Modeling tumor growth using fractal calculus: Insights into tumor dynamics

Important concepts like fractal calculus and fractal analysis, the sum of squared residuals, and Aikaike's information criterion must be thoroughly understood in order to correctly fit cancer-related data using the proposed models. The fractal growth models employed in this work are classified in three main categories: Sig-moidal growth models (Logistic, Gompertz, and Richards models), Power Law growth model, and Exponential growth models (Exponential and Exponential-Lineal models). We fitted the data, computed the sum of squared residuals, and determined Aikaike's information criteria using Matlab and the web tool WebPlotDigitizer. In addition, the research investigates double-size cancerin the fractal temporal dimension with respect to various mathematical models.

Automated summary

This article introduces important concepts such as fractal calculus and fractal analysis, the calculation of squared residuals, and the determination of Aikaike's information criterion for fitting cancer-related data. The study also investigates the double-size cancer in the fractal temporal dimension with respect to various mathematical models.