Many psychology researchers have shown that humans do not process probabilistic information in a manner consistent with Bayes' theory [9, 10, 16, 24, 23, 27]. Robinson and Hastie [24, 23] showed that humans made non-compensatory probability updates, produced super-additive distributions, and resuscitated zero probability possibilities. While most researchers have classified these behaviors as nonnormative, we found that the Dempster-Shafer theory could model each of these behaviors in a normative and theoretically sound fashion. While not claiming that the theory modeb human processes, we claim that the similarities should aid user acceptance of Dempster-Shafer based decision systems.

Over the past 16 years, several scientific organizations have proposed systems that incorporate imaging for surveillance, diagnosis, staging, treatment, and monitoring of treatment response of hepatocellular carcinoma (HCC). These systems are needed to standardize the acquisition, interpretation, and reporting of liver imaging examinations; help differentiate benign from malignant observations; improve consistency between radiologists; and provide guidance for management of HCC. This review article discusses the historical evolution of HCC imaging systems. We indicate the features differentiating these systems, including target population, screening and surveillance algorithm, diagnostic imaging modalities, diagnostic scope, expertise and technical requirements, terminology, major and ancillary imaging features, staging and transplant eligibility, and assessment of treatment response. We highlight the potential benefits of unifying the systems, which we anticipate will enable sharing, pooling, and meta-analysis of data; facilitate multi-center trials; and accelerate dissemination of knowledge.

To improve standardization and consensus regarding performance, interpreting, and reporting computed tomography (CT) and magnetic resonance imaging (MRI) examinations of the liver in patients at risk for hepatocellular carcinoma (HCC), LI-RADS (Liver Imaging Reporting and Data System) was launched in March 2011 and adopted by many clinical practices throughout the world. LI-RADS categorizes nodules recognized at CT or MRI, in patients at high risk of HCC, as definitively benign, probably benign, intermediate probability of being HCC, probably HCC, and definitively HCC (corresponding to LI-RADS categories 1-5). The LI-RADS Management Working Group, consisting of internationally recognized medical and surgical experts on HCC management, as well as radiologists involved in the development of LI-RADS, was convened to evaluate management implications related to radiological categorization of the estimated probability that a lesion will be ultimately diagnosed as HCC. In this commentary, we briefly review LI-RADS and the initial consensus of the LI-RADS Management Working Group reached during its deliberations in 2013. We then focus on initial discordance of LI-RADS with American Association for the Study of Liver Diseases and Organ Procurement Transplant Network guidelines, the basis for these differences, and how they are being addressed going forward to optimize reporting of CT and MRI findings in patients at risk for HCC and to increase consensus throughout the international community of physicians involved in the diagnosis and treatment of HCC.

A radiologist issuing a LI-RADS category is, implicitly or explicitly, a member of a multidisciplinary team. If the definite diagnosis of a benign or malignant entity is not possible, categorizing the uncertainty as LR-2, -3, -4, or -M has important management implications. In this article, we discuss the range of options for management or further diagnostic testing and how a LR category may affect the choice between them. We then review recent published data regarding eventual diagnoses following assignment of a LR category.

H-ion (∼45 keV to ∼600 keV), He-ion (∼65 keV to ∼520 keV), and O-ion (∼140 keV to ∼1130 keV) integral flux measurements, from the Radiation Belt Storm Probe Ion Composition Experiment (RBSPICE) instrument aboard the Van Allan Probes spacecraft B, are reported. These abundance data form a cohesive picture of ring current ions during the first 9 months of measurements. Furthermore, the data presented herein are used to show injection characteristics via the He-ion/H-ion abundance ratio and the O-ion/H-ion abundance ratio. Of unique interest to ring current dynamics are the spatial-temporal decay characteristics of the two injected populations. We observe that He-ions decay more quickly at lower L shells, on the order of ∼0.8 day at L shells of 3-4, and decay more slowly with higher L shell, on the order of ∼1.7 days at L shells of 5-6. Conversely, O-ions decay very rapidly (∼1.5 h) across all L shells. The He-ion decay time are consistent with previously measured and calculated lifetimes associated with charge exchange. The O-ion decay time is much faster than predicted and is attributed to the inclusion of higher-energy (> 500 keV) O-ions in our decay rate estimation. We note that these measurements demonstrate a compelling need for calculation of high-energy O-ion loss rates, which have not been adequately studied in the literature to date.

