Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the “Radiological” Tumour Microenvironment

Abstract

The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017–2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.

Radiomics of Contrast-Enhanced Computed Tomography: A Potential Biomarker for Pretreatment Prediction of the Response to Bacillus Calmette-Guerin Immunotherapy in Non-Muscle-Invasive Bladder Cancer

“…We used the image feature extraction software Python package (pyradiomics) to obtain 107 CT-based radiomic features, all of which were based on original images, including 14 shape features, 18 histogram features, and 68 texture features (Supplementary Table S1). All of these features have been previously reported (Aerts et al, 2014;Zhang et al, 2020;Fiz et al, 2021).…”

Prognostic value of texture analysis of the primary tumour in high‐risk neuroblastoma: An 18F‐DOPA PET study

“…The radiomics analysis was carried out in 3D mode on the 18 F‐DOPA PET images. No analyses of the low‐dose basal CT were attempted, as the lack of contrast might significantly reduce the information content of this method 30 and an excess of texture parameters could have increased the risk of false discoveries. The volumes of interest (VOI) for the texture analysis were painted slice‐by‐slice on the primary tumour manually; the open‐source LifeX software application (Inserm, University of Paris, France; https://www.lifexsoft.org) was used 31 .…”

Radiomics of Biliary Tumors: A Systematic Review of Current Evidence

“…The strength of radiomics relies on its capability to easily extract pixel and voxel patterns from standard imaging modalities, which may predict tumor biology and prognosis [17,51]. Several studies regarding radiomics in oncology have been published, but, unfortunately, the quality of evidence was very low in most cases [19,20,52].…”