Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer related death worldwide. Radiology has traditionally played a central role in HCC management, ranging from screening of high-risk patients to non-invasive diagnosis, as well as the evaluation of treatment response and post-treatment follow-up. From liver ultrasonography with or without contrast to dynamic multiple phased CT and dynamic MRI with diffusion protocols, great progress has been achieved in the last decade. Throughout the last few years, pathological, biological, genetic, and immune-chemical analyses have revealed several tumoral subtypes with diverse biological behavior, highlighting the need for the re-evaluation of established radiological methods. Considering these changes, novel methods that provide functional and quantitative parameters in addition to morphological information are increasingly incorporated into modern diagnostic protocols for HCC. In this way, differential diagnosis became even more challenging throughout the last few years. Use of liver specific contrast agents, as well as CT/MRI perfusion techniques, seem to not only allow earlier detection and more accurate characterization of HCC lesions, but also make it possible to predict response to treatment and survival. Nevertheless, several limitations and technical considerations still exist. This review will describe and discuss all these imaging modalities and their advances in the imaging of HCC lesions in cirrhotic and non-cirrhotic livers. Sensitivity and specificity rates, method limitations, and technical considerations will be discussed.
Background Computed tomography liver perfusion (CTLP) has been improved in recent years, offering a variety of perfusion-parametric maps. A map that better discriminates hepatocellular carcinoma (HCC) is still to be found. Purpose To compare different CTLP maps, regarding their ability to differentiate cirrhotic or non-cirrhotic parenchyma from malignant HCC. Material and Methods Twenty-six patients (11 cirrhotic) with 50 diagnosed HCC lesions, underwent CTLP on a 128-row dual-energy CT system. Nine different maps were generated. Regions of interest (ROIs) were positioned on non-tumorous parenchyma and on HCCs found on previous magnetic resonance imaging. Perfusion parameters for non-cirrhotic and cirrhotic livers were compared. Receiver operating characteristic (ROC) analysis was employed to evaluate each map's ability to discriminate HCCs from non-tumorous livers. Comparison of ROC curves was performed to evaluate statistical significance of differences in the discriminating efficiency of derived perfusion maps. Results Perfusion parameters for non-tumorous liver and HCCs recorded in cirrhotic patients did not significantly differ from corresponding values recorded in non-cirrhotic patients ( P > 0.05). The highest power for HCC discrimination was found for the maximum-slope-of-increase (MSI) parametric map, with estimated the area under ROC curve of 0.997. An MSI cut-off criterion of 2.2 HU/s was found to provide 96% sensitivity and 100% specificity. Time to peak, blood flow, and transit time to peak were also found to have high discriminating power. Conclusion Among available CTLP-derived perfusion parameters, MSI was found to provide the highest diagnostic accuracy in discriminating HCCs from non-tumorous parenchyma. Perfusion parameters for non-tumorous livers and HCCs were not found to significantly differ between cirrhotic and non-cirrhotic patients.
Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.
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