Radiomic Gradient in Peritumoural Tissue of Liver Metastases: A Biomarker for Clinical Practice? Analysing Density, Entropy, and Uniformity Variations with Distance from the Tumour.
Francesco FizElisa Maria RagainiSara SirchiaChiara MasalaSamuele ViganòMarco FranconeLara CavinatoEttore LanzaroneAngela AmmirabileLuca ViganòPublished in: Diagnostics (Basel, Switzerland) (2024)
The radiomic analysis of the tissue surrounding colorectal liver metastases (CRLM) enhances the prediction accuracy of pathology data and survival. We explored the variation of the textural features in the peritumoural tissue as the distance from CRLM increases. We considered patients with hypodense CRLMs >10 mm and high-quality computed tomography (CT). In the portal phase, we segmented (1) the tumour, (2) a series of concentric rims at a progressively increasing distance from CRLM (from one to ten millimetres), and (3) a cylinder of normal parenchyma (Liver-VOI). Sixty-three CRLMs in 51 patients were analysed. Median peritumoural HU values were similar to Liver-VOI, except for the first millimetre around the CRLM. Entropy progressively decreased (from 3.11 of CRLM to 2.54 of Liver-VOI), while uniformity increased (from 0.135 to 0.199, p < 0.001). At 10 mm from CRLM, entropy was similar to the Liver-VOI in 62% of cases and uniformity in 46%. In small CRLMs (≤30 mm) and responders to chemotherapy, normalisation of entropy and uniformity values occurred in a higher proportion of cases and at a shorter distance. The radiomic analysis of the parenchyma surrounding CRLMs unveiled a wide halo of progressively decreasing entropy and increasing uniformity despite a normal radiological aspect. Underlying pathology data should be investigated.
Keyphrases
- liver metastases
- computed tomography
- clinical practice
- end stage renal disease
- electronic health record
- newly diagnosed
- positron emission tomography
- dual energy
- ejection fraction
- big data
- prognostic factors
- image quality
- peritoneal dialysis
- machine learning
- magnetic resonance
- data analysis
- mass spectrometry
- high resolution
- patient reported outcomes
- artificial intelligence