Dechang Chen1, Matthew T Hueman2, Donald E Henson1,3, Arnold M Schwartz4,5. 1. Department of Preventive Medicine & Biostatistics, The Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA. 2. Surgical Oncology, John P Murtha Cancer Center, Walter Reed National Military Medical Center, 8901 Wisconsin Ave., Bethesda, MD 20889, USA. 3. Department of Surgery, The Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA. 4. Department of Pathology, The George Washington University Medical Center, Washington, DC 20037, USA. 5. Department of Surgery, The George Washington University Medical Center, Washington, DC 20037, USA.
Abstract
AIM: We describe a new method to expand the tumor, lymph node, metastasis (TNM) staging system using a clustering algorithm. Cases of breast cancer were used for demonstration. MATERIALS & METHODS: An unsupervised ensemble-learning algorithm was used to create dendrograms. Cutting the dendrograms produced prognostic systems. RESULTS: Prognostic systems contained groups of patients with similar outcomes. The prognostic systems based on tumor size and lymph node status recapitulated the general structure of the TNM for breast cancer. The prognostic systems based on tumor size, lymph node status, histologic grade and estrogen receptor status revealed a more detailed stratification of patients when grade and estrogen receptor status were added. CONCLUSION: Prognostic systems from cutting the dendrogram have the potential to improve and expand the TNM.
AIM: We describe a new method to expand the tumor, lymph node, metastasis (TNM) staging system using a clustering algorithm. Cases of breast cancer were used for demonstration. MATERIALS & METHODS: An unsupervised ensemble-learning algorithm was used to create dendrograms. Cutting the dendrograms produced prognostic systems. RESULTS: Prognostic systems contained groups of patients with similar outcomes. The prognostic systems based on tumor size and lymph node status recapitulated the general structure of the TNM for breast cancer. The prognostic systems based on tumor size, lymph node status, histologic grade and estrogen receptor status revealed a more detailed stratification of patients when grade and estrogen receptor status were added. CONCLUSION: Prognostic systems from cutting the dendrogram have the potential to improve and expand the TNM.
Authors: Dechang Chen; Huan Wang; Li Sheng; Matthew T Hueman; Donald E Henson; Arnold M Schwartz; Jigar A Patel Journal: J Med Syst Date: 2016-05-17 Impact factor: 4.460
Authors: Matthew Hueman; Huan Wang; Zhenqiu Liu; Donald Henson; Cuong Nguyen; Dean Park; Li Sheng; Dechang Chen Journal: Thorac Cancer Date: 2021-03-13 Impact factor: 3.500
Authors: Philip M Grimley; Zhenqiu Liu; Kathleen M Darcy; Matthew T Hueman; Huan Wang; Li Sheng; Donald E Henson; Dechang Chen Journal: Acta Obstet Gynecol Scand Date: 2021-03-18 Impact factor: 4.544
Authors: Mathew T Hueman; Huan Wang; Charles Q Yang; Li Sheng; Donald E Henson; Arnold M Schwartz; Dechang Chen Journal: Cancer Med Date: 2018-07-02 Impact factor: 4.452