Imaging in Hepatocellular Carcinoma: What's New?

Hepatocellular carcinoma (HCC) is the most common liver cancer and is one of the uppermost 2 causes of cancer death. About 70%-90% of HCCs develop within a cirrhotic liver. According to the most recent guidelines, the imaging characteristics of HCC on contrast-enhanced Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) are generally satisfactory to make a diagnosis. Recently, new advanced techniques such as contrast-enhanced ultrasound, CT perfusion, Dynamic Contrast-enhanced MRI, diffusion weighted imaging and radiomics have increased the diagnostic accuracy and characterization of HCC. This review illustrates the state of the art and recent advances in non-invasive imaging evaluation of HCC.

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade.

PURPOSE: The aim of this single-center retrospective study is to assess whether contrast-enhanced computed tomography (CECT) radiomics analysis is predictive of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) grade based on the 2019 World Health Organization (WHO) classification and to establish a tumor grade (G) prediction model. MATERIAL AND METHODS: Preoperative CECT images of 78 patients with GEP-NENs were retrospectively reviewed and divided in two groups (G1-G2 in class 0, G3-NEC in class 1). A total of 107 radiomics features were extracted from each neoplasm ROI in CT arterial and venous phases acquisitions with 3DSlicer. Mann-Whitney test and LASSO regression method were performed in R for feature selection and feature reduction, in order to build the radiomic-based predictive model. The model was developed for a training cohort (75% of the total) and validated on the independent validation cohort (25%). ROC curves and AUC values were generated on training and validation cohorts. RESULTS: 40 and 24 features, for arterial phase and venous phase, respectively, were found to be significant in class distinction. From the LASSO regression 3 and 2 features, for arterial phase and venous phase, respectively, were identified as suitable for groups classification and used to build the tumor grade radiomic-based prediction model. The prediction of the arterial model resulted in AUC values of 0.84 (95% CI 0.72-0.97) and 0.82 (95% CI 0.62-1) for the training cohort and validation cohort, respectively, while the prediction of the venous model yielded AUC values of 0.7877 (95% CI 0.6416-0.9338) and 0.6813 (95% CI 0.3933-0.9693) for the training cohort and validation cohort, respectively. CONCLUSIONS: CT-radiomics analysis may aid in differentiating the histological grade for GEP-NENs.

Imaging of Pancreatic Neuroendocrine Neoplasms.

Pancreatic neuroendocrine neoplasms (panNENs) represent the second most common pancreatic tumors. They are a heterogeneous group of neoplasms with varying clinical expression and biological behavior, from indolent to aggressive ones. PanNENs can be functioning or non-functioning in accordance with their ability or not to produce metabolically active hormones. They are histopathologically classified according to the 2017 World Health Organization (WHO) classification system. Although the final diagnosis of neuroendocrine tumor relies on histologic examination of biopsy or surgical specimens, both morphologic and functional imaging are crucial for patient care. Morphologic imaging with ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) is used for initial evaluation and staging of disease, as well as surveillance and therapy monitoring. Functional imaging techniques with somatostatin receptor scintigraphy (SRS) and positron emission tomography (PET) are used for functional and metabolic assessment that is helpful for therapy management and post-therapeutic re-staging. This article reviews the morphological and functional imaging modalities now available and the imaging features of panNENs. Finally, future imaging challenges, such as radiomics analysis, are illustrated.

Gastrointestinal neuroendocrine neoplasms (GI-NENs): hot topics in morphological, functional, and prognostic imaging.

Neuroendocrine neoplasms (NENs) are heterogeneous tumours with a common phenotype descended from the diffuse endocrine system. NENs are found nearly anywhere in the body but the most frequent location is the gastrointestinal tract. Gastrointestinal neuroendocrine neoplasms (GI-NENs) are rather uncommon, representing around 2% of all gastrointestinal tumours and 20-30% of all primary neoplasms of the small bowel. GI-NENs have various clinical manifestations due to the different substances they can produce; some of these tumours appear to be associated with familial syndromes, such as multiple endocrine neoplasm and neurofibromatosis type 1. The current WHO classification (2019) divides NENs into three major categories: well-differentiated NENs, poorly differentiated NENs, and mixed neuroendocrine-non-neuroendocrine neoplasms. The diagnosis, localization, and staging of GI-NENs include morphology and functional imaging, above all contrast-enhanced computed tomography (CECT), and in the field of nuclear medicine imaging, a key role is played by 68Ga-labelled-somatostatin analogues (68Ga-DOTA-peptides) positron emission tomography/computed tomography (PET/TC). In this review of recent literature, we described the objectives of morphological/functional imaging and potential future possibilities of prognostic imaging in the assessment of GI-NENs.

Focal Liver Lesions Classification and Characterization: What Value Do DWI and ADC Have?

OBJECTIVE: The aim of this work was to analyze the value of diffusion-weighted imaging (DWI) in the classification/characterization of focal liver lesions (FLLs). METHODS: Retrospective study, approved by ethical board, of 100 proven FLLs (20 hemangiomas, 20 focal nodular hyperplasia, 20 dysplastic nodules, 20 hepatocellular carcinomas, and 20 metastases) was performed by 1.5-T MR. For each lesion, 2 readers, blinded of medical history, have evaluated 6 sets of images: set A (T1/T2-weighted images), set B (set A + DWI), set C (set B + apparent diffusion coefficient [ADC] map), set D (set A + dynamic and hepatobiliary phases), set E (set D + DWI), set F (set E + ADC map). RESULTS: In unenhanced images, the evaluation of the ADC improves the accuracy in classification/characterization (+9%/14%, respectively), whereas in enhanced images the accuracy was increased by DWI (+7%/12%, respectively) and ADC (+13%/19%, respectively). Diffusion-weighted imaging does not improve classification/characterization of hemangiomas, may be useful in focal nodular hyperplasia/dysplastic nodules vs metastases/hepatocellular carcinoma differentiation, and increases the classification/characterization of metastases in both unenhanced and enhanced images. CONCLUSIONS: Diffusion-weighted imaging may improve classification/characterization of FLLs at unenhanced/enhanced examinations.