Key points

We report initial observations of ring current ionsWe show that He-ion decay rates are consistent with theoryWe show that O-ions with energies greater than 500 keV decay very rapidly.

Objective

The purpose of this article is to review the imaging features and Liver Imaging Reporting and Data System (LI-RADS) categorization of benign and likely benign entities, including typical cirrhotic nodules, distinctive nodular observations, and benign entities that may simulate hepatocellular carcinoma.

Conclusion

LI-RADS is a system of standardized criteria for interpreting liver CT and MR images of patients at risk of hepatocellular carcinoma. Most of the observations in these patients are not malignant. With the development of fibrosis and cirrhosis, these benign entities may take on an altered appearance.

Since its initial release in 2011, the Liver Imaging Reporting and Data System (LI-RADS) has evolved and expanded in scope. It started as a single algorithm for hepatocellular carcinoma (HCC) diagnosis with CT or MRI with extracellular contrast agents and has grown into a multialgorithm network covering all major liver imaging modalities and contexts of use. Furthermore, it has developed its own lexicon, report templates, and supplementary materials. This article highlights the major achievements of LI-RADS in the past 11 years, including adoption in clinical care and research across the globe, and complete unification of HCC diagnostic systems in the United States. Additionally, the authors discuss current gaps in knowledge, which include challenges in surveillance, diagnostic population definition, perceived complexity, limited sensitivity of LR-5 (definite HCC) category, management implications of indeterminate observations, challenges in reporting, and treatment response assessment following radiation-based therapies and systemic treatments. Finally, the authors discuss future directions, which will focus on mitigating the current challenges and incorporating advanced technologies. Tha authors envision that LI-RADS will ultimately transform into a probability-based system for diagnosis and prognostication of liver cancers that will integrate patient characteristics and quantitative imaging features, while accounting for imaging modality and contrast agent.

The Liver Imaging Reporting And Data System (LI-RADS) was created with the support of the American College of Radiology (ACR) to standardize the acquisition, interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). A comprehensive and rigorous system developed by radiologists, hepatologists, pathologists, and surgeons, LI-RADS addresses a wide range of imaging contexts. Currently, 4 algorithms are available publicly on the ACR website: ultrasound for HCC surveillance, computed tomography and magnetic resonance imaging for HCC diagnosis and tumor staging, contrast-enhanced ultrasound for HCC diagnosis, and computed tomography/magnetic resonance imaging for treatment response assessment. Each algorithm is supported by a decision tree, categorization table, lexicon, atlas, technical requirements, and reporting and management guidance. Category codes reflecting the relative probability of HCC and malignancy are assigned to imaging-detected liver observations, with emerging evidence suggesting that LI-RADS accurately stratifies HCC and malignancy probabilities. LI-RADS is an evolving system and has been updated and refined iteratively since 2011 based on scientific evidence, expert opinion, and user feedback, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Concurrent with its most recent update, LI-RADS was integrated into the American Association for the Study of Liver Diseases HCC guidance released in 2018. We anticipate continued refinement of LI-RADS and progressive adoption by radiologists worldwide, with the eventual goal of culminating in a single unified system for international use.

The Liver Imaging Reporting and Data System (LI-RADS) is a reporting system created for the standardized interpretation of liver imaging findings in patients who are at risk for hepatocellular carcinoma (HCC). This system was developed with the cooperative and ongoing efforts of an American College of Radiology-supported committee of diagnostic radiologists with expertise in liver imaging and valuable input from hepatobiliary surgeons, hepatologists, hepatopathologists, and interventional radiologists. In this article, the 2017 version of LI-RADS for computed tomography and magnetic resonance imaging is reviewed. Specific topics include the appropriate population for application of LI-RADS; technical recommendations for image optimization, including definitions of dynamic enhancement phases; diagnostic and treatment response categories; definitions of major and ancillary imaging features; criteria for distinguishing definite HCC from a malignancy that might be non-HCC; management options following LI-RADS categorization; and reporting. ^©RSNA, 2017.