Evaluation of Radiomics to Predict the Accuracy of Markerless Motion Tracking of Lung Tumors: A Preliminary Study.

Kevin Nguyen,John C Roeske,Maksat Haytmyradov,Rakesh Patel,Hassan Mostafavi,Alec Block,Matthew M Harkenrider,Murat Surucu

doi:10.3389/fonc.2018.00292

Abstract

Template-based matching algorithms are currently being considered for markerless motion tracking of lung tumors. These algorithms use tumor templates derived from the planning CT scan, and track the motion of the tumor on single energy fluoroscopic images obtained at the time of treatment. In cases where bone may obstruct the view of the tumor, dual energy fluoroscopy may be used to enhance soft tissue contrast. The goal of this study is to predict which tumors will have a high degree of accuracy for markerless motion tracking based on radiomic features obtained from the planning CT scan, using peak-to-sidelobe ratio (PSR) as a surrogate of tracking accuracy. In this study, CT imaging data of 8 lung cancer patients were obtained and analyzed through the open source IBEX program to generate 2,287 radiomic features. Agglomerative hierarchical clustering was used to narrow down these features into 145 clusters comprised of the highest correlation to PSR. The features among the clusters with the least inter-correlation were then chosen to limit redundancy in the data. The results of this study demonstrated a number of radiomic features that are positively correlated to PSR. The features with the highest degree of correlation included complexity, orientation and range. This approach may be used to determine patients for whom markerless motion tracking would be beneficial.

Highlights

It is estimated that nearly 190,000 new cases of non-small cell lung cancer (NSCLC) are diagnosed in the United States each year, accounting for ∼130,000 deaths annually [1]
We evaluated the correlation between radiomic parameters and the accuracy of a template-based markerless motion tracking algorithm
A high correlation coefficient was observed between several radiomic features and the peak-to-sidelobe ratio (PSR) values for both single energy (SE) and dual energy (DE) images

Summary

Introduction

It is estimated that nearly 190,000 new cases of non-small cell lung cancer (NSCLC) are diagnosed in the United States each year, accounting for ∼130,000 deaths annually [1]. Due to the highly conformal nature of SBRT and high dose per fraction with a limited number of fractions, motion management is critical to ensure that the tumor receives the full dose of radiation and the volume of irradiated normal tissue is minimized. To achieve these endpoints, there has Radiomics to Predict Tracking Accuracy been recent interest in lung tumor tracking during treatment delivery [4,5,6,7,8,9,10,11]. Migration of fiducial markers in the lung may be significant [4,5,6]

Objectives

Methods

Results

Conclusion