On the Significance of Fuzzification of the N and M in Cancer Staging
Author(s) -
Sara A. Yones,
Ahmed Moussa,
Hesham A. Hassan,
Nelly Alieldin
Publication year - 2014
Publication title -
cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.606
H-Index - 31
ISSN - 1176-9351
DOI - 10.4137/cin.s13765
Subject(s) - class (philosophy) , fuzzy logic , metastasis , stage (stratigraphy) , staging system , medicine , cancer , lymph node , primary tumor , focus (optics) , fuzzy set , computer science , oncology , radiology , artificial intelligence , physics , biology , paleontology , optics
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.
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