Magnetic resonance imaging of pediatric liver tumors

Guide to use of ferumoxytol for hepatic vascular assessment as part of dual contrast MRI

Magnetic resonance imaging (MRI) assessment of hepatic vasculature can be challenging in the setting of liver disease and liver lesions. The widely used hepatobiliary contrast agent gadoxetate is an extracellular contrast agent that provides excellent soft tissue characterization but has limitations as a vascular contrast agent. Ferumoxytol is an iron oxide nanoparticle with superparamagnetic properties that can be used as blood pool contrast agent to provide dedicated vascular assessment. We provide a detailed protocol for evaluation of pediatric liver vasculature using ferumoxytol, after imaging of the parenchyma with gadoxetate. We provide multiple examples and discuss practical considerations when incorporating ferumoxytol into practice.

Deep Learning for Medical Image-Based Cancer Diagnosis

(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

Advances in the treatment of pediatric solid tumors: A 50-year perspective

In the United States, more than 10 000 cancers occur annually in children aged 0-14 years, and more than 5000 in adolescents aged 15-19. In the last 50 years, significant advances have been made in imaging, molecular pathology, stage and risk assessment, surgical approach, multidisciplinary treatment, and survival for pediatric solid tumors (particularly neuroblastoma, Wilms tumor, rhabdomyosarcoma, and hepatoblastoma). Moreover, the molecular driver for fibrolamellar hepatocellular carcinoma, which occurs in adolescence and young adulthood, has been identified.

Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine

Simple Summary

Magnetic Resonance Imaging (MRI) is a consolidated imaging tool for the multiparametric assessment of tissues in various pathologies from degenerative and inflammatory diseases to cancer. In recent years, the continuous technological evolution of the equipment has led to the development of sequences that provide not only anatomical but also functional and metabolic information. In addition, there is a growing and emerging field of research in clinical applications using MRI to exploit the diagnostic and therapeutic capabilities of nanocompounds. This review illustrates the application of the most advanced magnetic resonance techniques in the field of nanomedicine.

Abstract

In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment in preclinical and clinical settings. This paper reviews the state of the art of the application of magnetic resonance imaging in the field of nanomedicine, as well as the use of nanoparticles as diagnostic and therapeutic tools, especially in cancer, including the characteristics that hinder the use of nanoparticles in clinical practice.

A case report of fibrolamellar hepatocellular carcinoma, with particular reference to preoperative diagnosis, value of molecular genetic diagnosis, and cell origin

Background

Fibrolamellar hepatocellular carcinoma (FL-HCC) is a liver tumor that occurs almost exclusively in young adults without underlying liver disease. In spite of its distinct clinical characteristics and specific imaging findings, preoperative diagnosis is often difficult due to the extremely low incidence of the tumor. Although FL-HCC shows particular morphological features on H&E-stained tissue sections, differential diagnosis from ordinary HCC, especially the scirrhous variant of HCC, and intrahepatic cholangiocarcinoma needs additional immunohistochemical (IHC) analyses and/or molecular genetic testing.

Case presentation

A 21-year-old male patient was referred to our hospital for further evaluation of a large liver mass. Abdominal ultrasound examination, contrast-enhanced computed tomography, and magnetic resonance imaging revealed a well-defined hypervascular lobulated liver mass, 11 × 11 cm in diameter, with a central scar and calcification, in segments 5/8. Under the diagnosis of FL-HCC, we carried out extended anterior sectorectomy, including a part of segment 4. On microscopic examination, the tumor was composed of proliferating polygonal cells with abundant eosinophilic granular cytoplasm containing nuclei with vesicular chromatin and enlarged nucleoli, in an abundant stroma. Collagen fibers arranged in a parallel lamellar pattern were seen in the tumor stroma. These findings, together with the results of subsequent IHC analyses using HAS, CK7, and CD 67, we made the diagnosis of FL-HCC, which was further confirmed by detection of the DNAJB1-PRKACA fusion gene in the tumor cells by RT-PCR.

Conclusion

FL-HCC shows distinct imaging appearances. Although it also has characteristic morphological features, combined use of IHC and/or molecular genetic studies are necessary for the final diagnosis.