Social Determinants of Health Framework to Identify and Reduce Barriers to Imaging in Marginalized Communities.

Social determinants of health (SDOH) are conditions influencing individuals' health based on their environment of birth, living, working, and aging. Addressing SDOH is crucial for promoting health equity and reducing health outcome disparities. For conditions such as stroke and cancer screening where imaging is central to diagnosis and management, access to high-quality medical imaging is necessary. This article applies a previously described structural framework characterizing the impact of SDOH on patients who require imaging for their clinical indications. SDOH factors can be broadly categorized into five sectors: economic stability, education access and quality, neighborhood and built environment, social and community context, and health care access and quality. As patients navigate the health care system, they experience barriers at each step, which are significantly influenced by SDOH factors. Marginalized communities are prone to disparities due to the inability to complete the required diagnostic or screening imaging work-up. This article highlights SDOH that disproportionately affect marginalized communities, using stroke and cancer as examples of disease processes where imaging is needed for care. Potential strategies to mitigate these disparities include dedicating resources for clinical care coordinators, transportation, language assistance, and financial hardship subsidies. Last, various national and international health initiatives are tackling SDOH and fostering health equity.

Understanding the role of radiologists in complex treatment decisions for patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver and represents a significant global health burden. Management of HCC can be challenging due to multiple factors, including variable expectations for treatment outcomes. Several treatment options are available, each with specific eligibility and ineligibility criteria, and are provided by a multidisciplinary team of specialists. Radiologists should be aware of the types of treatment options available, as well as the criteria guiding the development of individualized treatment plans. This awareness enables radiologists to contribute effectively to patient-centered multidisciplinary tumor boards for HCC and play a central role in reassessing care plans when the treatment response is deemed inadequate. This comprehensive review aims to equip radiologists with an overview of HCC staging systems, treatment options, and eligibility criteria. The review also discusses the significance of imaging in HCC diagnosis, treatment planning, and monitoring treatment response. Furthermore, we highlight the crucial branch points in the treatment decision-making process that depend on radiological interpretation.

Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation.

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.

Liver Imaging Reporting and Data System Comprehensive Guide: MR Imaging Edition.

The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the lexicon, technique, interpretation, reporting, and data collection of liver imaging. Developed specifically for assessment of liver observations in patients at risk for hepatocellular carcinoma (HCC), LI-RADS classifies hepatic observations on the basis of the probability of their being HCC, from LR-1 (definitely benign) to LR-5 (definitely HCC). This article discusses the technical requirements, major features, and ancillary features of and a systematic approach for using the LI-RADS diagnostic algorithm, with special emphasis on MR imaging.

CT imaging review of uncommon peritoneal-based neoplasms: beyond carcinomatosis.

Pathologic involvement of the peritoneum can result from a wide variety of conditions, including both neoplastic and non-neoplastic entities. Neoplastic involvement of the peritoneal ligaments, mesenteries, and spaces from malignant spread of epithelial cancers, termed peritoneal carcinomatosis, is frequently encountered at CT evaluation. However, a host of other more unusual benign and malignant neoplasms can manifest with peritoneal disease, including both primary and secondary peritoneal processes, many of which can closely mimic peritoneal carcinomatosis at CT. In this review, we discuss a wide array of unusual peritoneal-based neoplasms that can resemble the more common peritoneal carcinomatosis. Beyond reviewing the salient features for each of these entities, particular emphasis is placed on any specific clinical and CT imaging clues that may allow the interpreting radiologist to appropriately narrow the differential diagnosis and, in some cases, make an imaging-specific diagnosis.

The Lesser Sac and Foramen of Winslow: Anatomy, Embryology, and CT Appearance of Pathologic Processes.

OBJECTIVE. This article reviews the embryologic development, relevant anatomy, and imaging features, on CT, of pathologic processes involving the lesser sac and foramen of Winslow. CONCLUSION. The lesser peritoneal sac is an intricate anatomic region involved in many disease processes. It is a significant conduit for the spread of disease within the peritoneal cavity. The spectrum of pathologic processes pertaining to the lesser sac can be classified on the basis of the type of involvement, such as a fluid collection (e.g., transudate, exudate, bile, and blood), a mass (e.g., neoplastic or nonneoplastic conditions and lymphadenopathy), or an internal hernia into the lesser sac.

Non-neoplastic conditions mimicking peritoneal carcinomatosis at CT imaging.

The general appearance of peritoneal carcinomatosis at abdominal CT and other cross-sectional imaging modalities consists of varying amounts of peritoneal-based soft tissue implants (mass-forming or infiltrative), peritoneal fluid, and occasionally calcification. However, a wide variety of common and uncommon neoplastic and non-neoplastic conditions can closely mimic peritoneal carcinomatosis at imaging. Neoplastic mimics of peritoneal carcinomatosis include primary peritoneal and sub peritoneal tumors, as well as peritoneal lymphomatosis and sarcomatosis.Broad categories of non-neoplastic mimics of peritoneal carcinomatosis include tumor-like conditions, systemic processes, atypical infections, and fat-based conditions. For many entities, suggestive or specific patient information and/or CT imaging findings exist that may allow the radiologist to narrow the differential diagnosis. In this article, we review the salient clinical and cross-sectional imaging features of non-neoplastic mimics of peritoneal carcinomatosis and malignancy, with emphasis on the CT findings and the additional clues that may suggest the correct benign diagnosis.

