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Optimization in treatment planning of high dose-rate brachytherapy - Review and analysis of mathematical models
被引:18
|作者:
Moren, Bjorn
[1
]
Larsson, Torbjorn
[1
]
Tedgren, Asa Carlsson
[2
,3
,4
]
机构:
[1] Linkoping Univ, Dept Math, Linkoping, Sweden
[2] Linkoping Univ, Radiat Phys, Dept Hlth Med & Caring Sci, Linkoping, Sweden
[3] Karolinska Univ Hosp, Med Radiat Phys & Nucl Med, Stockholm, Sweden
[4] Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden
基金:
瑞典研究理事会;
关键词:
catheter placement;
dose planning;
dwell time optimization;
intensity modulated brachytherapy;
high dose‐
rate brachytherapy;
mathematical optimization;
radiobiology;
INTENSITY-MODULATED BRACHYTHERAPY;
SOCIETY CONSENSUS GUIDELINES;
LINEAR-PROGRAMMING APPROACH;
MULTIPLE LOCAL MINIMA;
HDR-BRACHYTHERAPY;
PROSTATE BRACHYTHERAPY;
AMERICAN BRACHYTHERAPY;
CERVICAL-CANCER;
COLD SPOTS;
REAL-TIME;
D O I:
10.1002/mp.14762
中图分类号:
R8 [特种医学];
R445 [影像诊断学];
学科分类号:
1002 ;
100207 ;
1009 ;
摘要:
Treatment planning in high dose-rate brachytherapy has traditionally been conducted with manual forward planning, but inverse planning is today increasingly used in clinical practice. There is a large variety of proposed optimization models and algorithms to model and solve the treatment planning problem. Two major parts of inverse treatment planning for which mathematical optimization can be used are the decisions about catheter placement and dwell time distributions. Both these problems as well as integrated approaches are included in this review. The proposed models include linear penalty models, dose-volume models, mean-tail dose models, quadratic penalty models, radiobiological models, and multiobjective models. The aim of this survey is twofold: (i) to give a broad overview over mathematical optimization models used for treatment planning of brachytherapy and (ii) to provide mathematical analyses and comparisons between models. New technologies for brachytherapy treatments and methods for treatment planning are also discussed. Of particular interest for future research is a thorough comparison between optimization models and algorithms on the same dataset, and clinical validation of proposed optimization approaches with respect to patient outcome.
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页码:2057 / 2082
页数:26
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