Mathematical computation of the tumor growth

• Published in: Network modeling and analysis in health informatics and bioinformatics (Wien) Vol. 11; no. 1; p. 27
• Main Authors: Namdev, Nishant, Shende, Pradeep, Sinha, Arvind Kumar
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.12.2022; Springer Nature B.V
• Subjects: Applications of Graph Theory and Complex Networks; Applied mathematics; Bioinformatics; Cells; Computational Biology/Bioinformatics; Computer Science; Data analysis; Differential calculus; Health Informatics; Lipschitz condition; Mathematical functions; Mathematical models; Medical procedures; Metastases; Metastasis; Original Article; Per capita; Population growth; Tumor cells; Tumors; Dynamics of tumor cells; Nonlinear dynamics; Tumor; Rough sets
• ISSN: 2192-6662; 2192-6670
• DOI: 10.1007/s13721-022-00368-0

The tumor is a vital concern in the medical system, and it is one of the causes of losing life and puts a big load on medical. Describing the tumor staging by analysis is a crucial subject for medical procedures. Advanced exposure to hidden metastasis indistinguishable from modern techniques would significantly impact the most appropriate concern and long-term survival. Researchers have been working to explain a new medical practice model for tumors. The tumor cells' population growth is dubious due to their strange response.  Metastasis dispersal  is how some cells from the tumors move and shape different tumors. In the paper, the tumor cell's population dynamics are organized by the metastasis method using the concept of the calculus, mean value theorem, and Lipschitz condition for the stability of the model. The paper forms a relationship between applied computational mathematics and the biological system.