Prediction of tumor growth at the microscopic level through mathematical modeling

Abstract

All articles must contain an abstract. The abstract text should be formatted using 10 point Times or Times New Roman and indented 25 mm from the left margin. Set the pre-paragraph to 0 pounds and the post-break to 22.7 pounds. Starting on the same page as the abstract. The abstract should give readers concise information about the content of the article and indicate the main results obtained and conclusions drawn. The abstract is not part of the text and should be complete in itself; no table numbers, figure numbers, references or displayed mathematical expressions should be included. It should be suitable for direct inclusion in abstracting services and should not normally exceed 200 words in a single paragraph. Since contemporary information-retrieval systems rely heavily on the content of titles and abstracts to identify relevant articles in literature searches, great care should be taken in constructing both. To gain valuable insights into the growth of microscopic tumors, which is crucial for advancing cancer research, scientists employ mathematical modeling techniques. These models help researchers to comprehend how cells interact, proliferate, and organize spatially within tumors, ultimately aiding in developing better treatment strategies, including personalized medicine. A recent study introduced a novel approach using the Occam Plausibility Algorithm (OPAL) for modeling tumor growth. OPAL is a probabilistic framework adept at generating and refining hypotheses based on complex datasets. In this study, researchers integrated two existing models, the Proliferation Invasion Model (PIM) and the Mathematical Phase Field Model (MPFM), to create a comprehensive understanding of microscopic tumor growth dynamics. In this research, we have employed a literature review methodology to explore how researchers utilize three various mathematical modeling techniques, including the Occam Plausibility Algorithm (OPAL), to delve deeply into the growth dynamics of microscopic tumors. The conclusions drawn emphasize the significant potential of mathematical modeling in advancing scientists understanding of microscopic tumor growth.