Dynamic Contrast Enhanced-MRI and Apparent Diffusion Coefficient Quantitation for Differentiate Hepatocellular Carcinoma from Hepatocellular Adenoma.

BACKGROUND: Due to their overlapping radiological characteristics, hepatic lesions, such as hepatocellular carcinoma (HCC) and hepatocellular adenoma (HCA), present a substantial diagnostic challenge. Accurate differentiation between HCC and HCA is essential for the best clinical treatment and therapeutic decision-making. This study aims to assess the potential role of DCE-MRI and Apparent Diffusion Coefficient (ADC) quantitation in the diagnosis of hepatocellular carcinoma (HCC) from hepatocellular adenoma (HCA). METHODS: 103 patients (56 HCC, 47 HCA) with histopathologically proven hepatocellular lesions were the subjects of a cross-sectional investigation. A standardized imaging technique was used for DCE-MRI on all patients. Diffusion-weighted imaging (DWI) provided the ADC values. The diagnostic efficacy of DCE-MRI and ADC in differentiation was evaluated using statistical analyses, such as t-tests and receiver operating characteristic (ROC) curve analysis. SPSS VER 16 was used for the analysis of the collected data. RESULTS: A total of 103 patients (female: male= 52:51, 57.14±3.09 years) were included in the study. The study revealed significant differences in DCE-MRI parameters and ADC values between HCC and HCA lesions. ADC value was significantly lower in HCC than in HCA (p < 0.001). The area under the curve (AUC) was 0.78 (95% CI: 0.69-0.87) for ADC, 0.84 (95% CI: 0.76-0.91) for Ktrans, and 0.72 (95% CI: 0.62-0.82) for Ve. Sensitivity and specificity for ADC were 76.59% and 71.42%, respectively. Also, PPV and NPV of ADC were 69.23% and 78.43%, respectively. Sensitivity and specificity for Ktrans were 82.14% and 76.59%, respectively. Also, PPV and NPV of Ktrans were 80.7% and 78.26%, respectively. CONCLUSION: In conclusion, DCE-MRI-derived parameters, along with ADC values, exhibit promise as non-invasive tools for differentiating HCC from HCA.

Multi-parametric assessment of cardiac magnetic resonance images to distinguish myocardial infarctions: A tensor-based radiomics feature.

AIM: This study assessed the myocardial infarction (MI) using a novel fusion approach (multi-flavored or tensor-based) of multi-parametric cardiac magnetic resonance imaging (CMRI) at four sequences; T1-weighted (T1W) in the axial plane, sense-balanced turbo field echo (sBTFE) in the axial plane, late gadolinium enhancement of heart short axis (LGE-SA) in the sagittal plane, and four-chamber views of LGE (LGE-4CH) in the axial plane. METHODS: After considering the inclusion and exclusion criteria, 115 patients (83 with MI diagnosis and 32 as healthy control patients), were included in the present study. Radiomic features were extracted from the whole left ventricular myocardium (LVM). Feature selection methods were Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-Square (Chi2), Analysis of Variance (Anova), Recursive Feature Elimination (RFE), and SelectPersentile. The classification methods were Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). Different metrics, including receiver operating characteristic curve (AUC), accuracy, F1- score, precision, sensitivity, and specificity were calculated for radiomic features extracted from CMR images using stratified five-fold cross-validation. RESULTS: For the MI detection, Lasso (as the feature selection) and RF/LR (as the classifiers) in sBTFE sequences had the best performance (AUC: 0.97). All features and classifiers of T1 + sBTFE sequences with the weighted method (as the fused image), had a good performance (AUC: 0.97). In addition, the results of the evaluated metrics, especially mean AUC and accuracy for all models, determined that the T1 + sBTFE-weighted fused method had strong predictive performance (AUC: 0.93±0.05; accuracy: 0.93±0.04), followed by T1 + sBTFE-PCA fused method (AUC: 0.85±0.06; accuracy: 0.84±0.06). CONCLUSION: Our selected CMRI sequences demonstrated that radiomics analysis enables to detection of MI accurately. Among the investigated sequences, the T1 + sBTFE-weighted fused method with the highest AUC and accuracy values was chosen as the best technique for MI detection.