if three biomarkers were combined, the sensitivity was 97.2% and the specificity was 21.1% when the cut-off values were 3.0 for PG I/II, 70 ng/mL for PG I, and 10.0 U/mL for H. pylori antibody.

Conclusions

The predictive accuracy of gastric cancer development was low with the serum pepsinogen and H. pylori antibody tests even if these tests were combined. To adopt these biomarkers for gastric cancer screening, a high specificity is required. When these tests are adopted for gastric cancer screening, they should be carefully interpreted with a clear understanding of their limitations.
Literature
1.
2.
3.
IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. HELICOBACTER PYLORI. In: IARC Working Group Reports Volume 8. Helicobacter pylori eradication as a strategy for preventing gastric cancer. Lyon: IARC; 2014. p. 1–4.
4.
IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. HELICOBACTER PYLORI. In: International Agency of Research on Cancer. IARC Monographs on the evaluation of carcinogenic risks to human: Biological agents. Volume 100B. Lyon: IARC; 2012. p. 385–435.
5.
Plummer M, Franceschi S, Vignat J, et al. Global burden of gastric cancer attributable to Helicobacter pylori. Int J Cancer. 2015;136:487–90.CrossRefPubMed
6.
Crabtree JE, Covacci A, Farmery SM, et al. Helicobacter pylori-induced interleukin-8 expression in gastric epithelial cells is associated with CagA positive phenotype. J Clin Pathol. 1995;48:41–5.CrossRefPubMedPubMedCentral
7.
Cover TL, Glupczynski Y, Lage AP, et al. Serologic detection of infection with CagA+ Helicobacter pyloristrains. J Clin Microbiol. 1995;33:1496–500.PubMedPubMedCentral
8.
Blaser MJ. Intrastrain differences in Helicobacter pylori: a key question in mucosal damage? Ann Med. 1995;27:559–63.CrossRefPubMed
9.
Rizzato C, Kato I, Plummer M, et al. Risk of advanced gastric precancerous lesions in Helicobacter pylori infected subjects is influenced by ABO blood group and cagA status. Int J Cancer. 2013;133:315–22.CrossRefPubMedPubMedCentral
10.
Loh JT, Shaffer CL, Piazuelo MB, et al. Analysis of cagA in Helicobacter pylori strains from Colombian populations with contrasting gastric cancer risk reveals a biomarker for disease severity. Cancer Epidemiol Biomarkers Prev. 2011;20:2237–49.CrossRefPubMedPubMedCentral
11.
Sasazuki S, Inoue M, Iwasaki M, et al. Effect of Helicobacter pylori infection combined with CagA and pepsinogen status on gastric cancer development among Japanese men and women: a nested case-control study. Cancer Epidemiol Biomarkers Prev. 2006;15:1341–7.CrossRefPubMed
12.
Buckland G, Travier N, Huerta JM, et al. Healthy lifestyle index and risk of gastric adenocarcinoma in the EPIC cohort study. Int J Cancer. 2015;137:598–606.CrossRefPubMed
13.
Ohata H, Kitauchi S, Yoshimura N, et al. Progression of chronic atrophic gastritis associated with Helicobacter pylori infection increases risk of gastric cancer. Int J Cancer. 2004;109:138–43.CrossRefPubMed
14.
Watabe H, Mitsushima T, Yamaji Y, et al. Predicting the development of gastric cancer from combining Helicobacter pylori antibodies and serum pepsinogen status: a prospective endoscopic cohort study. Gut. 2005;54:764–8.CrossRefPubMedPubMedCentral
15.
Mizuno S, Miki I, Ishida T, et al. Prescreening of a high-risk group for gastric cancer by serologically determined Helicobacter pylori infection and atrophic gastritis. Dig Dis Sci. 2010;55:3132–7.CrossRefPubMed
16.
Zhang X, Xue L, Xing L, et al. Low serum pepsinogen I and pepsinogen I/II ratio and Helicobacter pylori infection are associated with increased risk of gastric cancer: 14-year follow up result in a rural Chinese community. Int J Cancer. 2012;130:1614–9.CrossRefPubMed
17.
Yoshida T, Kato J, Inoue I, et al. Cancer development based on chronic active gastritis and resulting gastric atrophy as assessed by serum levels of pepsinogen and Helicobacter pylori antibody titer. Int J Cancer. 2014;134:1445–57.CrossRefPubMed
18.
Charvat H, Sasazuki S, Inoue M, JPHC Study Group, et al. Prediction of the 10-year probability of gastric cancer occurrence in the Japanese population: the JPHC study cohort II. Int J Cancer. 2016;138:320–31.CrossRefPubMed
19.
Akobeng AK. Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr. 2007;96:644–7.CrossRefPubMed
20.
