Knowledge-based IMRT treatment planning for prostate cancer

Abstract

The goal of intensity-modulated radiation therapy (IMRT) treatment plan optimization is to produce a cumulative dose distribution that satisfies both the dose prescription and the normal tissue dose constraints. The typical manual treatment planning process is iterative, time consuming, and highly dependent on the skill and experience of the planner. We have addressed this problem by developing a knowledgebased approach that utilizes a database of prior plans to leverage the planning expertise of physicians and physicists at our institution. We developed a case-similarity algorithm that uses mutual information to identify a similar matched case for a given query case, and various treatment parameters from the matched case are then adapted to derive new treatment plans that are patient specific.We used 10 randomly selected cases matched against a knowledge base of 100 cases to demonstrate that new, clinically acceptable IMRT treatment plans can be developed. This approach substantially reduced planning time by skipping all but the last few iterations of the optimization process. Additionally, we established a simple metric based on the areas under the curve (AUC) of the dose volume histogram (DVH), specifically for the planning target volume (PTV), rectum, and bladder. This plan quality metric was used to successfully rank order the plan quality of a collection of knowledgebased plans. Further, we used 100 pre-optimized plans (20 query x 5 matches) to show that the average normalized MI score can be used as a surrogate of overall plan quality.Plans of lower pre-optimized plan quality tended to improve substantially after optimization, though its final plan quality did not improve to the same level as a plan that has a higher pre-optimized plan quality to begin with. Optimization usually improved PTV coverage slightly while providing substantial dose sparing for both v bladder and rectum of 12.4% and 9.1% respectively. Lastly, we developed new treatment plans for cases selected from an outside institution matched against our sitespecific database. The knowledge-based plans are very comparable to the original manual plan, providing adequate PTV coverage as well as substantial improvement in dose sparing to the rectum and bladder.In conclusion, we found that a site-specific database of prior plans can be effectively used to design new treatment plans for our own institution as well as outside cases. Specifically, knowledge-based plans can provide clinically acceptable planning target volume coverage and clinically acceptable dose sparing to the rectum and bladder. This approach has been demonstrated to improve the efficiency of the treatment planning process, and may potentially improve the quality of patient care by enabling more consistent treatment planning across institutions.vi

A dosimetric evaluation of knowledge‐based VMAT planning with simultaneous integrated boosting for rectal cancer patients

“…1) and OAR (Fig. 2) metrics, the reduced degree of data dispersion in the RapidPlan group demonstrated its superior quality consistency and less variety than the original manual plans, which agreed with the RapidPlan rationale of alleviating the subjective dependency of individual planners 1 , 2 , 3 , 4 , 5 , 6 , 7 …”

“…As reported by many inhouse approaches, knowledge‐based radiotherapy (KBRT) treatment planning is deemed to reduce the interplanner varieties of plan quality 1 , 2 , 3 , 4 , 5 , 6 , 7 and expedite the planning process 8 , 9 , 10 , 11 . As a commercial KBRT optimization engine, RapidPlan (Varian Medical Systems, Palo Alto, CA) uses a pool of selected plans with consistent high quality as historical knowledge to train a DVH estimation model which predicts achievable DVH ranges and acceptable trade‐offs during the semi‐automatic plan optimization for the prospective patient.…”

An interactive plan and model evolution method for knowledge‐based pelvic VMAT planning

“…Knowledge‐based radiotherapy treatment planning is deemed to reduce the inter‐planner varieties of plan quality1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and expedite the planning process 14, 15, 16, 1718, 19 and displayed good compatibility across patient orientations, treatment techniques, and systems 20, 21…”

Application programming in C# environment with recorded user software interactions and its application in autopilot of VMAT/IMRT treatment planning

“…Here, some predefined geometric criteria are employed for the selection of reference plan. Similar to the work done by Chanyavanich et al, (12) the images of current case are overlaid with a candidate reference plan from a library of previously treated patients. The candidate reference plans are first filtered according to anatomical site, physical target volume(s), corresponding structure names, and prescribed dose.…”