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Comprehensive Intra-Institution stepping validation of knowledge-based models for automatic plan optimization.

R Castriconi1, C Fiorino1, S Broggi1

  • 1Medical Physics, San Raffaele Scientific Institute, Milano, Italy.

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|December 15, 2018
PubMed
Summary
This summary is machine-generated.

This study validated knowledge-based (KB) models for radiation therapy planning, showing automated plans matched or exceeded clinical quality. Planner interaction further enhanced these optimized plans for improved patient outcomes.

Keywords:
Automatic planningKnowledge-basedMachine learningProstate radiotherapy

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Computational Biology

Background:

  • Knowledge-based (KB) models offer potential for optimizing radiation therapy planning.
  • Validation of these models is crucial for clinical implementation, especially in complex cases like post-prostatectomy patients.

Purpose of the Study:

  • To develop and apply a novel stepping approach for validating KB models in radiation therapy planning.
  • To assess the performance of KB-generated plans against clinical plans for pelvic node and prostate/seminal vesicle irradiation.

Main Methods:

  • A KB model (RapidPlan) was developed using VMAT plans from 52 patients.
  • A three-step validation (closed-loop, open-loop, wide-loop) compared KB-generated plans (with and without planner interaction) to clinical plans using dose-volume parameters and gEUD.

Main Results:

  • KB-generated plans were generally equivalent or superior to clinical plans.
  • Planner interaction improved PTV coverage and OAR sparing.
  • Significant improvements were observed in OAR dose metrics (e.g., V50Gy, Dmean) and gEUD reduction for rectum, bladder, and bowel.

Conclusions:

  • The stepping validation approach confirmed KB models can achieve comparable or better results than manual planning.
  • Planner interaction enhances the performance of KB-optimized radiation therapy plans.