Creating prognostic systems for cancer patients: A demonstration using breast cancer
Abstract Integrating additional prognostic factors into the tumor, lymph node, metastasis staging system improves the relative stratification of cancer patients and enhances the accuracy in planning their treatment options and predicting clinical outcomes. We describe a novel approach to build progn...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-08-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.1629 |
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| _version_ | 1846114412295880704 |
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| author | Mathew T. Hueman Huan Wang Charles Q. Yang Li Sheng Donald E. Henson Arnold M. Schwartz Dechang Chen |
| author_facet | Mathew T. Hueman Huan Wang Charles Q. Yang Li Sheng Donald E. Henson Arnold M. Schwartz Dechang Chen |
| author_sort | Mathew T. Hueman |
| collection | DOAJ |
| description | Abstract Integrating additional prognostic factors into the tumor, lymph node, metastasis staging system improves the relative stratification of cancer patients and enhances the accuracy in planning their treatment options and predicting clinical outcomes. We describe a novel approach to build prognostic systems for cancer patients that can admit any number of prognostic factors. In the approach, an unsupervised learning algorithm was used to create dendrograms and the C‐index was used to cut dendrograms to generate prognostic groups. Breast cancer data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute were used for demonstration. Two relative prognostic systems were created for breast cancer. One system (7 prognostic groups with C‐index = 0.7295) was based on tumor size, regional lymph nodes, and no distant metastasis. The other system (7 prognostic groups with C‐index = 0.7458) was based on tumor size, regional lymph nodes, no distant metastasis, grade, estrogen receptor, progesterone receptor, and age. The dendrograms showed a relationship between survival and prognostic factors. The proposed approach is able to create prognostic systems that have a good accuracy in survival prediction and provide a manageable number of prognostic groups. The prognostic systems have the potential to permit a thorough database analysis of all information relevant to decision‐making in patient management and prognosis. |
| format | Article |
| id | doaj-art-f2469ca7bbb0421a9a01cd9adb59ef91 |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2018-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-f2469ca7bbb0421a9a01cd9adb59ef912024-12-20T13:15:44ZengWileyCancer Medicine2045-76342018-08-01783611362110.1002/cam4.1629Creating prognostic systems for cancer patients: A demonstration using breast cancerMathew T. Hueman0Huan Wang1Charles Q. Yang2Li Sheng3Donald E. Henson4Arnold M. Schwartz5Dechang Chen6Department of Surgical Oncology John P. Murtha Cancer Center Walter Reed National Military Medical Center Bethesda MD USADepartment of Biostatistics The George Washington University Washington DC USADepartment of Surgery Walter Reed National Military Medical Center Bethesda MD USADepartment of Mathematics Drexel University Philadelphia PA USADepartment of Preventive Medicine & Biostatistics F. Edward Hébert School of Medicine Uniformed Services University of the Health Sciences Bethesda MD USADepartment of Pathology School of Medicine and Health Sciences The George Washington University Washington DC USADepartment of Preventive Medicine & Biostatistics F. Edward Hébert School of Medicine Uniformed Services University of the Health Sciences Bethesda MD USAAbstract Integrating additional prognostic factors into the tumor, lymph node, metastasis staging system improves the relative stratification of cancer patients and enhances the accuracy in planning their treatment options and predicting clinical outcomes. We describe a novel approach to build prognostic systems for cancer patients that can admit any number of prognostic factors. In the approach, an unsupervised learning algorithm was used to create dendrograms and the C‐index was used to cut dendrograms to generate prognostic groups. Breast cancer data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute were used for demonstration. Two relative prognostic systems were created for breast cancer. One system (7 prognostic groups with C‐index = 0.7295) was based on tumor size, regional lymph nodes, and no distant metastasis. The other system (7 prognostic groups with C‐index = 0.7458) was based on tumor size, regional lymph nodes, no distant metastasis, grade, estrogen receptor, progesterone receptor, and age. The dendrograms showed a relationship between survival and prognostic factors. The proposed approach is able to create prognostic systems that have a good accuracy in survival prediction and provide a manageable number of prognostic groups. The prognostic systems have the potential to permit a thorough database analysis of all information relevant to decision‐making in patient management and prognosis.https://doi.org/10.1002/cam4.1629breast cancercancer stagingC‐indexdendrogrammachine learningsurvival |
| spellingShingle | Mathew T. Hueman Huan Wang Charles Q. Yang Li Sheng Donald E. Henson Arnold M. Schwartz Dechang Chen Creating prognostic systems for cancer patients: A demonstration using breast cancer Cancer Medicine breast cancer cancer staging C‐index dendrogram machine learning survival |
| title | Creating prognostic systems for cancer patients: A demonstration using breast cancer |
| title_full | Creating prognostic systems for cancer patients: A demonstration using breast cancer |
| title_fullStr | Creating prognostic systems for cancer patients: A demonstration using breast cancer |
| title_full_unstemmed | Creating prognostic systems for cancer patients: A demonstration using breast cancer |
| title_short | Creating prognostic systems for cancer patients: A demonstration using breast cancer |
| title_sort | creating prognostic systems for cancer patients a demonstration using breast cancer |
| topic | breast cancer cancer staging C‐index dendrogram machine learning survival |
| url | https://doi.org/10.1002/cam4.1629 |
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