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Novel knowledge-based treatment planning model for hypofractionated radiotherapy of prostate cancer patients

Published:December 06, 2019DOI:https://doi.org/10.1016/j.ejmp.2019.11.023

      Highlights

      • Model validated on 50 patients receiving 60 Gy to prostate and 44 Gy to pelvic nodes.
      • Model plans had improved sparing of organs-at-risk compared to clinical plans.
      • Model offers unbiased way to check benefit of different treatment arc configurations.
      • Model failed to produce reliable DVH predictions for prostate-only treatment plans.

      Abstract

      Purpose

      To demonstrate the strength of an innovative knowledge-based model-building method for radiotherapy planning using hypofractionated, multi-target prostate patients.

      Material and methods

      An initial RapidPlan model was trained using 48 patients who received 60 Gy to prostate (PTV60) and 44 Gy to pelvic nodes (PTV44) in 20 fractions. To improve the model's goodness-of-fit, an intermediate model was generated using the dose-volume histograms of best-spared organs-at-risk (OARs) of the initial model. Using the intermediate model and manual tweaking, all 48 cases were re-planned. The final model, trained using these re-plans, was validated on 50 additional patients. The validated final model was used to determine any planning advantage of using three arcs instead of two on 16 VMAT cases and tested on 25 additional cases to determine efficacy for single-PTV (PTV60-only) treatment planning.

      Results

      For model validation, PTV V95% of 99.9% was obtained by both clinical and knowledge-based planning. D1% was lower for model plans: by 1.23 Gy (PTV60, CI = [1.00, 1.45]), and by 2.44 Gy (PTV44, CI = [1.72, 3.16]). OAR sparing was superior for knowledge-based planning: ΔDmean = 3.70 Gy (bladder, CI = [2.83, 4.57]), and 3.22 Gy (rectum, CI = [2.48, 3.95]); ΔD2% = 1.17 Gy (bowel bag, CI = [0.64, 1.69]), and 4.78 Gy (femoral heads, CI = [3.90, 5.66]). Using three arcs instead of two, improvements in OAR sparing and PTV coverage were statistically significant, but of magnitudes < 1 Gy. The model failed at reliable DVH predictions for single PTV plans.

      Conclusions

      Our knowledge-based model delivers efficient, consistent plans with excellent PTV coverage and improved OAR sparing compared to clinical plans.

      Keywords

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