Calcified Adrenal Lesions: Pattern Recognition Approach on Computed Tomography With Pathologic Correlation.

Incidental adrenal lesions are found in 2% to 10% of the population. The presence and pattern of calcifications, in conjunction with other clinical and imaging features, such as soft tissue attenuation, enhancement, and laterality, can aid in narrowing a differential diagnosis, thereby preventing unnecessary biopsies and avoiding delays in management. Calcified adrenal lesions can be categorized under the clinical and laboratory headings of normal adrenal function, hyperfunctioning adrenal tissue, and adrenal insufficiency. In this review, we provide an algorithmic approach to assessing calcified adrenal nodules with correlative radiologic findings.

Hepatic hemangioendothelioma: CT, MR, and FDG-PET-CT in 67 patients-a bi-institutional comprehensive cancer center review.

OBJECTIVE: To evaluate the imaging features of hepatic epithelioid hemangioendothelioma (HEH) on multiphasic CT, MR, and FDG-PET-CT. METHODS: Bi-institutional review identified 67 adults (mean age, 47 years; 23 M/44 F) with pathologically proven HEH and pretreatment multiphasic CT (n = 67) and/or MR (n = 30) and/or FDG-PET-CT (n = 13). RESULTS: HEHs were multifocal in 88% (59/67). Mean size of the dominant mass was 4.1 cm (range, 1.4-19 cm). The tumors were located in the peripheral, subcapsular regions of the liver in 96% (64/67). Capsular retraction was present in 81% (54/67 cases) and tumors were coalescent in 61% (41/67). HEH demonstrated peripheral ring enhancement on arterial phase imaging in 33% (21/64) and target appearance on the portal venous phase in 69% (46/67). Persistent peripheral enhancement on the delayed phase was seen in 49% (31/63). On MR, multilayered target appearance was seen on the T2-weighted sequences in 67% (20/30) and on the diffusion-weighted sequences in 61% (11/18). Target appearance on hepatobiliary phase of MRI was seen in 57% (4/7). On pre-therapy FDG-PET-CT, increased FDG uptake above the background liver parenchyma was seen in 62% (8/13). CONCLUSION: HEHs typically manifest as multifocal, coalescent hepatic nodules in peripheral subcapsular location, with associated capsular retraction. Peripheral arterial ring enhancement and target appearance on portal venous phase are commonly seen on CT. Similarly, multilayered target appearance correlating with its histopathological composition is typically seen on multiple sequences of MR including T2-weighted, diffusion-weighted, and dynamic contrast-enhanced multiphasic MR. KEY POINTS: • Hepatic epithelioid hemangioendotheliomas manifest on CT and MR as multifocal, coalescent hepatic nodules in peripheral subcapsular location, with associated capsular retraction. • Enhancement pattern on contrast-enhanced CT and MR can vary but peripheral ring enhancement on arterial phase and target appearance on portal venous phase are commonly seen. • Retrospective two-center study showed that cross-sectional imaging may help in the diagnosis.

Mimics, pitfalls, and misdiagnoses of adrenal masses on CT and MRI.

Due to the widespread use of imaging, incidental adrenal masses are commonly encountered. A number of pitfalls can result in misdiagnosis of these lesions, including inappropriate choice of imaging technique, presence of pseudolesions, and overlap of imaging features of different adrenal lesions. This article explores the potential pitfalls in imaging of the adrenal glands, on computed tomography and magnetic resonance imaging, that can lead to misinterpretation. Clues to correct diagnoses are provided to evade potential misinterpretation.

A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization.

PURPOSE: Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE. MATERIALS AND METHODS: Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP <14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input as well as the BCLC stage alone as a control. RESULTS: The model's response prediction accuracy rate was 74.2% (95% CI=64%-82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI= 52%-72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome. CONCLUSION: This preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding HCC patient selection for TACE.

User and system pitfalls in liver imaging with LI-RADS.

The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging, created specifically for patients at risk for hepatocellular carcinoma. Over the past years, LI-RADS has been progressively implemented into clinical practice, but pitfalls remain related to user error and inherent limitations of the system. User pitfalls include the inappropriate application of LI-RADS to a low-risk patient population, incorrect measurement techniques, inaccurate assumptions about LI-RADS requirements, and improper usage of LI-RADS terminology and categories. System pitfalls include areas of discordance with the Organ Procurement and Transplantation Network (OPTN) as well as pitfalls related to rare ancillary features. This article reviews common user pitfalls in applying LI-RADS v2018 and how to avoid preventable errors and also highlights deficiencies of the current version of LI-RADS and how it might be improved in the future. Level of Evidence:3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2019;50:1673-1686.

White paper of the Society of Abdominal Radiology hepatocellular carcinoma diagnosis disease-focused panel on LI-RADS v2018 for CT and MRI.

The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral "washout", and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers.