Liver cirrhosis and hepatocellular carcinoma (HCC) constitute one of the major causes of morbidity, mortality, and high health care costs worldwide. Multiple treatment options are available for HCC depending on the clinical status of the patient, size and location of the tumor, and available techniques and expertise. Locoregional treatment options are multiple. The most challenging part is how to assess the treatment response by different imaging modalities, but our scope will be assessing the response to locoregional therapy for HCC by MRI. This will be addressed by conventional MR methods using LI-RADS v2018 and by functional MR using diffusion-weighted imaging, perfusion, and highlighting the value of the novel intravoxel incoherent motion (IVIM).
Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver failure and portal hypertension. Early detection of liver fibrosis helps in improving its treatment and prevents its progression to cirrhosis. In this work, we present a novel noninvasive method to detect liver fibrosis from tagged MRI images using a machine learning‐based approach. Specifically, coronal and sagittal tagged MRI imaging are analyzed separately to capture cardiac‐induced deformation of the liver. The liver is manually delineated and a novel image feature, namely, the histogram of the peak strain (HPS) value, is computed from the segmented liver region and is used to classify the liver as being either normal or fibrotic. Classification is achieved using a support vector machine algorithm. The in vivo study included 15 healthy volunteers (10 males; age range 30–45 years) and 22 patients (15 males; age range 25–50 years) with liver fibrosis verified and graded by transient elastography, and 10 patients only had a liver biopsy and were diagnosed with a score of F3‐F4. The proposed method demonstrates the usefulness and efficiency of extracting the HPS features from the sagittal slices for patients with moderate fibrosis. Cross‐validation of the method showed an accuracy of 83.7% (specificity = 86.6%, sensitivity = 81.8%).
• F-FDG PET/CT is a valuable tool for the detection of HCC recurrence • F-FDG PET/CT should be incorporated during routine workup awaiting liver transplantation • Significant correlation was found between AFP level and SUVmax ratio • The best threshold for F-FDG PET positivity was>1.21 • The ideal cut-off value for AFP was >202.
Background
18F-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) has been widely used for diagnosis and staging of disease recurrence in breast cancer patients.
Purpose
To determine the validity of 18F-FDG PET/CT maximum standardized uptake value (SUVmax) of primary breast cancer (SUVmax-T) as a prognostic factor for locoregional axillary lymph node metastasis (ALNM).
Material and Methods
We prospectively studied 198 female patients with pathologically proven breast cancer. All patients were submitted to 18F-FDG PET/CT imaging for their initial tumor staging. The SUVmax-T on 18F-FDG PET/CT and ALNM were assessed. The patients were categorized into two groups; group A, with ALNM, and group B, without ALNM, according to the presence of locoregional ALNM on visual analysis of 18F-FDG PET/CT. The correlations and differences between two groups were statistically evaluated. The optimal cut-off value of SUVmax-T was determined.
Results
The mean tumor size in group A was significantly greater than that in group B (P = 0.0416). The mean SUVmax-T value in group A was significantly higher than that in group B (P = 0.0037). Tumor stage in group A was higher than that in group B (P = 0.0045). The correlation between tumor size and SUVmax-T value was statistically significant in group A (r = 0.6768, P = 0.0111) and in group B (r = 0.7221, P = 0.0280). The optimal cut-off of SUVmax-T for detecting ALNM was >4.2. As regards molecular subtypes, the mean SUVmax-T values of HER2-positive and triple-negative subtypes in group A were significantly higher than that in group B (P = 0.0012 and <0.001, respectively).
Conclusion
Breast cancer patients with higher primary tumor 18F-FDG uptake are at greater risk of concurrent ALNM.
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