Wilt TJ, Harris RP, Qaseem A. Screening for cancer: advice for high-value care from the American College of Physicians. Ann Intern Med. 2015;162:718–25.CrossRefPubMed
21.
Terasawa T, Nishida H, Kato K, et al. Prediction of gastric cancer development by serum pepsinogen test and Helicobacter pylori seropositivity in Eastern Asians: A systematic review and meta-analysis. PLoS One. 2014;9, e109783.CrossRefPubMedPubMedCentral
22.
Fischer JE, Bachmann LM, Jaeschke R. A readers’ guide to the interpretation of diagnostic test properties: clinical example of sepsis. Intensive Care Med. 2003;29:1043–51.CrossRefPubMed
23.
Royston P, Moons KG, Altman DG, et al. Prognosis and prognostic research: Developing a prognostic model. BMJ. 2009;338:1373–7.CrossRef
24.
Winkler Wille MM, van Riel SJ, Saghir Z, et al. Predictive accuracy of the PanCan lung cancer risk prediction model: External validation based on CT from the Danish Lung Cancer Screening Trial. Eur Radiol. 2015;25:3093–9.CrossRefPubMed
25.
Tammemagi CM, Pinsky PF, Caporaso NE, et al. Lung cancer risk prediction: Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial models and validation. J Natl Cancer Inst. 2011;103:1058–68.CrossRefPubMedPubMedCentral
26.
Kovalchik SA, Tammemagi M, Berg CD, et al. Targeting of low-dose CT screening according to the risk of lung-cancer death. N Engl J Med. 2013;369:245–54.CrossRefPubMedPubMedCentral
27.
Raji OY, Duffy SW, Agbaje OF, et al. Predictive accuracy of the Liverpool Lung Project risk model for stratifying patients for computed tomography screening for lung cancer: a case-control and cohort validation study. Ann Intern Med. 2012;157:242–50.CrossRefPubMedPubMedCentral
28.
Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how? BMJ. 2009;338:1317–20.CrossRef
29.
Kondo H, Yoneda M, Inoue K. Application of risk evaluation for gastric cancer (ABC classification) to population-based gastric cancer screening: Current status and issues in Maniwa City, Okayama Prefecture. J Gastrointestinal Cancer Screen. 2015;53:589–99 (in Japanese).
30.
Suzuki H, Saito Y. Current situation of gastric cancer risk evaluation system “ABC classification” in Japanese local governments. J Gastrointestinal Cancer Screen. 2015;53:463–70 (in Japanese).
31.
Moons KG, Altman DG, Vergouwe Y, et al. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ. 2009;338:1487–90.
32.
Dijkstra MG, van Zummeren M, Rozendaal L, et al. Safety of extending screening intervals beyond five years in cervical screening programmes with testing for high risk human papillomavirus: 14 year follow-up of population based randomised cohort in the Netherlands. BMJ. 2016;355:i4924.CrossRefPubMed
33.
Elfström KM, Smelov V, Johansson AL, et al. Long term duration of protective effect for HPV negative women: follow-up of primary HPV screening randomised controlled trial. BMJ. 2014;348:g130.CrossRefPubMedPubMedCentral
34.
Nomura S, Ida K, Terao S, et al. Endoscopic diagnosis of gastric mucosal atrophy: multicenter prospective study. Dig Endosc. 2014;26:709–19.CrossRefPubMed
35.
Hirayama Y, Kawai T, Otaki J, et al. Prevalence of Helicobacter pylori infection with healthy subjects in Japan. J Gastroenterol Hepatol. 2014;29 Suppl 4:16–9.CrossRefPubMed
36.
Kamada T, Haruma K, Ito M, et al. Time trends in Helicobacter pylori infection and atrophic gastritis over 40 years in Japan. Helicobacter. 2015;20:192–8.CrossRefPubMed
37.
Iwasaki M, Otani T, Yamamoto S, et al. Background characteristics of basic health examination participants: the JPHC Study Baseline Survey. J Epidemiol. 2003;13:216–25.CrossRefPubMed
38.
Lijmer JG, Mol BW, Heisterkamp S, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA. 1999;282:1061–6.CrossRefPubMed
39.
Whiting PF, Rutjes AW, Westwood ME, QUADAS-2 Group, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529–36.CrossRefPubMed
Metadata
Title
Receiver operating characteristic analysis of prediction for gastric cancer development using serum pepsinogen and Helicobacter pylori antibody tests
Authors
Chisato Hamashima
Shizuka Sasazuki
Manami Inoue
Shoichiro Tsugane
for the JPHC Study Group
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2017
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-017-3173-0

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