Using Machine Learning to Expand the Ann Arbor Staging System for Hodgkin and Non-Hodgkin Lymphoma
The Ann Arbor system is disadvantaged in utilizing information from additional prognostic factors. In this study, we applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) to create a prognostic system for lymphoma that integrates additional prognostic factors. Hodgkin and non-Hodgkin lym...
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MDPI AG
2023-07-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/3/3/35 |
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| author | Huan Wang Zhenqiu Liu Julie Yang Li Sheng Dechang Chen |
| author_facet | Huan Wang Zhenqiu Liu Julie Yang Li Sheng Dechang Chen |
| author_sort | Huan Wang |
| collection | DOAJ |
| description | The Ann Arbor system is disadvantaged in utilizing information from additional prognostic factors. In this study, we applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) to create a prognostic system for lymphoma that integrates additional prognostic factors. Hodgkin and non-Hodgkin lymphoma survival data were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and divided into the training set (131,725 cases) and the validation set (15,683 cases). Five prognostic factors were studied: Ann Arbor stage, type, site, age, and sex. EACCD was applied to the training set to produce a prognostic system, called an EACCD system, for convenience. The EACCD system stratified patients into eight prognostic groups with well-separated survival curves. These eight prognostic groups had significantly higher accuracies in survival prediction than the 24 Ann Arbor substages. A higher-risk group in the EACCD system roughly corresponds to a higher Ann Arbor substage. The proposed system shows a good performance in risk stratification and survival prediction on both the training and the validation sets. The EACCD system expands the traditional Ann Arbor staging system by leveraging additional prognostic information and is expected to advance treatment management for lymphoma patients. |
| first_indexed | 2024-03-10T23:00:21Z |
| format | Article |
| id | doaj.art-658c25aab3094232aaec5ce271719cf2 |
| institution | Directory Open Access Journal |
| issn | 2673-7426 |
| language | English |
| last_indexed | 2024-03-10T23:00:21Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-658c25aab3094232aaec5ce271719cf22023-11-19T09:43:30ZengMDPI AGBioMedInformatics2673-74262023-07-013351452510.3390/biomedinformatics3030035Using Machine Learning to Expand the Ann Arbor Staging System for Hodgkin and Non-Hodgkin LymphomaHuan Wang0Zhenqiu Liu1Julie Yang2Li Sheng3Dechang Chen4Division of Biometrics IX, OB/OTS/CDER, FDA, Silver Spring, MD 10903, USADepartment of Public Health Sciences, Penn State Cancer Institute, Hershey, PA 17033, USASchool of Public Health, University of Maryland, College Park, MD 20742, USADepartment of Mathematics, Drexel University, Philadelphia, PA 19104, USADepartment of Preventive Medicine & Biostatistics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USAThe Ann Arbor system is disadvantaged in utilizing information from additional prognostic factors. In this study, we applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) to create a prognostic system for lymphoma that integrates additional prognostic factors. Hodgkin and non-Hodgkin lymphoma survival data were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and divided into the training set (131,725 cases) and the validation set (15,683 cases). Five prognostic factors were studied: Ann Arbor stage, type, site, age, and sex. EACCD was applied to the training set to produce a prognostic system, called an EACCD system, for convenience. The EACCD system stratified patients into eight prognostic groups with well-separated survival curves. These eight prognostic groups had significantly higher accuracies in survival prediction than the 24 Ann Arbor substages. A higher-risk group in the EACCD system roughly corresponds to a higher Ann Arbor substage. The proposed system shows a good performance in risk stratification and survival prediction on both the training and the validation sets. The EACCD system expands the traditional Ann Arbor staging system by leveraging additional prognostic information and is expected to advance treatment management for lymphoma patients.https://www.mdpi.com/2673-7426/3/3/35lymphomacancer stagingC-indexdendrogrammachine learning |
| spellingShingle | Huan Wang Zhenqiu Liu Julie Yang Li Sheng Dechang Chen Using Machine Learning to Expand the Ann Arbor Staging System for Hodgkin and Non-Hodgkin Lymphoma BioMedInformatics lymphoma cancer staging C-index dendrogram machine learning |
| title | Using Machine Learning to Expand the Ann Arbor Staging System for Hodgkin and Non-Hodgkin Lymphoma |
| title_full | Using Machine Learning to Expand the Ann Arbor Staging System for Hodgkin and Non-Hodgkin Lymphoma |
| title_fullStr | Using Machine Learning to Expand the Ann Arbor Staging System for Hodgkin and Non-Hodgkin Lymphoma |
| title_full_unstemmed | Using Machine Learning to Expand the Ann Arbor Staging System for Hodgkin and Non-Hodgkin Lymphoma |
| title_short | Using Machine Learning to Expand the Ann Arbor Staging System for Hodgkin and Non-Hodgkin Lymphoma |
| title_sort | using machine learning to expand the ann arbor staging system for hodgkin and non hodgkin lymphoma |
| topic | lymphoma cancer staging C-index dendrogram machine learning |
| url | https://www.mdpi.com/2673-7426/3/3/35 |
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