Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data.

Abdalla Ibrahim,Roland Hustinx,Matthias P Fabritius,Osman Öcal,Felix M Mottaghy,Turkey Refaee,Razvan L Miclea,Michael Ingrisch,Max Seidensticker,Philippe Lambin,Jens Ricke,Henry C Woodruff,Sergey Primakov,Yousif Widaatalla

doi:10.3390/cancers13184638

Abstract

Simple SummaryRadiomics has been reported to have potential for correlating with clinical outcomes. However, handcrafted radiomic features (HRFs)—the quantitative features extracted from medical images—are limited by their sensitivity to variations in scanning parameters. Furthermore, radiomics analyses require big data with good quality to achieve desirable performances. In this study, we investigated the reproducibility of HRFs between scans acquired with the same scanning parameters except for the imaging phase (arterial and portal venous phases) to assess the possibilities of merging scans from different phases or replacing missing scans from a phase with other phases to increase data entries. Additionally, we assessed the potential of ComBat harmonization to remove batch effects attributed to this variation. Our results show that the majority of HRFs were not reproducible between the arterial and portal venous phases before or after ComBat harmonization. We provide a guide for analyzing scans of different imaging phases.Handcrafted radiomic features (HRFs) are quantitative imaging features extracted from regions of interest on medical images which can be correlated with clinical outcomes and biologic characteristics. While HRFs have been used to train predictive and prognostic models, their reproducibility has been reported to be affected by variations in scan acquisition and reconstruction parameters, even within the same imaging vendor. In this work, we evaluated the reproducibility of HRFs across the arterial and portal venous phases of contrast-enhanced computed tomography images depicting hepatocellular carcinomas, as well as the potential of ComBat harmonization to correct for this difference. ComBat harmonization is a method based on Bayesian estimates that was developed for gene expression arrays, and has been investigated as a potential method for harmonizing HRFs. Our results show that the majority of HRFs are not reproducible between the arterial and portal venous imaging phases, yet a number of HRFs could be used interchangeably between those phases. Furthermore, ComBat harmonization increased the number of reproducible HRFs across both phases by 1%. Our results guide the pooling of arterial and venous phases from different patients in an effort to increase cohort size, as well as joint analysis of the phases.

Highlights

Recent decades have witnessed vast advances in computational power, artificial intelligence, and medical imaging techniques [1], which have provided a unique opportunity for transforming the abundant amounts of medical imaging into mineable quantitative data
Handcrafted radiomic features (HRFs) are divided into 11 feature families: Fractal (n = 3), Gray Level Co-occurence Matrix (GLCM; n = 26), Gray Level Distance Zone Matrix (GLDZM; n = 16), Gray Level Run Length
The application of ComBat harmonization to remove the batch effects attributed to In this study, we investigated the reproducibility of Hepatocellular carcinoma (HCC) computed tomography (CT)-based HRFs across the difference in time between contrast injection and scan acquisition resulted in a total of the arterial and portal venous imaging phases when all other scanning parameters were fixed, and whether ComBat harmonization improves the reproducibility of HRFs in such a scenario

Summary

Introduction

Recent decades have witnessed vast advances in computational power, artificial intelligence, and medical imaging techniques [1], which have provided a unique opportunity for transforming the abundant amounts of medical imaging into mineable quantitative data. This concept acquired much scientific attention recently, and a branch of medical imaging analysis—known as handcrafted radiomics—emerged as a result [2]. Handcrafted radiomic features (HRFs) are quantitative features extracted with high throughput from medical imaging, with its varying modalities. Since the introduction of the field, many studies have reported on the potential of radiomic signatures to predict clinical endpoints, the majority of which were performed on computed tomography (CT) [4,5,6,7], magnetic resonance (MR) [8,9,10], and positron emission tomography (PET) scans [11,12].

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