Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis
Abstract Objective To construct a machine learning (ML) model to predict the progression of chronic atrophic gastritis (CAG) to gastric cancer (GC), given its precancerous significance. Methods Using medical records from the Affiliated Hospital of Qingdao University, common laboratory indicators wer...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
|
| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-02453-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849311929796919296 |
|---|---|
| author | Jia-Yi Zhang Ding Li Guo-Jie Hu |
| author_facet | Jia-Yi Zhang Ding Li Guo-Jie Hu |
| author_sort | Jia-Yi Zhang |
| collection | DOAJ |
| description | Abstract Objective To construct a machine learning (ML) model to predict the progression of chronic atrophic gastritis (CAG) to gastric cancer (GC), given its precancerous significance. Methods Using medical records from the Affiliated Hospital of Qingdao University, common laboratory indicators were extracted. LASSO regression identified 10 core risk factors, which were further analyzed using binary logistic regression to develop a nomogram model in R. The model’s performance was evaluated using receiver operating characteristic (ROC) curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Results The model showed excellent performance, with a C-index of 0.887. The key factors included sex, coagulation, blood cell indexes, and blood lipid levels. The ROC areas were 0.892 (quantitative) and 0.853 (qualitative), confirming model reliability. Conclusion A new nomogram model for assessing GC risk in CAG patients was successfully developed. However, due to data collection and time limitations, future studies should expand the sample size, perfect the validation process, and optimize the model to achieve more accurate risk prediction. |
| format | Article |
| id | doaj-art-cb9e61da35fd4f6fbf9d3af357a35b77 |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-cb9e61da35fd4f6fbf9d3af357a35b772025-08-20T03:53:13ZengSpringerDiscover Oncology2730-60112025-05-0116111110.1007/s12672-025-02453-yDevelopment of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritisJia-Yi Zhang0Ding Li1Guo-Jie Hu2Institute of Integrated Medicine, Qingdao Medical College of Qingdao University, Qingdao UniversityDepartment of Traditional Chinese Medicine, The Affiliated Hospital of Qingdao UniversityDepartment of Traditional Chinese Medicine, The Affiliated Hospital of Qingdao UniversityAbstract Objective To construct a machine learning (ML) model to predict the progression of chronic atrophic gastritis (CAG) to gastric cancer (GC), given its precancerous significance. Methods Using medical records from the Affiliated Hospital of Qingdao University, common laboratory indicators were extracted. LASSO regression identified 10 core risk factors, which were further analyzed using binary logistic regression to develop a nomogram model in R. The model’s performance was evaluated using receiver operating characteristic (ROC) curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Results The model showed excellent performance, with a C-index of 0.887. The key factors included sex, coagulation, blood cell indexes, and blood lipid levels. The ROC areas were 0.892 (quantitative) and 0.853 (qualitative), confirming model reliability. Conclusion A new nomogram model for assessing GC risk in CAG patients was successfully developed. However, due to data collection and time limitations, future studies should expand the sample size, perfect the validation process, and optimize the model to achieve more accurate risk prediction.https://doi.org/10.1007/s12672-025-02453-yChronic atrophic gastritisGastric cancerMachine learningPredictive modelNomogram |
| spellingShingle | Jia-Yi Zhang Ding Li Guo-Jie Hu Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis Discover Oncology Chronic atrophic gastritis Gastric cancer Machine learning Predictive model Nomogram |
| title | Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis |
| title_full | Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis |
| title_fullStr | Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis |
| title_full_unstemmed | Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis |
| title_short | Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis |
| title_sort | development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis |
| topic | Chronic atrophic gastritis Gastric cancer Machine learning Predictive model Nomogram |
| url | https://doi.org/10.1007/s12672-025-02453-y |
| work_keys_str_mv | AT jiayizhang developmentofanomogramforpredictingtheriskofcarcinomainchronicatrophicgastritis AT dingli developmentofanomogramforpredictingtheriskofcarcinomainchronicatrophicgastritis AT guojiehu developmentofanomogramforpredictingtheriskofcarcinomainchronicatrophicgastritis |