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Knowledge-based planning (KBP) models improve prostate cancer radiation therapy by predicting doses to organs at risk. Implementing KBP reduced suboptimal plans and improved organ sparing in clinical practice.

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Area of Science:

  • Radiation Oncology
  • Medical Physics

Background:

  • Radiation therapy planning requires extensive dosimetric knowledge, which is challenging to acquire.
  • Knowledge-based planning (KBP) offers a methodology to standardize and enhance treatment planning.
  • Prostate cancer treatment planning is complex due to proximity of organs at risk.

Purpose of the Study:

  • To assess the impact of empirical predictive models based on KBP for prostate cancer treatment.
  • To detect suboptimal treatment plans and homogenize planning practices.
  • To evaluate the dosimetric effects of implementing KBP models in routine clinical practice.

Main Methods:

  • Analysis of 25 VMAT prostate treatment plans using KBP to correlate dose indicators with patient geometry.
  • Linking dose indicators to geometric characteristics like PTV-OAR distance and OAR volume.
  • Prospective implementation of KBP in a cohort of 25 patients, compared to 41 prior plans.

Main Results:

  • Strong geometric predictive factors for organs at risk identified (R² > 0.8).
  • Implementation of KBP led to significant dose reductions in evaluated organs at risk, particularly for the peritoneal cavity.
  • Reduced the percentage of plans exceeding clinical recommendations for organs at risk from 19% to 8%.

Conclusions:

  • KBP methodology provides a robust, personalized predictive model for organ at risk dose estimation in prostate cancer.
  • Model implementation improved organ sparing and established a foundation for harmonizing dosimetric practices.
  • KBP aids in identifying suboptimal plans and advancing dosimetric knowledge.