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Related Experiment Video

Updated: Dec 6, 2025

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

Clinical Implementation of Knowledge-Based Automatic Plan Optimization for Helical Tomotherapy.

Roberta Castriconi¹, Giovanni Mauro Cattaneo¹, Paola Mangili¹

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

Practical Radiation Oncology

|October 11, 2020

Summary

Knowledge-based automatic planning for helical TomoTherapy (HTT) was successfully implemented for high-risk prostate cancer. This automated approach generally produced plans equivalent or superior to traditional methods, improving target coverage and organ sparing.

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Area of Science:

Radiation Oncology
Medical Physics
Computational Biology

Background:

Helical TomoTherapy (HTT) is a widely used radiation therapy technique.
Automatic planning aims to improve efficiency and consistency in treatment delivery.
Knowledge-based (KB) planning leverages past treatment data to guide new plan creation.

Purpose of the Study:

To implement and validate a knowledge-based (KB) automatic planning workflow for helical TomoTherapy (HTT).
To assess the efficacy of KB automatic planning for high-risk prostate cancer patients, including pelvic node irradiation.

Main Methods:

A KB model was trained using 102 HTT clinical plans with the RapidPlan tool.
KB-based templates were automatically converted and applied to the HTT treatment planning system.
Internal and external validation cohorts (20 and 28 patients, respectively) were used to compare KB automatic plans (KB-TP) against original plans (TP).
Comparison included dose-volume parameters for planning target volumes (PTVs) and organs at risk (OARs), alongside blinded clinical evaluation.

Main Results:

KB-TP plans were generally equivalent or superior to TP plans across both validation cohorts.
Significant improvements were observed in PTV and rectum-PTV overlap dosimetry.
OAR sparing, particularly for the bladder and bowel, showed slight but noticeable improvement with KB-TP.
Clinical evaluation indicated KB-TP was preferred in 60% of cases and inferior in 10% compared to TP (P < .05).

Conclusions:

A fully knowledge-based automatic planning workflow for HTT has been successfully implemented.
This approach is effective for optimizing treatment plans in high-risk prostate cancer cases.
KB automatic planning offers a promising method for enhancing radiation therapy planning efficiency and quality.