Discrimination between Precancerous Gastric Lesions and Gastritis Using a Gastric Cancer Risk Stratification Model
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: 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: 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: 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
Asian Pacific journal of cancer prevention : APJCP - 24(2023), 3 vom: 01. März, Seite 935-943 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Murphy, John D [VerfasserIn] |
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| Links: |
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| Themen: |
Biostatistics |
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| Anmerkungen: |
Date Completed 29.03.2023 Date Revised 13.07.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.31557/APJCP.2023.24.3.935 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM354795554 |
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| 520 | |a 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") | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Biostatistics | |
| 650 | 4 | |a Epidemiology | |
| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a precancerous lesions | |
| 650 | 4 | |a risk stratification | |
| 700 | 1 | |a Epplein, Meira |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Feng-Chang |e verfasserin |4 aut | |
| 700 | 1 | |a Troester, Melissa A |e verfasserin |4 aut | |
| 700 | 1 | |a Nichols, Hazel B |e verfasserin |4 aut | |
| 700 | 1 | |a Butt, Julia |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Kaifeng |e verfasserin |4 aut | |
| 700 | 1 | |a You, Weicheng |e verfasserin |4 aut | |
| 700 | 1 | |a Olshan, Andrew |e verfasserin |4 aut | |
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