A feasibility study of treatment verification using EPID cine images for hypofractionated lung radiotherapy

TL;DR

This study explores a machine learning-based method using cine EPID images to verify tumor positioning during hypofractionated lung radiotherapy, aiming for real-time treatment monitoring with high accuracy.

Contribution

It introduces a novel ANN-based classification approach for on-line treatment verification using cine EPID images, with effective dimensionality reduction and high accuracy demonstrated.

Findings

Achieved 98.0% classification accuracy

High recall rate of 97.6%

Precision rate of 99.7%

Abstract

We propose a novel approach for potential on-line treatment verification using cine EPID (Electronic Portal Imaging Device) images for hypofractionated lung radiotherapy based on a machine learning algorithm. Hypofractionated radiotherapy requires high precision. It is essential to effectively monitor the target to ensure that the tumor is within the beam aperture. We modeled the treatment verification problem as a two-class classification problem and applied an Artificial Neural Network (ANN) to classify the cine EPID images acquired during the treatment into corresponding classes with the tumor inside or outside of the beam aperture. Training samples were generated for the ANN using digitally reconstructed radiographs (DRRs) with artificially added shifts in tumor location to simulate cine EPID images with different tumor locations. Principal Component Analysis (PCA) was used to reduce the dimensionality of the training samples and cine EPID images acquired during the treatment. The proposed treatment verification algorithm was tested on five hypofractionated lung patients in a retrospective fashion. On average, our proposed algorithm achieved a 98.0% classification accuracy, a 97.6% recall rate, and a 99.7% precision rate.