Exploring trade-offs between target coverage, healthy tissue sparing, and the placement of catheters in HDR brachytherapy for prostate cancer using a novel multi-objective model-based mixed-integer evolutionary algorithm
| Autor: | Sadowski, Krzysztof L., van der Meer, Marjolein C., Luong, Ngoc Hoang, Alderliesten, Tanja, Thierens, Dirk, van der Laarse, Rob, Niatsetski, Yuri, Bel, Arjan, Bosman, Peter A. N. |
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| Přispěvatelé: | Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands, CCA - Cancer Treatment and Quality of Life, Graduate School, Radiotherapy, Cancer Center Amsterdam |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Optimization problem Computer science medicine.medical_treatment Brachytherapy Evolutionary algorithm 02 engineering and technology Radiation Genetic algorithms medicine.disease Multi-objective optimization 030218 nuclear medicine & medical imaging Empirical study Radiation therapy 03 medical and health sciences Prostate cancer Catheter 0302 clinical medicine Genetic algorithm 0202 electrical engineering electronic engineering information engineering medicine Medicine 020201 artificial intelligence & image processing Radiation treatment planning |
| Zdroj: | GECCO GECCO '17 : Genetic and Evolutionary Computation Conference, 1224-1231 STARTPAGE=1224;ENDPAGE=1231;TITLE=GECCO '17 : Genetic and Evolutionary Computation Conference |
| Popis: | Brachytherapy is a form of radiotherapy whereby a radiation source is guided near tumors, using devices such as catheter implants. In the present clinical workflow, catheters are first placed inside or close to the tumor based on clinical expertise. Subsequently, software is used to design a plan for the delivery of radiation. Treatment planning is essentially a multi-objective optimization problem, where conflicting objectives represent radiation delivered to tumor cells and healthy cells. However, current clinical software collapses this information into a single-objective, constrained optimization problem. Moreover, catheter positioning is typically not included. As a consequence, it is hard to obtain insight into the true nature of the trade-offs between key planning objectives and the placement of catheters. Such insights are however crucial in understanding how better treatment plans may be constructed. To obtain such insights, we interface with real-world clinical software and derive potential catheter positions for real-world patients. Selecting and configuring catheters requires mixed-integer optimization. For this reason, we extend the recently-proposed Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT) to tackle multi-objective optimization problems. Our results indicate that clinically acceptable plans of high quality may be achievable with less catheters than typically used in current clinical practice. |
| Databáze: | OpenAIRE |
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