title: On the significance of fuzzification of the N and m in cancer staging.
abstract: The tumor, node, metastasis (TNM) staging system has been regarded as one of the most widely used staging systems for solid cancer. The "T" is assigned a value according to the primary tumor size, whereas the "N" and "M" are dependent on the number of regional lymph nodes and the presence of distant metastasis, respectively. The current TNM model classifies stages into five crisp classes. This is unrealistic since the drastic modification in treatment that is based on a change in one class may be based on a slight shift around the class boundary. Moreover, the system considers any tumor that has distant metastasis as stage 4, disregarding the metastatic lesion concentration and size. We had handled the problem of T staging in previous studies using fuzzy logic. In this study, we focus on the fuzzification of N and M staging for more accurate and realistic modeling which may, in turn, lead to better treatment and medical decisions.
authors.affiliation: Computer Science Department, Faculty of Computers and Information, Cairo University, Egypt.
authors.forename: Sara A
authors.initials: SA
authors.lastname: Yones
authors.affiliation: Computer Science Department, Faculty of Computers and Information, Cairo University, Egypt.
authors.forename: Ahmed S
authors.initials: AS
authors.lastname: Moussa
authors.affiliation: Computer Science Department, Faculty of Computers and Information, Cairo University, Egypt.
authors.forename: Hesham
authors.initials: H
authors.lastname: Hassan
authors.affiliation: Department of Statistics, National Cancer Institute, Cairo University, Egypt.
authors.forename: Nelly H
authors.initials: NH
authors.lastname: Alieldin
keywords: MR imaging; alpha cut; distant metastasis; fuzzy logic; regional lymph nodes
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