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Optimization problems in radiotherapy for hypoxic tumors

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimization problems in radiotherapy for hypoxic tumors/ by Alexei V. Chvetsov.
作者:	Chvetsov, Alexei V.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xiv, 139 p. :ill., digital ;24 cm.
內容註:	Introduction -- Tumor Models for Optimization of Radiotherapy Response -- Dose-Volume Effects in Tumor Control Probability -- Equivalent Uniform Aerobic Dose -- Dose Nonuniformity Effectiveness in Hypoxic Tumors -- Stability of Inverse Planning with Different Objective Functions.
Contained By:	Springer Nature eBook
標題:	Cancer - Radiotherapy -
電子資源:	https://doi.org/10.1007/978-981-96-7001-7
ISBN:	9789819670017

Optimization problems in radiotherapy for hypoxic tumors
Chvetsov, Alexei V.

Optimization problems in radiotherapy for hypoxic tumors[electronic resource] /by Alexei V. Chvetsov. - Singapore :Springer Nature Singapore :2025. - xiv, 139 p. :ill., digital ;24 cm. - Biological and medical physics, biomedical engineering,2197-5647. - Biological and medical physics, biomedical engineering..

Introduction -- Tumor Models for Optimization of Radiotherapy Response -- Dose-Volume Effects in Tumor Control Probability -- Equivalent Uniform Aerobic Dose -- Dose Nonuniformity Effectiveness in Hypoxic Tumors -- Stability of Inverse Planning with Different Objective Functions.

This book highlights the mathematical aspects of treatment outcomes analysis and dose optimization in radiotherapy for heterogeneous hypoxic tumors. Hypoxia is a major factor of cancer resistance to radiotherapy treatment and is present in most tumors encountered in humans. The author tried to present a systematic consideration of radiotherapy for hypoxic tumors, but the emphasis was put on mathematical content of the problems. The book contains new approaches to the concepts of tumor control probability, equivalent uniform dose and radiotherapy dose optimization for hypoxic tumors developed by the author. Significant attention in this book is paid to comparison of models with measured and clinical data; therefore, the reduction of model parameters to overcome overfitting (model parsimony) was followed as much as possible.

ISBN: 9789819670017

Standard No.: 10.1007/978-981-96-7001-7doiSubjects--Topical Terms:

3789883
Cancer
--Radiotherapy

LC Class. No.: RC271.R3 / C48 2025

Dewey Class. No.: 616.9940642

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