Elizabeth M. Hecht

Screening for pancreatic cancer with imaging techniques in high-risk individuals—including patients with a genetic predisposition, a family history, or precursor lesions—is critical for improving early detection and survival outcomes, and multimodal strategies that integrate AI-enhanced radiomics with liquid biopsy hold promise for developing efficient and effective screening programs tailored to high-risk populations.

Purpose The Liver Imaging Reporting and Data System (LI-RADS, LR) provides a framework for diagnosing hepatocellular carcinoma (HCC, LR-5). However, not all HCCs meet LR-5 criteria and are instead categorized as LR-M, probably or definitely malignant but not specific for HCC, necessitating biopsy for diagnosis. The purpose is to identify factors associated with HCC in LR-M observations. Methods This is an IRB-approved, retrospective analysis of participants from 8 institutions that had a LR-M observation on CT or MRI with corresponding histopathologic diagnosis. Demographics and biochemical data were examined. Central review using the LI-RADS v2018 algorithm was performed. Kappa statistics defined inter-reader agreement. Random forest and logistic regression analyses generated a model for HCC diagnosis. Results 162 participants with 162 LR-M observations were included. 46% of observations (74/162) were HCC and 37% were cholangiocarcinoma (60/162). Two of 34 imaging features– observation size and intra-lesion iron– showed moderate to strong inter-reader agreement (Kappa ≥ 0.60) while the remainder showed weak or no agreement (Kappa < 0.60). Random forest analysis showed biochemical features to be more predictive of HCC than imaging features. Logistic regression analysis of a model based on INR and AFP provided a 72% sensitivity and 61% specificity for HCC by Youden’s index and a 90% specificity threshold yielded 38% sensitivity, 75% positive predictive value, and 66% negative predictive value. Conclusions Our results show INR and AFP are associated with HCC in LR-M observations. A high-specificity threshold may assist in the non-invasive diagnosis of HCC in the appropriate setting. Summary statement In certain at-risk patients with a LR-M observation on diagnostic imaging, serum AFP and INR maybe useful tools for the non-invasive diagnosis of HCC.

Given the crucial role of imaging in HCC diagnosis, LI-RADS CT/MRI was developed to standardize the imaging interpretation and reporting of HCC in patients at risk for HCC and categorize hepatic observations on an ordinal scale according to the likelihood of HCC. LI-RADS has since been expanded to include 5 algorithms: LI-RADS US Surveillance, contrast-enhanced US (CEUS) LI-RADS, LI-RADS CT/MRI, and LI-RADS Treatment Response Assessment. LI-RADS has been adopted broadly in North America, however with less ubiquitous adoption outside of North America. Further elucidation of the perceived strengths and weakness of the LI-RADS algorithm, as it pertains to various geographic regions, will continue to inform a future system that may be more readily adopted globally. Therefore, the aim of this article is to summarize HCC risk factors and imaging guidelines in select geographically disparate regions, and to solicit feedback from liver imaging experts on the limitations and barriers to adoption of LI-RADS algorithms in their patient populations. Graphical Abstract

In 2021, the Human Rights Council declared that having a clean, healthy, and sustainable environment is a human right. According to the WHO, 24% of deaths are attributable to environmental health risks and are largely preventable. Current predictions show that rising emissions will be linked to an enormous healthcare burden, especially for high-risk populations and historically disadvantaged communities. The US healthcare industry accounts for nearly 18% of its GDP and is a major consumer of resources. The largest healthcare-related source of greenhouse gas emissions is from the supply chain, including pharmaceuticals, other chemicals, food, and the transportation required to mobilize them accounting for 80% of emissions, with only 20% of emissions from purchased energy and the facilities directly. As a field, radiology has historically monitored its impact in terms of radiation exposure and thermal effects but has not focused on other pollutants, greenhouse gas emissions, or waste. Although tackling large issues such as climate change and pollution seems daunting, we can start by raising awareness through education, investigation, and advocacy. In this review, we discuss a systems-based approach to addressing climate change from the federal to the local level focusing on the potential role of the radiologist. Export

Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at this st

Pancreatic ductal adenocarcinoma (PDA) is one of the most aggressive cancers. It has a poor 5-year survival rate of 12%, partly because most cases are diagnosed at advanced stages, precluding curative surgical resection. Early-stage PDA has significantly better prognoses due to increased potential for curative interventions, making early detection of PDA critically important to improved patient outcomes. We examine current and evolving early detection concepts, screening strategies, diagnostic yields among high-risk individuals, controversies, and limitations of standard-of-care imaging.

Background: Pancreatic cystic lesions (PCLs) are frequent on MRI and are thought to be associated with pancreatic adenocarcinoma (PDAC) necessitating long‐term surveillance based on older studies suffering from selection bias.Purpose: To establish the percentage of patients with PCLs on MRI with a present or future PDAC.Study Type: Systematic review, meta‐analysis.Population: Adults with PCLs on MRI and a present or future diagnosis of PDAC were eligible. MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Scopus were searched to April 2022 (PROSPERO:CRD42022320502). Studies limited to PCLs not requiring surveillance, <100 patients, or those with a history/genetic risk of PDAC were excluded.Field Strength/Sequence: ≥1.5 T with ≥1 T2‐weighted sequence.Assessment: Two investigators extracted data, with discrepancies resolved by a third. QUADAS‐2 assessed bias. PDAC was diagnosed using a composite reference standard.Statistical Tests: A meta‐analysis of proportions was performed at the patient‐level with 95% confidence intervals (95% CI).Results: Eight studies with 1289 patients contributed to the percentage of patients with a present diagnosis of PDAC, and 10 studies with 3422 patients to the percentage with a future diagnosis. Of patients with PCLs on MRI, 14.8% (95% CI 2.4–34.9) had a PDAC at initial MRI, which decreased to 6.0% (2.2–11.3) for studies at low risk of bias. For patients without PDAC on initial MRI, 2.0% (1.1–3.2) developed PDAC during surveillance, similar for low risk of bias studies at 1.9% (0.7–3.6), with no clear trend of increased PDAC for longer surveillance durations. For patients without worrisome features or high‐risk stigmata, 0.9% (0.1–2.2) developed PDAC during surveillance. Of 10, eight studies had a median surveillance ≥3 years (range 3–157 months). Sources of bias included retrospectively limiting PCLs to those with histopathology and inconsistent surveillance protocols.Data Conclusion: A low percentage of patients with PCLs on MRI develop PDAC while on surveillance. The first MRI revealing a PCL should be scrutinized for PDAC.Level of Evidence: 3Technical Efficacy: Stage 2