Evaluations of an adaptive planning technique incorporating dose feedback in image‐guided radiotherapy of prostate cancer

Abstract

Purpose: Online image guidance (IG) has been used to effectively correct the setup error and interfraction rigid organ motion for prostate cancer. However, planning margins are still necessary to account for uncertainties such as deformation and intra-fraction motion. The purpose of this study is to investigate the effectiveness of an adaptive planning technique incorporating offline dose feedback to manage inter-fraction motion and residuals from online correction. Methods: Repeated helical CT scans from 28 patients were included in the study. The contours of prostate and organs-at-risk (OARs) were delineated on each CT, and online IG was simulated by matching center-of-mass of prostate between treatment CTs and planning CT. A seven beam intensity modulated radiation therapy (IMRT) plan was designed for each patient on planning CT for a total of 15 fractions. Dose distribution at each fraction was evaluated based on actual contours of the target and OARs from that fraction. Cumulative dose up to each fraction was calculated by tracking each voxel based on a deformable registration algorithm. The cumulative dose was compared with the dose from initial plan. If the deviation exceeded the pre-defined threshold, such as 2% of the D 99 to the prostate, an adaptive planning technique called dose compensation was invoked, in which the cumulative dose distribution was fed back to the treatment planning system and the dose deficit was made up through boost radiation in future treatment fractions. The dose compensation was achieved by IMRT inverse planning. Two weekly compensation delivery strategies were simulated: one intended to deliver the boost dose in all future fractions (schedule A) and the other in the following week only (schedule B). The D 99 to prostate and generalized equivalent uniform dose (gEUD) to rectal wall and bladder were computed and compared with those without the dose compensation. Results: If only 2% underdose is allowed at the end of the treatment course, then 11 patients fail. If the same criteria is assessed at the end of each week (every five fractions), then 14 patients fail, with three patients failing the 1st or 2nd week but passing at the end. The average dose deficit from these 14 patients was 4.4%. They improved to 2% after the weekly compensation. Out of these 14 patients who needed dose compensation, ten passed the dose criterion after weekly dose compensation, three patients failed marginally, and one patient still failed the criterion significantly (10% deficit), representing 3.6% of the patient population. A more aggressive compensation frequency (every three fractions) could successfully reduce the dose deficit to the acceptable level for this patient. The average number of required dose compensation re-planning per patient was 0.82 (0.79) per patient for schedule A (B) delivery strategy. The doses to OARs were not significantly different from the online IG only plans without dose compensation. Conclusions: We have demonstrated the effectiveness of offline dose compensation techni...

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

“…This process is along the line of our earlier work in automated weighting factors and model parameters determination 6 , 16 , 17 . Instead of simply using prior knowledge extracted from previous clinical treatment plan(s) as a “class solution” or as upper/lower bounds to examine the results of the optimization calculation, 18 , 19 , 20 , 21 , 22 , 23 , 24 in our approach, the reference information is utilized throughout the plan selection process. During the process, the parameters used in the Eclipse optimizer are constantly updated through the comparison of current and prior knowledge characterized by reference plans.…”

Adaptive radiotherapy strategies for pelvic tumors – a systematic review of clinical implementations

“…Deformable image registration was more frequently applied in the in silico studies, mostly for online re-planning/re-optimization [59,61,67,71,86,87,94] and offline re-planning/re-optimization strategies [60,63,66,69,75,107] but also applied for online MLC/field adjustments [77,89]. To facilitate adaptations, especially for online execution, modifications of the planning system or automatic activation/selection of plans was applied in 50% of the in silico studies [58][59][60][61][67][68][69]71,72,[74][75][76][77][78][79][80][82][83][84][85][86][87][88][92][93][94]. In view of the increasing speed of optimization algorithms, limited in-room imaging quality together with manual (re-)contouring remains the biggest bottlenecks for clinical implementation of many of the ART strategies and certainly limiting online applications.…”

“…However, the majority of the studies in this review adapted the treatment for all patients with only about 17% of the clinically applied ART and 18% of the in silico studies followed a workflow allowing for selective use of ART. Examples of patient selection in the reviewed workflows were, applied alone or in combination: exerting a threshold criteria on the detected motion for initiation of modifications [20,45,46,[61][62][63][64]69,72] or shrinking the PTV margins to less than the common 90% of the population and correct for dose degradation of outliers during/at the end of treatment [63,65,66,94]. As an alternative, differentiating the size of the plan library based on a criterion for large and small cervix-uterus [41] or discriminating the adaptations based on greater and smaller response to treatment as in the gynecological GEC-ESTRO recommendations has been applied clinically [42].…”

“…Whereas online re-planning for prostate, gynecological and bladder cancer EBRT was applied in silico, the strategy was only used clinically for gynecological cancer treated with BT ( Figure 3) [58,59,61,65-67,70-72,74,78,80,82,83,88-91,94,100, 101,103]. An ART workflow presented in the in silico studies but not in the clinical workflows was offline re-plan/re-optimization, applied during or at the end of EBRT, to correct degradation of the dose distribution from previously delivered fraction [60,63,69,75,76]. In addition to one in silico implementation of online compensation [58], these were the only studies where feedback from delivered dose triggered adaptations.…”

“…In addition to one in silico implementation of online compensation [58], these were the only studies where feedback from delivered dose triggered adaptations. Contributing to differences in applied strategies and workflows could be the additional manual contouring, which was a pre-requisite in over 70% of the simulated ART workflows [22,59,60,[62][63][64][65][66][68][69][70]72,74,78,79,[81][82][83]85,86,88,89,91,93,[95][96][97][98][100][101][102][103][104][105][106][107]. To reduce the need of manual contours, the segmentation could be propagated using deformable image registration.…”