Knowledge-based radiation therapy (KBRT) treatment planning versus planning by experts: validation of a KBRT algorithm for prostate cancer treatment planning

Abstract

BackgroundA knowledge-based radiation therapy (KBRT) treatment planning algorithm was recently developed. The purpose of this work is to investigate how plans that are generated with the objective KBRT approach compare to those that rely on the judgment of the experienced planner.MethodsThirty volumetric modulated arc therapy plans were randomly selected from a database of prostate plans that were generated by experienced planners (expert plans). The anatomical data (CT scan and delineation of organs) of these patients and the KBRT algorithm were given to a novice with no prior treatment planning experience. The inexperienced planner used the knowledge-based algorithm to predict the dose that the OARs receive based on their proximity to the treated volume. The population-based OAR constraints were changed to the predicted doses. A KBRT plan was subsequently generated. The KBRT and expert plans were compared for the achieved target coverage and OAR sparing. The target coverages were compared using the Uniformity Index (UI), while 5 dose-volume points (D10, D30, D50, D70 and D90) were used to compare the OARs (bladder and rectum) doses. Wilcoxon matched-pairs signed rank test was used to check for significant differences (p < 0.05) between both datasets.ResultsThe KBRT and expert plans achieved mean UI values of 1.10 ± 0.03 and 1.10 ± 0.04, respectively. The Wilcoxon test showed no statistically significant difference between both results. The D90, D70, D50, D30 and D10 values of the two planning strategies, and the Wilcoxon test results suggests that the KBRT plans achieved a statistically significant lower bladder dose (at D30), while the expert plans achieved a statistically significant lower rectal dose (at D10 and D30).ConclusionsThe results of this study show that the KBRT treatment planning approach is a promising method to objectively incorporate patient anatomical variations in radiotherapy treatment planning.

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

“…These results echoed the superiority of knowledge‐based solution over the conventional trial‐and‐error manual planning, in line with previous publications 17, 20, 22, 23, 24, 25, 26, 27. It suggested that knowledge‐ and geometry‐based dosimetric predictions can help avoid selecting suboptimal or conflict optimization constraints as manual limitations.…”

“…Well‐trained RapidPlan models have outperformed conventional trial and error‐based manual planning by reducing excess organs‐at‐risk (OAR) dose with greater consistency 17, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30. Should the model performance be highly dependent on the library volume31 and average quality of the training plans,17, 32 incorporating the model‐improved constituent training plans into the model (closed‐loop)25 may potentially evolve the model as a cycle of interactive improvement.…”

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

“…These observations were highly consistent with earlier prostate studies at a similar clinical site. RapidPlan‐associated dose reduction to the bladder 7 , 13 and femoral head 7 , 12 , 13 were macroscopic and significant. Similar improvement of OAR sparing and consistency has also been widely reported in other clinical sites 14 , 15 , 16 , 17 …”

“…As other potential limitations of RapidPlan, some critical parameters such as the beam energy and field geometry are not optimizable by the model, which may vary dramatically among different centers and planners. That was why custom models were more popular in the previous publications 12 , 13 , 14 , 15 , 16 , 17 . As for this study, the historical plans represent the average quality that is currently and reasonably achievable, and the improvement may be mostly attributable to the personalized objective as estimated by RapidPlan, which passes on the expertise as incorporated in the model to prospective plans.…”

“…Relative to the conventional experience‐based planning, superior or comparable results of RapidPlan have been reported in the preliminary applications to head and neck, lung, oesophageal, breast, hepatocellular, and prostate cancer patients 12 , 13 , 14 , 15 , 16 , 17 . However, consensus has been reached by these studies that both the model training and plan evaluation should be investigated further in a larger population and other cancer types in order to gain more experience and confidence before it is extensively applied clinically world‐wide.…”