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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

An operator-independent quality assurance system for automatically generated structure sets.

Alexander Bookbinder^1,2, Mislav Bobić^3,4,5, Gregory C Sharp^4,5

¹Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.

Physics in Medicine and Biology

|July 24, 2024

Summary

This study introduces an automated quality assurance (QA) system for online adaptive radiotherapy, using geometry and intensity tests to detect errors in automatically generated treatment structures.

Keywords:

adaptive proton therapy adaptive radiotherapy deformable image registration structure QA structure propagation

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Area of Science:

Medical Physics
Radiotherapy Technology
Image Analysis

Background:

Online adaptive radiotherapy requires precise auto-generated structures for treatment planning.
Manual verification of these structures is time-consuming and prone to errors.
Automated quality assurance (QA) is needed to improve efficiency and accuracy.

Purpose of the Study:

To describe geometry- and intensity-based tools for QA of auto-generated radiotherapy structures.
To design an operator-independent traffic light system for identifying erroneous structure sets.
To evaluate the accuracy of the automated QA system.

Main Methods:

Deformable image registration used to propagate contours from planning CT to daily CBCT.
Development of structure-test combinations using image intensity, gradient, and geometry.
Aggregation of structure-test combinations into a traffic light QA system.
Validation on head and neck cancer patient data with simulated error scenarios.

Main Results:

The automated QA system demonstrated high accuracy in distinguishing acceptable from unacceptable treatment fractions.
294/324 acceptable fractions were correctly labeled green or yellow.
19/20 unacceptable fractions were correctly identified as yellow or red.

Conclusions:

The developed system effectively supplements manual review of radiotherapy planning structures.
Automated QA, by aggregating multiple tests, enhances the reliability of structure set verification.
This approach offers a robust solution for quality control in online adaptive radiotherapy.