Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, usually occurring on a background of liver cirrhosis. HCC is a highly vascular tumor in which angiogenesis plays a major role in tumor growth and spread. Tumor-induced angiogenesis is usually related to a complex interplay between multiple factors and pathways, with vascular endothelial growth factor being a major player in angiogenesis. In the past decade, understanding of tumor-induced angiogenesis has led to the emergence of novel anti-angiogenic therapies, which act by reducing neo-angiogenesis, and improving patient survival. Currently, Sorafenib and Lenvatinib are being used as the first-line treatment for advanced unresectable HCC. However, a disadvantage of these agents is the presence of numerous side effects. A major challenge in the management of HCC patients being treated with anti-angiogenic therapy is effective monitoring of treatment response, which decides whether to continue treatment or to seek second-line treatment. Several criteria can be used to assess response to treatment, such as quantitative perfusion on cross-sectional imaging and novel/emerging MRI techniques, including a host of known and emerging biomarkers and radiogenomics. This review addresses the pathophysiology of angiogenesis in HCC, accurate imaging assessment of angiogenesis, monitoring effects of anti-angiogenic therapy to guide future treatment and assessing prognosis.

Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology. Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting. In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.

Hepatocellular carcinoma (HCC) is the most common primary liver neoplasm, and its incidence has doubled over the past two decades owing to increasing risk factors. Despite surveillance, most HCC cases are diagnosed at advanced stages and can only be treated using transarterial chemo-embolization (TACE) or systemic therapy. TACE failure may occur with incidence reaching up to 60% of cases, leaving patients with a financial and emotional burden. Radiomics has emerged as a new tool capable of predicting tumor response to TACE from pre-procedural computed tomography (CT) studies. This data report defines the HCC-TACE data collection of confirmed HCC patients who underwent TACE and have pre- and post-procedure CT imaging studies and available treatment outcomes (time-to-progression and overall survival). Clinically curated segmentation of pre-procedural CT studies was done for the purpose of algorithm training for prediction and automatic liver tumor segmentation.

The Peutz-Jeghers Syndrome (PJS) is an autosomal dominant neoplastic syndrome defined by hamartomatous polyps through the gastrointestinal tract, development of characteristic mucocutaneous pigmentations, and an elevated lifetime cancer risk. The majority of cases are due to a mutation in the STK11 gene located at 19p13.3. The estimated incidence of PJS ranges from 1:50,000 to 1:200,000. PJS carries an elevated risk of malignancies including gastrointestinal, breast, lung, and genitourinary (GU) neoplasms. Patients with PJS are at a 15- to 18-fold increased malignancy risk relative to the general population. Radiologists have an integral role in the diagnosis of these patients. Various imaging modalities are used to screen for malignancies and complications associated with PJS. Awareness of various PJS imaging patterns, associated malignancies, and their complications is crucial for accurate imaging interpretation and patient management. In this manuscript, we provide a comprehensive overview of PJS, associated malignancies, and surveillance protocols.