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<p>Cancer is defined by uncontrolled cell growth and division, which can result in tumor formation &amp; spread to other parts of the body. This study employs stochastic modeling to analyze tumor dynamics, with a focus on cancer growth and metastasis. It aims to assess treatment efficacy, identify survival factors, and examine tumor growth variability. The goal is to develop customized treatment procedures and patient outcomes in oncology. This study employs stochastic modeling, Kaplan-Meier analysis, Cox proportional hazards models, and hierarchical linear modelling to analyze tumor dynamics, survival outcomes, and therapeutic implications in cancer research. The study discovered that Treatment Group 1 had significantly longer survival periods and lower event rates than Group 0, demonstrating its efficacy in extending survival. The Cox model and HLM analysis revealed that age and treatment variability play important roles, and the treatment’s influence requires more exploration. The study accurately forecasted cancer progression using stochastic processes and discovered significant differences in survival rates between treatment regimens. The findings emphasize the intricacies of cancer and the significance of personalized treatment. Integrating these ideas into therapy strategies can improve patients’ outcomes and quality of life. This study is unusual in that it employs stochastic models to generate personalized treatment methods, hence boosting cancer prognosis and specialized care.</p>

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Stochastic process tumor growth and metastasis cancer progression and survival analysis

  • 期刊论文

  • 发表日期：

    2026年2月17日

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  • S. Vetrivel,
  • S. Sasikala

摘要

Cancer is defined by uncontrolled cell growth and division, which can result in tumor formation & spread to other parts of the body. This study employs stochastic modeling to analyze tumor dynamics, with a focus on cancer growth and metastasis. It aims to assess treatment efficacy, identify survival factors, and examine tumor growth variability. The goal is to develop customized treatment procedures and patient outcomes in oncology. This study employs stochastic modeling, Kaplan-Meier analysis, Cox proportional hazards models, and hierarchical linear modelling to analyze tumor dynamics, survival outcomes, and therapeutic implications in cancer research. The study discovered that Treatment Group 1 had significantly longer survival periods and lower event rates than Group 0, demonstrating its efficacy in extending survival. The Cox model and HLM analysis revealed that age and treatment variability play important roles, and the treatment’s influence requires more exploration. The study accurately forecasted cancer progression using stochastic processes and discovered significant differences in survival rates between treatment regimens. The findings emphasize the intricacies of cancer and the significance of personalized treatment. Integrating these ideas into therapy strategies can improve patients’ outcomes and quality of life. This study is unusual in that it employs stochastic models to generate personalized treatment methods, hence boosting cancer prognosis and specialized care.