Evaluation of the stability of radiomic features of non-irradiated organs utilizing fan-beam kilovoltage computed tomography

Background

Although radiation oncologists obtain non-contrast computed tomography (CT) images in every treatment, their quality is often insufficient for radiomic analyses for monitoring response over the course of treatment. Newer linear accelerators, such as RefleXion X1, have higher-quality imaging, creating new opportunities for radiomic analysis throughout treatment.

Purpose

To best utilize the high-quality kilovoltage computed tomography (kVCT) scans of RefleXion X1 for radiomic analyses of cancerous tissue, radiomic features that remain consistent through treatment in normal organs must first be identified. Stable features can be used for normalization to calculate radiomic features in tumors, enabling monitoring of response during treatment and early adaptation if required.

Methods

The kVCT localization images acquired over the course of treatment on RefleXion X1 were analyzed for a total of five patients. A total of five patients were scanned using the RefleXion X1 throughout treatment. The imaging used standardized acquisition parameters for all treatments to minimize variation. Images were acquired using “Body/Medium Dose/Slow Couch” parameters. The regions of interest (ROIs) for each organ were automatically segmented using an auto-segmentation system Medical Mind Inc. Daily CT images and structure files were imported into an Image Biomarker Standardization Initiative (IBSI) compliant radiomic software package (LifeX) to extract radiomic features. Four non-irradiated organs were used to analyze the repeatability of normal tissues: liver, spleen, heart, and spinal cord.  The intraclass correlation coefficient (ICC) using a 2-way mixed-effects model was used to measure repeatability, while the concordance correlation coefficient (CCC) was used to measure reproducibility.

Results

Cutoff values were applied to the average ICC across patients and the average CCC across both patients and fractions. Forty features were identified with a cutoff value of 0.8, accounting for 82% of the original features. Using a cutoff value of 0.9, the subset of stable features was further reduced to 29, representing 59% of the original features.

Conclusions

A subset of several radiomic features extracted remained stable throughout treatment. Thus, radiomic analyses of cancerous tissue using RefleXion X1 imaging throughout treatment would be feasible as an ongoing assessment of response during treatment for personalized adaptive approaches.