Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction

Abstract

A recent publication indicated that the patient anatomical feature (PAF) model was capable of predicting optimal objectives based on past experience. In this study, the benefits of IMRT optimization using PAF‐predicted objectives as guidance for prostate were evaluated. Three different optimization methods were compared. 1) Expert Plan: Ten prostate cases (16 plans) were planned by an expert planner using conventional trial‐and‐error approach started with institutional modified OAR and PTV constraints. Optimization was stopped at 150 iterations and that plan was saved as Expert Plan. 2) Clinical Plan: The planner would keep working on the Expert Plan till he was satisfied with the dosimetric quality and the final plan was referred to as Clinical Plan. 3) PAF Plan: A third sets of plans for the same ten patients were generated fully automatically using predicted DVHs as guidance. The optimization was based on PAF‐based predicted objectives, and was continued to 150 iterations without human interaction. DMAX and D98% for PTV, DMAX for femoral heads, DMAX, D10cc, normalD25%/normalD17%, and D40% for bladder/rectum were compared. Clinical Plans are further optimized with more iterations and adjustments, but in general provided limited dosimetric benefits over Expert Plans. PTV D98% agreed within 2.31% among Expert, Clinical, and PAF plans. Between Clinical and PAF Plans, differences for DMAX of PTV, bladder, and rectum were within 2.65%, 2.46%, and 2.20%, respectively. Bladder D10cc was higher for PAF but <1.54% in general. Bladder D25% and D40% were lower for PAF, by up to 7.71% and 6.81%, respectively. Rectum D10cc, D17%, and D40% were 2.11%, 2.72%, and 0.27% lower for PAF, respectively. DMAX for femoral heads were comparable (<35 Gy on average). Compared to Clinical Plan (Primary+Boost), the average optimization time for PAF plan was reduced by 5.2 min on average, with a maximum reduction of 7.1 min. Total numbers of MUs per plan for PAF Plans were lower than Clinical Plans, indicating better delivery efficiency. The PAF‐guided planning process is capable of generating clinical‐quality prostate IMRT plans with no human intervention. Compared to manual optimization, this automatic optimization increases planning and delivery efficiency, while maintaining plan quality.PACS numbers: 87.55.D‐, 87.55.de, 87.53.Jw

Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge‐based VMAT planning technique

“…This study had several limitations. Planning time was not explicitly recorded in this research since KBP has been extensively shown to improve planning efficiency . However, average KBP planning time was qualitatively comparable to average reference planning time in the present study.…”

Outlier identification in radiation therapy knowledge‐based planning: A study of pelvic cases

“…The predicted DVH curves from both models were used to extract the dose-volume objectives for both the bladder and rectum to guide treatment planning. 30 As shown in Fig. 6, the prediction from the outlier-added model was less favorable than that of the outlier-free-model which agreed better with the clinical plan DVH.…”

“…A prostate model without a dosimetric outlier and a prostate model with 10 dosimetric outliers were tested on a fresh prostate case that was not used to train the models. The predicted DVH curves from both models were used to extract the dose‐volume objectives for both the bladder and rectum to guide treatment planning . As shown in Fig.…”

Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches

“…79 To gain further understanding of the overall performance of KBP in prostate cancer planning, we have developed a visualization scheme to provide a summary view of nine KBP prostate studies that compared re-planned results with original clinically approved values. 20,25,27,34,35,56,57,60,63 As mentioned previously, the challenge of summarizing results across all studies lie in two aspects: (a) the results are based on different sample points of the DVH curve and measure changes along different directions (e.g., one study may use D35 while another use V65); (b) some of the studies report only the differences in DVH point metrics (e.g., D35 is reduced by 1.5) without providing the original clinically approved values. The first issue makes it difficult to quantitatively compare results from different studies even though many DVH point metrics assess performance in similar areas of the DVH curve.…”

“…And some studies suggest that this is true especially for models learned from experienced planners' datasets and applied to cases generated by either inexperienced planners or planners who are not experienced with a planning system. Some studies 15,20,22,28,31 have also compared the time and efficiency of KBP methods to the current manual planning process. In all cases, the KBP methods were faster and the improvement is more significant for more complex cases.…”