Related Experiment Video
Updated: Feb 5, 2026

miRNA Expression Analyses in Prostate Cancer Clinical Tissues
Published on: September 8, 2015
A multi-criteria optimization approach for HDR prostate brachytherapy: II. Benchmark against clinical plans
Songye Cui1,2, Philippe Després1,2, Luc Beaulieu1,2
1Department of Physics, Engineering Physics and Optics and Cancer Research Center, Université Laval, Quebec City, QC, G1V 0A6, Canada.
None:
The current iterative approach to inverse planning of high dose rate treatment planning can be time consuming. The purpose of this two-part study is to streamline the planning process while maintaining plan quality. In this second part, a multi-criteria optimization (MCO) planning algorithm is proposed and benchmarked against a standard planning algorithm. With a set of previously established regression models, a patient-specific valid solution space on the Pareto surface was predicted based on the anchor plans results. Alternative plans generated alongside the partial Pareto front were presented to the planner, and one plan was selected as the MCO plan. The dosimetric parameters results as well as the planning time were compared between the MCO plans and the physician-approved standard plans for 236 prostate cases. Results show that the urethra is better spared with MCO planning than with standard planning (a lower mean urethral D 10 value of 2.25%). The overall MCO plan quality also outperforms the standard plan quality, since MCO planning is able to increase the frequency of clinically acceptable plans meeting all of RTOG criteria simultaneously without any human intervention (from 83.05% to 97.46%). Finally, the average MCO planning time is [Formula: see text] without any interventions of treatment planners. The presented MCO planning algorithm constitutes a robust and automated way to improve treatment quality in brachytherapy.
Related Concept Videos
Criteria for Causality: Bradford Hill Criteria - II
Criteria for Causality: Bradford Hill Criteria - I
Sampling Plans
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Planning Nursing Care I
Planning Nursing Care II
Optimal Foraging

