Scholarlink

Chapter 1 presents a general introduction to biomathematical problems that may be needed in treatment outcomes analysis and optimization in radiotherapy for hypoxic tumors with the emphasis on hypoxia imaging, cell survival models and mechanistic models of tumor control probability. We consider biomathematical modeling and optimization of cancer radiotherapy assuming that the tumor response to radiotherapy is defined by the following three broad radiobiological categories: 1) intrinsic cellular radiosensitivity, 2) cellular proliferative potential, and 3) tumor oxygen status. The intrinsic cellular radiosensitivity, cellular proliferative potential, and tumor oxygen status vary between patients and markedly contribute to inter-patient variation of the tumor control probability (TCP) and treatment outcomes.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Introduction

  • 章节

  • 发表日期：

    2025年

查看全文
  • Alexei V. Chvetsov

摘要

Chapter 1 presents a general introduction to biomathematical problems that may be needed in treatment outcomes analysis and optimization in radiotherapy for hypoxic tumors with the emphasis on hypoxia imaging, cell survival models and mechanistic models of tumor control probability. We consider biomathematical modeling and optimization of cancer radiotherapy assuming that the tumor response to radiotherapy is defined by the following three broad radiobiological categories: 1) intrinsic cellular radiosensitivity, 2) cellular proliferative potential, and 3) tumor oxygen status. The intrinsic cellular radiosensitivity, cellular proliferative potential, and tumor oxygen status vary between patients and markedly contribute to inter-patient variation of the tumor control probability (TCP) and treatment outcomes.