Calibrating multiplex serology for Helicobacter pylori
Abstract Background Helicobacter pylori (H. pylori) is a bacterium that colonizes the stomach and is a major risk factor for gastric cancer, with an estimated 89% of non-cardia gastric cancer cases worldwide attributable to H. pylori. Prospective studies provide reliable evidence for quantifying the...
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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | Diagnostic and Prognostic Research |
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| Online Access: | https://doi.org/10.1186/s41512-025-00202-x |
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| author | Emmanuelle A. Dankwa Martyn Plummer Daniel Chapman Rima Jeske Julia Butt Michael Hill Tim Waterboer Iona Y. Millwood Ling Yang Christiana Kartsonaki |
| author_facet | Emmanuelle A. Dankwa Martyn Plummer Daniel Chapman Rima Jeske Julia Butt Michael Hill Tim Waterboer Iona Y. Millwood Ling Yang Christiana Kartsonaki |
| author_sort | Emmanuelle A. Dankwa |
| collection | DOAJ |
| description | Abstract Background Helicobacter pylori (H. pylori) is a bacterium that colonizes the stomach and is a major risk factor for gastric cancer, with an estimated 89% of non-cardia gastric cancer cases worldwide attributable to H. pylori. Prospective studies provide reliable evidence for quantifying the association between gastric cancer and H. pylori, as they circumvent the risk of a false negative due to possible reduction in antibody levels before cancer development. Methods In a large-scale prospective study within the China Kadoorie Biobank, H. pylori infection is being analysed as a risk factor for gastric cancer. The presence of infection is typically determined by serological tests. The immunoblot test, although well established, is more labour intensive and uses a larger amount of plasma than the alternative high-throughput multiplex serology test. Immunoblot outputs a binary positive/negative serostatus classification, while multiplex outputs a vector of continuous antigen measurements. When mapping such multidimensional continuous measurements onto a binary classification, statistical challenges arise in defining classification cut-offs and accounting for the differences in infection evidence provided by different antigens. We discuss these challenges and propose a novel solution to optimize the translation of the continuous measurements from multiplex serology into probabilities of H. pylori infection, using classification algorithms (Bayesian additive regressive trees (BART), multidimensional monotone BART, logistic regression, random forest and elastic net). We (i) calibrate and apply classification models to predict probabilities of H. pylori infection given multiplex measurements, (ii) compare the predictive performance of the models using immunoblot as reference, (iii) discuss reasons for the differences in predictive performance and (iv) apply the calibrated models to gain insights on the relative strengths of infection evidence provided by the various antigens. Results All models showed high discriminative ability with at least 95% area under the curve (AUC) estimates on the training and test data. There was no substantial difference between the performance of models on the training and test data. Conclusions Classification algorithms can be used to calibrate the H. pylori multiplex serology test to the immunoblot test in the China Kadoorie Biobank. This study furthers our understanding of the applicability of classification algorithms to the context of serologic tests. |
| format | Article |
| id | doaj-art-c3dd890fbcce4cd2a8960de0237d74b6 |
| institution | Kabale University |
| issn | 2397-7523 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Diagnostic and Prognostic Research |
| spelling | doaj-art-c3dd890fbcce4cd2a8960de0237d74b62025-08-20T03:42:10ZengBMCDiagnostic and Prognostic Research2397-75232025-08-019111110.1186/s41512-025-00202-xCalibrating multiplex serology for Helicobacter pyloriEmmanuelle A. Dankwa0Martyn Plummer1Daniel Chapman2Rima Jeske3Julia Butt4Michael Hill5Tim Waterboer6Iona Y. Millwood7Ling Yang8Christiana Kartsonaki9Harvard T. H. Chan School of Public HealthDepartment of Statistics, University of WarwickClinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of OxfordInfections and Cancer Epidemiology Division, German Cancer Research CenterInfections and Cancer Epidemiology Division, German Cancer Research CenterClinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of OxfordInfections and Cancer Epidemiology Division, German Cancer Research CenterClinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of OxfordAbstract Background Helicobacter pylori (H. pylori) is a bacterium that colonizes the stomach and is a major risk factor for gastric cancer, with an estimated 89% of non-cardia gastric cancer cases worldwide attributable to H. pylori. Prospective studies provide reliable evidence for quantifying the association between gastric cancer and H. pylori, as they circumvent the risk of a false negative due to possible reduction in antibody levels before cancer development. Methods In a large-scale prospective study within the China Kadoorie Biobank, H. pylori infection is being analysed as a risk factor for gastric cancer. The presence of infection is typically determined by serological tests. The immunoblot test, although well established, is more labour intensive and uses a larger amount of plasma than the alternative high-throughput multiplex serology test. Immunoblot outputs a binary positive/negative serostatus classification, while multiplex outputs a vector of continuous antigen measurements. When mapping such multidimensional continuous measurements onto a binary classification, statistical challenges arise in defining classification cut-offs and accounting for the differences in infection evidence provided by different antigens. We discuss these challenges and propose a novel solution to optimize the translation of the continuous measurements from multiplex serology into probabilities of H. pylori infection, using classification algorithms (Bayesian additive regressive trees (BART), multidimensional monotone BART, logistic regression, random forest and elastic net). We (i) calibrate and apply classification models to predict probabilities of H. pylori infection given multiplex measurements, (ii) compare the predictive performance of the models using immunoblot as reference, (iii) discuss reasons for the differences in predictive performance and (iv) apply the calibrated models to gain insights on the relative strengths of infection evidence provided by the various antigens. Results All models showed high discriminative ability with at least 95% area under the curve (AUC) estimates on the training and test data. There was no substantial difference between the performance of models on the training and test data. Conclusions Classification algorithms can be used to calibrate the H. pylori multiplex serology test to the immunoblot test in the China Kadoorie Biobank. This study furthers our understanding of the applicability of classification algorithms to the context of serologic tests.https://doi.org/10.1186/s41512-025-00202-xHelicobacter pyloriClassification algorithmsMultiplex serologyImmunoblotPredictionSupervised learning |
| spellingShingle | Emmanuelle A. Dankwa Martyn Plummer Daniel Chapman Rima Jeske Julia Butt Michael Hill Tim Waterboer Iona Y. Millwood Ling Yang Christiana Kartsonaki Calibrating multiplex serology for Helicobacter pylori Diagnostic and Prognostic Research Helicobacter pylori Classification algorithms Multiplex serology Immunoblot Prediction Supervised learning |
| title | Calibrating multiplex serology for Helicobacter pylori |
| title_full | Calibrating multiplex serology for Helicobacter pylori |
| title_fullStr | Calibrating multiplex serology for Helicobacter pylori |
| title_full_unstemmed | Calibrating multiplex serology for Helicobacter pylori |
| title_short | Calibrating multiplex serology for Helicobacter pylori |
| title_sort | calibrating multiplex serology for helicobacter pylori |
| topic | Helicobacter pylori Classification algorithms Multiplex serology Immunoblot Prediction Supervised learning |
| url | https://doi.org/10.1186/s41512-025-00202-x |
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