Cancer staging has been regarded as a critical activity for cancer control. Cancer staging systems typically split tumors into 5 crisp categories. The classification of the tumor into one of the five stages significantly affects not only the treatment design and surgical decision for individuals but also cancer control for populations. Several cancer staging systems have been in use of which the TNM is the most widely applied. The acute distinction between the stages makes the staging unrealistic since the drastic modification in treatment based on a change of stage may be based on a slight shift around the stage boundary. Tumor size is the major component of staging systems. The TNM is no exception, where the T represents the size which is the dominant component of the staging system. In this paper we discuss the need for a fuzzy cancer staging system to capture the uncertainty and use it for more accurate treatment and medical decisions. The authors then focus on the size computation component of the cancer staging presenting a new approach depending on fuzzy volume computation. In the course, the authors demonstrate how the fuzzy volume can affect the staging system and, consequently, the medical treatment, decision, and possibly drug design. 
Tumor volume fuzzification for intelligent cancer staging